Visual cue-specific craving is diminished in stressed smokers.

PubMed

Cochran, Justinn R; Consedine, Nathan S; Lee, John M J; Pandit, Chinmay; Sollers, John J; Kydd, Robert R

2017-09-01

Craving among smokers is increased by stress and exposure to smoking-related visual cues. However, few experimental studies have tested both elicitors concurrently and considered how exposures may interact to influence craving. The current study examined craving in response to stress and visual cue exposure, separately and in succession, in order to better understand the relationship between craving elicitation and the elicitor. Thirty-nine smokers (21 males) who forwent smoking for 30 minutes were randomized to complete a stress task and a visual cue task in counterbalanced orders (creating the experimental groups); for the cue task, counterbalanced blocks of neutral, motivational control, and smoking images were presented. Self-reported craving was assessed after each block of visual stimuli and stress task, and after a recovery period following each task. As expected, the stress and smoking images generated greater craving than neutral or motivational control images (p < .001). Interactions indicated craving in those who completed the stress task first differed from those who completed the visual cues task first (p < .05), such that stress task craving was greater than all image type craving (all p's < .05) only if the visual cue task was completed first. Conversely, craving was stable across image types when the stress task was completed first. Findings indicate when smokers are stressed, visual cues have little additive effect on craving, and different types of visual cues elicit comparable craving. These findings may imply that once stressed, smokers will crave cigarettes comparably notwithstanding whether they are exposed to smoking image cues.

Development of a Model for Measuring Scientific Processing Skills Based on Brain-Imaging Technology: Focused on the Experimental Design Process

ERIC Educational Resources Information Center

Lee, Il-Sun; Byeon, Jung-Ho; Kim, Young-shin; Kwon, Yong-Ju

2014-01-01

The purpose of this study was to develop a model for measuring experimental design ability based on functional magnetic resonance imaging (fMRI) during biological inquiry. More specifically, the researchers developed an experimental design task that measures experimental design ability. Using the developed experimental design task, they measured…

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, G; Pan, X; Stayman, J

2014-06-15

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within themore » reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.« less

Anterior temporal cortex and semantic memory: reconciling findings from neuropsychology and functional imaging.

PubMed

Rogers, Timothy T; Hocking, Julia; Noppeney, Uta; Mechelli, Andrea; Gorno-Tempini, Maria Luisa; Patterson, Karalyn; Price, Cathy J

2006-09-01

Studies of semantic impairment arising from brain disease suggest that the anterior temporal lobes are critical for semantic abilities in humans; yet activation of these regions is rarely reported in functional imaging studies of healthy controls performing semantic tasks. Here, we combined neuropsychological and PET functional imaging data to show that when healthy subjects identify concepts at a specific level, the regions activated correspond to the site of maximal atrophy in patients with relatively pure semantic impairment. The stimuli were color photographs of common animals or vehicles, and the task was category verification at specific (e.g., robin), intermediate (e.g., bird), or general (e.g., animal) levels. Specific, relative to general, categorization activated the antero-lateral temporal cortices bilaterally, despite matching of these experimental conditions for difficulty. Critically, in patients with atrophy in precisely these areas, the most pronounced deficit was in the retrieval of specific semantic information.

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

Recognizable or Not: Towards Image Semantic Quality Assessment for Compression

NASA Astrophysics Data System (ADS)

Liu, Dong; Wang, Dandan; Li, Houqiang

2017-12-01

Traditionally, image compression was optimized for the pixel-wise fidelity or the perceptual quality of the compressed images given a bit-rate budget. But recently, compressed images are more and more utilized for automatic semantic analysis tasks such as recognition and retrieval. For these tasks, we argue that the optimization target of compression is no longer perceptual quality, but the utility of the compressed images in the given automatic semantic analysis task. Accordingly, we propose to evaluate the quality of the compressed images neither at pixel level nor at perceptual level, but at semantic level. In this paper, we make preliminary efforts towards image semantic quality assessment (ISQA), focusing on the task of optical character recognition (OCR) from compressed images. We propose a full-reference ISQA measure by comparing the features extracted from text regions of original and compressed images. We then propose to integrate the ISQA measure into an image compression scheme. Experimental results show that our proposed ISQA measure is much better than PSNR and SSIM in evaluating the semantic quality of compressed images; accordingly, adopting our ISQA measure to optimize compression for OCR leads to significant bit-rate saving compared to using PSNR or SSIM. Moreover, we perform subjective test about text recognition from compressed images, and observe that our ISQA measure has high consistency with subjective recognizability. Our work explores new dimensions in image quality assessment, and demonstrates promising direction to achieve higher compression ratio for specific semantic analysis tasks.

Comparison of gesture and conventional interaction techniques for interventional neuroradiology.

PubMed

Hettig, Julian; Saalfeld, Patrick; Luz, Maria; Becker, Mathias; Skalej, Martin; Hansen, Christian

2017-09-01

Interaction with radiological image data and volume renderings within a sterile environment is a challenging task. Clinically established methods such as joystick control and task delegation can be time-consuming and error-prone and interrupt the workflow. New touchless input modalities may have the potential to overcome these limitations, but their value compared to established methods is unclear. We present a comparative evaluation to analyze the value of two gesture input modalities (Myo Gesture Control Armband and Leap Motion Controller) versus two clinically established methods (task delegation and joystick control). A user study was conducted with ten experienced radiologists by simulating a diagnostic neuroradiological vascular treatment with two frequently used interaction tasks in an experimental operating room. The input modalities were assessed using task completion time, perceived task difficulty, and subjective workload. Overall, the clinically established method of task delegation performed best under the study conditions. In general, gesture control failed to exceed the clinical input approach. However, the Myo Gesture Control Armband showed a potential for simple image selection task. Novel input modalities have the potential to take over single tasks more efficiently than clinically established methods. The results of our user study show the relevance of task characteristics such as task complexity on performance with specific input modalities. Accordingly, future work should consider task characteristics to provide a useful gesture interface for a specific use case instead of an all-in-one solution.

Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights

PubMed Central

Akbari, Hamed; Bilello, Michel; Da, Xiao; Davatzikos, Christos

2015-01-01

Evaluating various algorithms for the inter-subject registration of brain magnetic resonance images (MRI) is a necessary topic receiving growing attention. Existing studies evaluated image registration algorithms in specific tasks or using specific databases (e.g., only for skull-stripped images, only for single-site images, etc.). Consequently, the choice of registration algorithms seems task- and usage/parameter-dependent. Nevertheless, recent large-scale, often multi-institutional imaging-related studies create the need and raise the question whether some registration algorithms can 1) generally apply to various tasks/databases posing various challenges; 2) perform consistently well, and while doing so, 3) require minimal or ideally no parameter tuning. In seeking answers to this question, we evaluated 12 general-purpose registration algorithms, for their generality, accuracy and robustness. We fixed their parameters at values suggested by algorithm developers as reported in the literature. We tested them in 7 databases/tasks, which present one or more of 4 commonly-encountered challenges: 1) inter-subject anatomical variability in skull-stripped images; 2) intensity homogeneity, noise and large structural differences in raw images; 3) imaging protocol and field-of-view (FOV) differences in multi-site data; and 4) missing correspondences in pathology-bearing images. Totally 7,562 registrations were performed. Registration accuracies were measured by (multi-)expert-annotated landmarks or regions of interest (ROIs). To ensure reproducibility, we used public software tools, public databases (whenever possible), and we fully disclose the parameter settings. We show evaluation results, and discuss the performances in light of algorithms’ similarity metrics, transformation models and optimization strategies. We also discuss future directions for the algorithm development and evaluations. PMID:24951685

Reinventing Image Detective: An Evidence-Based Approach to Citizen Science Online

NASA Astrophysics Data System (ADS)

Romano, C.; Graff, P. V.; Runco, S.

2017-12-01

Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online?Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image.Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project:• Concise explanation of the project, its context, and its purpose;• Including a mention of the funding agency (in this case, NASA);• A preview of the specific tasks required of participants;• A dedicated user interface for the actual citizen science interaction.In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.

Reinventing Image Detective: An Evidence-Based Approach to Citizen Science Online

NASA Technical Reports Server (NTRS)

Romano, Cia; Graff, Paige V.; Runco, Susan

2017-01-01

Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online? Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image. Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project: (1) Concise explanation of the project, its context, and its purpose; (2) Including a mention of the funding agency (in this case, NASA); (3) A preview of the specific tasks required of participants; (4) A dedicated user interface for the actual citizen science interaction. In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.

Specific-Token Effects in Screening Tasks: Possible Implications for Aviation Security

ERIC Educational Resources Information Center

Smith, J. David; Redford, Joshua S.; Washburn, David A.; Taglialatela, Lauren A.

2005-01-01

Screeners at airport security checkpoints perform an important categorization task in which they search for threat items in complex x-ray images. But little is known about how the processes of categorization stand up to visual complexity. The authors filled this research gap with screening tasks in which participants searched for members of target…

Differences in perceptual learning transfer as a function of training task.

PubMed

Green, C Shawn; Kattner, Florian; Siegel, Max H; Kersten, Daniel; Schrater, Paul R

2015-01-01

A growing body of research--including results from behavioral psychology, human structural and functional imaging, single-cell recordings in nonhuman primates, and computational modeling--suggests that perceptual learning effects are best understood as a change in the ability of higher-level integration or association areas to read out sensory information in the service of particular decisions. Work in this vein has argued that, depending on the training experience, the "rules" for this read-out can either be applicable to new contexts (thus engendering learning generalization) or can apply only to the exact training context (thus resulting in learning specificity). Here we contrast learning tasks designed to promote either stimulus-specific or stimulus-general rules. Specifically, we compare learning transfer across visual orientation following training on three different tasks: an orientation categorization task (which permits an orientation-specific learning solution), an orientation estimation task (which requires an orientation-general learning solution), and an orientation categorization task in which the relevant category boundary shifts on every trial (which lies somewhere between the two tasks above). While the simple orientation-categorization training task resulted in orientation-specific learning, the estimation and moving categorization tasks resulted in significant orientation learning generalization. The general framework tested here--that task specificity or generality can be predicted via an examination of the optimal learning solution--may be useful in building future training paradigms with certain desired outcomes.

Distributed encoding of spatial and object categories in primate hippocampal microcircuits

PubMed Central

Opris, Ioan; Santos, Lucas M.; Gerhardt, Greg A.; Song, Dong; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.

2015-01-01

The primate hippocampus plays critical roles in the encoding, representation, categorization and retrieval of cognitive information. Such cognitive abilities may use the transformational input-output properties of hippocampal laminar microcircuitry to generate spatial representations and to categorize features of objects, images, and their numeric characteristics. Four nonhuman primates were trained in a delayed-match-to-sample (DMS) task while multi-neuron activity was simultaneously recorded from the CA1 and CA3 hippocampal cell fields. The results show differential encoding of spatial location and categorization of images presented as relevant stimuli in the task. Individual hippocampal cells encoded visual stimuli only on specific types of trials in which retention of either, the Sample image, or the spatial position of the Sample image indicated at the beginning of the trial, was required. Consistent with such encoding, it was shown that patterned microstimulation applied during Sample image presentation facilitated selection of either Sample image spatial locations or types of images, during the Match phase of the task. These findings support the existence of specific codes for spatial and numeric object representations in primate hippocampus which can be applied on differentially signaled trials. Moreover, the transformational properties of hippocampal microcircuitry, together with the patterned microstimulation are supporting the practical importance of this approach for cognitive enhancement and rehabilitation, needed for memory neuroprosthetics. PMID:26500473

Brain activations during bimodal dual tasks depend on the nature and combination of component tasks

PubMed Central

Salo, Emma; Rinne, Teemu; Salonen, Oili; Alho, Kimmo

2015-01-01

We used functional magnetic resonance imaging to investigate brain activations during nine different dual tasks in which the participants were required to simultaneously attend to concurrent streams of spoken syllables and written letters. They performed a phonological, spatial or “simple” (speaker-gender or font-shade) discrimination task within each modality. We expected to find activations associated specifically with dual tasking especially in the frontal and parietal cortices. However, no brain areas showed systematic dual task enhancements common for all dual tasks. Further analysis revealed that dual tasks including component tasks that were according to Baddeley's model “modality atypical,” that is, the auditory spatial task or the visual phonological task, were not associated with enhanced frontal activity. In contrast, for other dual tasks, activity specifically associated with dual tasking was found in the left or bilateral frontal cortices. Enhanced activation in parietal areas, however, appeared not to be specifically associated with dual tasking per se, but rather with intermodal attention switching. We also expected effects of dual tasking in left frontal supramodal phonological processing areas when both component tasks required phonological processing and in right parietal supramodal spatial processing areas when both tasks required spatial processing. However, no such effects were found during these dual tasks compared with their component tasks performed separately. Taken together, the current results indicate that activations during dual tasks depend in a complex manner on specific demands of component tasks. PMID:25767443

Benchmarking image fusion system design parameters

NASA Astrophysics Data System (ADS)

Howell, Christopher L.

2013-06-01

A clear and absolute method for discriminating between image fusion algorithm performances is presented. This method can effectively be used to assist in the design and modeling of image fusion systems. Specifically, it is postulated that quantifying human task performance using image fusion should be benchmarked to whether the fusion algorithm, at a minimum, retained the performance benefit achievable by each independent spectral band being fused. The established benchmark would then clearly represent the threshold that a fusion system should surpass to be considered beneficial to a particular task. A genetic algorithm is employed to characterize the fused system parameters using a Matlab® implementation of NVThermIP as the objective function. By setting the problem up as a mixed-integer constraint optimization problem, one can effectively look backwards through the image acquisition process: optimizing fused system parameters by minimizing the difference between modeled task difficulty measure and the benchmark task difficulty measure. The results of an identification perception experiment are presented, where human observers were asked to identify a standard set of military targets, and used to demonstrate the effectiveness of the benchmarking process.

Task demands determine the specificity of the search template.

PubMed

Bravo, Mary J; Farid, Hany

2012-01-01

When searching for an object, an observer holds a representation of the target in mind while scanning the scene. If the observer repeats the search, performance may become more efficient as the observer hones this target representation, or "search template," to match the specific demands of the search task. An effective search template must have two characteristics: It must reliably discriminate the target from the distractors, and it must tolerate variability in the appearance of the target. The present experiment examined how the tolerance of the search template is affected by the search task. Two groups of 18 observers trained on the same set of stimuli blocked either by target image (block-by-image group) or by target category (block-by-category group). One or two days after training, both groups were tested on a related search task. The pattern of test results revealed that the two groups of observers had developed different search templates, and that the templates of the block-by-category observers better captured the general characteristics of the category. These results demonstrate that observers match their search templates to the demands of the search task.

Specific PET Imaging Probes for Early Detection of Prostate Cancer Metastases

DTIC Science & Technology

2009-05-01

REPORT DATE (DD-MM-YYYY) 31/05/2009 2 . REPORT TYPE Annual 3. DATES COVERED (From - To) 1 MAY 2008-30 APR 2009 4. TITLE AND SUBTITLE Specific...specific” GAG (Objective I) Task 2 : Synthesis of L-, D-, or mixed L/D polyargines (Objective II) Task 3: In vitro and in vivo evaluation of the FITC...tagged polyarginines (Objective II) Months 6 – 24: Task 4: Prepare p-SCN-Bn-CB-TE2A and the multivalent scaffolds (CB-TE2O-(PEG-COOH) 2 or CB- TE2A-(PEG

“No level up!”: no effects of video game specialization and expertise on cognitive performance

PubMed Central

Gobet, Fernand; Johnston, Stephen J.; Ferrufino, Gabriella; Johnston, Matthew; Jones, Michael B.; Molyneux, Antonia; Terzis, Argyrios; Weeden, Luke

2014-01-01

Previous research into the effects of action video gaming on cognition has suggested that long term exposure to this type of game might lead to an enhancement of cognitive skills that transfer to non-gaming cognitive tasks. However, these results have been controversial. The aim of the current study was to test the presence of positive cognitive transfer from action video games to two cognitive tasks. More specifically, this study investigated the effects that participants' expertise and genre specialization have on cognitive improvements in one task unrelated to video gaming (a flanker task) and one related task (change detection task with both control and genre-specific images). This study was unique in three ways. Firstly, it analyzed a continuum of expertise levels, which has yet to be investigated in research into the cognitive benefits of video gaming. Secondly, it explored genre-specific skill developments on these tasks by comparing Action and Strategy video game players (VGPs). Thirdly, it used a very tight experiment design, including the experimenter being blind to expertise level and genre specialization of the participant. Ninety-two university students aged between 18 and 30 (M = 21.25) were recruited through opportunistic sampling and were grouped by video game specialization and expertise level. While the results of the flanker task were consistent with previous research (i.e., effect of congruence), there was no effect of expertise, and the action gamers failed to outperform the strategy gamers. Additionally, contrary to expectation, there was no interaction between genre specialization and image type in the change detection task, again demonstrating no expertise effect. The lack of effects for game specialization and expertise goes against previous research on the positive effects of action video gaming on other cognitive tasks. PMID:25506330

"No level up!": no effects of video game specialization and expertise on cognitive performance.

PubMed

Gobet, Fernand; Johnston, Stephen J; Ferrufino, Gabriella; Johnston, Matthew; Jones, Michael B; Molyneux, Antonia; Terzis, Argyrios; Weeden, Luke

2014-01-01

Previous research into the effects of action video gaming on cognition has suggested that long term exposure to this type of game might lead to an enhancement of cognitive skills that transfer to non-gaming cognitive tasks. However, these results have been controversial. The aim of the current study was to test the presence of positive cognitive transfer from action video games to two cognitive tasks. More specifically, this study investigated the effects that participants' expertise and genre specialization have on cognitive improvements in one task unrelated to video gaming (a flanker task) and one related task (change detection task with both control and genre-specific images). This study was unique in three ways. Firstly, it analyzed a continuum of expertise levels, which has yet to be investigated in research into the cognitive benefits of video gaming. Secondly, it explored genre-specific skill developments on these tasks by comparing Action and Strategy video game players (VGPs). Thirdly, it used a very tight experiment design, including the experimenter being blind to expertise level and genre specialization of the participant. Ninety-two university students aged between 18 and 30 (M = 21.25) were recruited through opportunistic sampling and were grouped by video game specialization and expertise level. While the results of the flanker task were consistent with previous research (i.e., effect of congruence), there was no effect of expertise, and the action gamers failed to outperform the strategy gamers. Additionally, contrary to expectation, there was no interaction between genre specialization and image type in the change detection task, again demonstrating no expertise effect. The lack of effects for game specialization and expertise goes against previous research on the positive effects of action video gaming on other cognitive tasks.

Behavioral and neural stability of attention bias to threat in healthy adolescents

PubMed Central

Britton, Jennifer C.; Sequeira, Stefanie; Ronkin, Emily G.; Chen, Gang; Bar-Haim, Yair; Shechner, Tomer; Ernst, Monique; Fox, Nathan A.; Leibenluft, Ellen; Pine, Daniel S.

2016-01-01

Considerable translational research on anxiety examines attention bias to threat and the efficacy of attention training in reducing symptoms. Imaging research on the stability of brain functions engaged by attention bias tasks could inform such research. Perturbed fronto-amygdala function consistently arises in attention bias research on adolescent anxiety. The current report examines the stability of the activation and functional connectivity of these regions on the dot-probe task. Functional magnetic resonance imaging (fMRI) activation and connectivity data were acquired with the dot-probe task in 39 healthy youth (f =18, Mean Age = 13.71 years, SD = 2.31) at two time points, separated by approximately nine weeks. Intraclass-correlations demonstrate good reliability in both neural activation for the ventrolateral PFC and task-specific connectivity for fronto-amygdala circuitry. Behavioral measures showed generally poor test-retest reliability. These findings suggest potential avenues for future brain imaging work by highlighting brain circuitry manifesting stable functioning on the dot-probe attention bias task. PMID:27129757

A new multi-spectral feature level image fusion method for human interpretation

NASA Astrophysics Data System (ADS)

Leviner, Marom; Maltz, Masha

2009-03-01

Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in a three-task experiment using MSSF against two established methods: averaging and principle components analysis (PCA), and against its two source bands, visible and infrared. The three tasks that we studied were: (1) simple target detection, (2) spatial orientation, and (3) camouflaged target detection. MSSF proved superior to the other fusion methods in all three tests; MSSF also outperformed the source images in the spatial orientation and camouflaged target detection tasks. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.

Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

PubMed

Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

2018-06-02

This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

Improving accuracy and power with transfer learning using a meta-analytic database.

PubMed

Schwartz, Yannick; Varoquaux, Gaël; Pallier, Christophe; Pinel, Philippe; Poline, Jean-Baptiste; Thirion, Bertrand

2012-01-01

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e., to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts.

Face recognition: database acquisition, hybrid algorithms, and human studies

NASA Astrophysics Data System (ADS)

Gutta, Srinivas; Huang, Jeffrey R.; Singh, Dig; Wechsler, Harry

1997-02-01

One of the most important technologies absent in traditional and emerging frontiers of computing is the management of visual information. Faces are accessible `windows' into the mechanisms that govern our emotional and social lives. The corresponding face recognition tasks considered herein include: (1) Surveillance, (2) CBIR, and (3) CBIR subject to correct ID (`match') displaying specific facial landmarks such as wearing glasses. We developed robust matching (`classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET database. The hybrid classifier architecture consist of an ensemble of connectionist networks--radial basis functions-- and decision trees. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, and (b) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experimental results, proving the feasibility of our approach, yield (i) 96% accuracy, using cross validation (CV), for surveillance on a data base consisting of 904 images (ii) 97% accuracy for CBIR tasks, on a database of 1084 images, and (iii) 93% accuracy, using CV, for CBIR subject to correct ID match tasks on a data base of 200 images.

A FUNCTIONAL NEUROIMAGING INVESTIGATION OF THE ROLES OF STRUCTURAL COMPLEXITY AND TASK-DEMAND DURING AUDITORY SENTENCE PROCESSING

PubMed Central

Love, Tracy; Haist, Frank; Nicol, Janet; Swinney, David

2009-01-01

Using functional magnetic resonance imaging (fMRI), this study directly examined an issue that bridges the potential language processing and multi-modal views of the role of Broca’s area: the effects of task-demands in language comprehension studies. We presented syntactically simple and complex sentences for auditory comprehension under three different (differentially complex) task-demand conditions: passive listening, probe verification, and theme judgment. Contrary to many language imaging findings, we found that both simple and complex syntactic structures activated left inferior frontal cortex (L-IFC). Critically, we found activation in these frontal regions increased together with increased task-demands. Specifically, tasks that required greater manipulation and comparison of linguistic material recruited L-IFC more strongly; independent of syntactic structure complexity. We argue that much of the presumed syntactic effects previously found in sentence imaging studies of L-IFC may, among other things, reflect the tasks employed in these studies and that L-IFC is a region underlying mnemonic and other integrative functions, on which much language processing may rely. PMID:16881268

Age-related decline in task switching is linked to both global and tract-specific changes in white matter microstructure.

PubMed

Jolly, Todd A D; Cooper, Patrick S; Rennie, Jaime L; Levi, Christopher R; Lenroot, Rhoshel; Parsons, Mark W; Michie, Patricia T; Karayanidis, Frini

2017-03-01

Task-switching performance relies on a broadly distributed frontoparietal network and declines in older adults. In this study, they investigated whether this age-related decline in task switching performance was mediated by variability in global or regional white matter microstructural health. Seventy cognitively intact adults (43-87 years) completed a cued-trials task switching paradigm. Microstructural white matter measures were derived using diffusion tensor imaging (DTI) analyses on the diffusion-weighted imaging (DWI) sequence. Task switching performance decreased with increasing age and radial diffusivity (RaD), a measure of white matter microstructure that is sensitive to myelin structure. RaD mediated the relationship between age and task switching performance. However, the relationship between RaD and task switching performance remained significant when controlling for age and was stronger in the presence of cardiovascular risk factors. Variability in error and RT mixing cost were associated with RaD in global white matter and in frontoparietal white matter tracts, respectively. These findings suggest that age-related increase in mixing cost may result from both global and tract-specific disruption of cerebral white matter linked to the increased incidence of cardiovascular risks in older adults. Hum Brain Mapp 38:1588-1603, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Optimization of medical imaging display systems: using the channelized Hotelling observer for detecting lung nodules: experimental study

NASA Astrophysics Data System (ADS)

Platisa, Ljiljana; Vansteenkiste, Ewout; Goossens, Bart; Marchessoux, Cédric; Kimpe, Tom; Philips, Wilfried

2009-02-01

Medical-imaging systems are designed to aid medical specialists in a specific task. Therefore, the physical parameters of a system need to optimize the task performance of a human observer. This requires measurements of human performance in a given task during the system optimization. Typically, psychophysical studies are conducted for this purpose. Numerical observer models have been successfully used to predict human performance in several detection tasks. Especially, the task of signal detection using a channelized Hotelling observer (CHO) in simulated images has been widely explored. However, there are few studies done for clinically acquired images that also contain anatomic noise. In this paper, we investigate the performance of a CHO in the task of detecting lung nodules in real radiographic images of the chest. To evaluate variability introduced by the limited available data, we employ a commonly used study of a multi-reader multi-case (MRMC) scenario. It accounts for both case and reader variability. Finally, we use the "oneshot" methods to estimate the MRMC variance of the area under the ROC curve (AUC). The obtained AUC compares well to those reported for human observer study on a similar data set. Furthermore, the "one-shot" analysis implies a fairly consistent performance of the CHO with the variance of AUC below 0.002. This indicates promising potential for numerical observers in optimization of medical imaging displays and encourages further investigation on the subject.

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

PubMed Central

Janowczyk, Andrew; Madabhushi, Anant

2016-01-01

Background: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific “handcrafted” features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. Aims: This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Results: Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). Conclusion: This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data. PMID:27563488

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

PubMed

Janowczyk, Andrew; Madabhushi, Anant

2016-01-01

Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data.

Task-Driven Tube Current Modulation and Regularization Design in Computed Tomography with Penalized-Likelihood Reconstruction.

PubMed

Gang, G J; Siewerdsen, J H; Stayman, J W

2016-02-01

This work applies task-driven optimization to design CT tube current modulation and directional regularization in penalized-likelihood (PL) reconstruction. The relative performance of modulation schemes commonly adopted for filtered-backprojection (FBP) reconstruction were also evaluated for PL in comparison. We adopt a task-driven imaging framework that utilizes a patient-specific anatomical model and information of the imaging task to optimize imaging performance in terms of detectability index ( d' ). This framework leverages a theoretical model based on implicit function theorem and Fourier approximations to predict local spatial resolution and noise characteristics of PL reconstruction as a function of the imaging parameters to be optimized. Tube current modulation was parameterized as a linear combination of Gaussian basis functions, and regularization was based on the design of (directional) pairwise penalty weights for the 8 in-plane neighboring voxels. Detectability was optimized using a covariance matrix adaptation evolutionary strategy algorithm. Task-driven designs were compared to conventional tube current modulation strategies for a Gaussian detection task in an abdomen phantom. The task-driven design yielded the best performance, improving d' by ~20% over an unmodulated acquisition. Contrary to FBP, PL reconstruction using automatic exposure control and modulation based on minimum variance (in FBP) performed worse than the unmodulated case, decreasing d' by 16% and 9%, respectively. This work shows that conventional tube current modulation schemes suitable for FBP can be suboptimal for PL reconstruction. Thus, the proposed task-driven optimization provides additional opportunities for improved imaging performance and dose reduction beyond that achievable with conventional acquisition and reconstruction.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Independent component model for cognitive functions of multiple subjects using [15O]H2O PET images.

PubMed

Park, Hae-Jeong; Kim, Jae-Jin; Youn, Tak; Lee, Dong Soo; Lee, Myung Chul; Kwon, Jun Soo

2003-04-01

An independent component model of multiple subjects' positron emission tomography (PET) images is proposed to explore the overall functional components involved in a task and to explain subject specific variations of metabolic activities under altered experimental conditions utilizing the Independent component analysis (ICA) concept. As PET images represent time-compressed activities of several cognitive components, we derived a mathematical model to decompose functional components from cross-sectional images based on two fundamental hypotheses: (1) all subjects share basic functional components that are common to subjects and spatially independent of each other in relation to the given experimental task, and (2) all subjects share common functional components throughout tasks which are also spatially independent. The variations of hemodynamic activities according to subjects or tasks can be explained by the variations in the usage weight of the functional components. We investigated the plausibility of the model using serial cognitive experiments of simple object perception, object recognition, two-back working memory, and divided attention of a syntactic process. We found that the independent component model satisfactorily explained the functional components involved in the task and discuss here the application of ICA in multiple subjects' PET images to explore the functional association of brain activations. Copyright 2003 Wiley-Liss, Inc.

Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal

PubMed Central

Trucco, Emanuele; Ruggeri, Alfredo; Karnowski, Thomas; Giancardo, Luca; Chaum, Edward; Hubschman, Jean Pierre; al-Diri, Bashir; Cheung, Carol Y.; Wong, Damon; Abràmoff, Michael; Lim, Gilbert; Kumar, Dinesh; Burlina, Philippe; Bressler, Neil M.; Jelinek, Herbert F.; Meriaudeau, Fabrice; Quellec, Gwénolé; MacGillivray, Tom; Dhillon, Bal

2013-01-01

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison. PMID:23794433

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

PubMed

Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei

2017-07-01

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

The power of Kawaii: viewing cute images promotes a careful behavior and narrows attentional focus.

PubMed

Nittono, Hiroshi; Fukushima, Michiko; Yano, Akihiro; Moriya, Hiroki

2012-01-01

Kawaii (a Japanese word meaning "cute") things are popular because they produce positive feelings. However, their effect on behavior remains unclear. In this study, three experiments were conducted to examine the effects of viewing cute images on subsequent task performance. In the first experiment, university students performed a fine motor dexterity task before and after viewing images of baby or adult animals. Performance indexed by the number of successful trials increased after viewing cute images (puppies and kittens; M ± SE=43.9 ± 10.3% improvement) more than after viewing images that were less cute (dogs and cats; 11.9 ± 5.5% improvement). In the second experiment, this finding was replicated by using a non-motor visual search task. Performance improved more after viewing cute images (15.7 ± 2.2% improvement) than after viewing less cute images (1.4 ± 2.1% improvement). Viewing images of pleasant foods was ineffective in improving performance (1.2 ± 2.1%). In the third experiment, participants performed a global-local letter task after viewing images of baby animals, adult animals, and neutral objects. In general, global features were processed faster than local features. However, this global precedence effect was reduced after viewing cute images. Results show that participants performed tasks requiring focused attention more carefully after viewing cute images. This is interpreted as the result of a narrowed attentional focus induced by the cuteness-triggered positive emotion that is associated with approach motivation and the tendency toward systematic processing. For future applications, cute objects may be used as an emotion elicitor to induce careful behavioral tendencies in specific situations, such as driving and office work.

The Power of Kawaii: Viewing Cute Images Promotes a Careful Behavior and Narrows Attentional Focus

PubMed Central

Nittono, Hiroshi; Fukushima, Michiko; Yano, Akihiro; Moriya, Hiroki

2012-01-01

Kawaii (a Japanese word meaning “cute”) things are popular because they produce positive feelings. However, their effect on behavior remains unclear. In this study, three experiments were conducted to examine the effects of viewing cute images on subsequent task performance. In the first experiment, university students performed a fine motor dexterity task before and after viewing images of baby or adult animals. Performance indexed by the number of successful trials increased after viewing cute images (puppies and kittens; M ± SE = 43.9±10.3% improvement) more than after viewing images that were less cute (dogs and cats; 11.9±5.5% improvement). In the second experiment, this finding was replicated by using a non-motor visual search task. Performance improved more after viewing cute images (15.7±2.2% improvement) than after viewing less cute images (1.4±2.1% improvement). Viewing images of pleasant foods was ineffective in improving performance (1.2±2.1%). In the third experiment, participants performed a global–local letter task after viewing images of baby animals, adult animals, and neutral objects. In general, global features were processed faster than local features. However, this global precedence effect was reduced after viewing cute images. Results show that participants performed tasks requiring focused attention more carefully after viewing cute images. This is interpreted as the result of a narrowed attentional focus induced by the cuteness-triggered positive emotion that is associated with approach motivation and the tendency toward systematic processing. For future applications, cute objects may be used as an emotion elicitor to induce careful behavioral tendencies in specific situations, such as driving and office work. PMID:23050022

Processing of Intentional and Automatic Number Magnitudes in Children Born Prematurely: Evidence From fMRI

PubMed Central

Klein, Elise; Moeller, Korbinian; Kiechl-Kohlendorfer, Ursula; Kremser, Christian; Starke, Marc; Cohen Kadosh, Roi; Pupp-Peglow, Ulrike; Schocke, Michael; Kaufmann, Liane

2014-01-01

This study examined the neural correlates of intentional and automatic number processing (indexed by number comparison and physical Stroop task, respectively) in 6- and 7-year-old children born prematurely. Behavioral results revealed significant numerical distance and size congruity effects. Imaging results disclosed (1) largely overlapping fronto-parietal activation for intentional and automatic number processing, (2) a frontal to parietal shift of activation upon considering the risk factors gestational age and birth weight, and (3) a task-specific link between math proficiency and functional magnetic resonance imaging (fMRI) signal within distinct regions of the parietal lobes—indicating commonalities but also specificities of intentional and automatic number processing. PMID:25090014

Mathematics education graduate students' understanding of trigonometric ratios

NASA Astrophysics Data System (ADS)

Yiǧit Koyunkaya, Melike

2016-10-01

This study describes mathematics education graduate students' understanding of relationships between sine and cosine of two base angles in a right triangle. To explore students' understanding of these relationships, an elaboration of Skemp's views of instrumental and relational understanding using Tall and Vinner's concept image and concept definition was developed. Nine students volunteered to complete three paper and pencil tasks designed to elicit evidence of understanding and three students among these nine students volunteered for semi-structured interviews. As a result of fine-grained analysis of the students' responses to the tasks, the evidence of concept image and concept definition as well as instrumental and relational understanding of trigonometric ratios was found. The unit circle and a right triangle were identified as students' concept images, and the mnemonic was determined as their concept definition for trigonometry, specifically for trigonometric ratios. It is also suggested that students had instrumental understanding of trigonometric ratios while they were less flexible to act on trigonometric ratio tasks and had limited relational understanding. Additionally, the results indicate that graduate students' understanding of the concept of angle mediated their understanding of trigonometry, specifically trigonometric ratios.

Hemispheric dominance during the mental rotation task in patients with schizophrenia.

PubMed

Chen, Jiu; Yang, Laiqi; Zhao, Jin; Li, Lanlan; Liu, Guangxiong; Ma, Wentao; Zhang, Yan; Wu, Xingqu; Deng, Zihe; Tuo, Ran

2012-04-01

Mental rotation is a spatial representation conversion capability using an imagined object and either object or self-rotation. This capability is impaired in schizophrenia. To provide a more detailed assessment of impaired cognitive functioning in schizophrenia by comparing the electrophysiological profiles of patients with schizophrenia and controls while completing a mental rotation task using both normally-oriented images and mirror images. This electroencephalographic study compared error rates, reaction times and the topographic map of event-related potentials in 32 participants with schizophrenia and 29 healthy controls during mental rotation tasks involving both normal images and mirror images. Among controls the mean error rate and the mean reaction time for normal images and mirror images were not significantly different but in the patient group the mean (sd) error rate was higher for mirror images than for normal images (42% [6%] vs. 32% [9%], t=2.64, p=0.031) and the mean reaction time was longer for mirror images than for normal images (587 [11] ms vs. 571 [18] ms, t=2.83, p=0.028). The amplitude of the P500 component at Pz (parietal area), Cz (central area), P3 (left parietal area) and P4 (right parietal area) were significantly lower in the patient group than in the control group for both normal images and mirror images. In both groups the P500 for both the normal and mirror images was significantly higher in the right parietal area (P4) compared with left parietal area (P3). The mental rotation abilities of patients with schizophrenia for both normally-oriented images and mirror images are impaired. Patients with schizophrenia show a diminished left cerebral contribution to the mental rotation task, a more rapid response time, and a differential response to normal images versus mirror images not seen in healthy controls. Specific topographic characteristics of the EEG during mental rotation tasks are potential biomarkers for schizophrenia.

A few of my favorite things: circumscribed interests in autism are not accompanied by increased attentional salience on a personalized selective attention task.

PubMed

Parsons, Owen E; Bayliss, Andrew P; Remington, Anna

2017-01-01

Autistic individuals commonly show circumscribed or "special" interests: areas of obsessive interest in a specific category. The present study investigated what impact these interests have on attention, an aspect of autistic cognition often reported as altered. In neurotypical individuals, interest and expertise have been shown to result in an automatic attentional priority for related items. Here, we examine whether this change in salience is also seen in autism. Adolescents and young adults with and without autism performed a personalized selective attention task assessing the level of attentional priority afforded to images related to the participant's specific interests. In addition, participants performed a similar task with generic images in order to isolate any effects of interest and expertise. Crucially, all autistic and non-autistic individuals recruited for this study held a strong passion or interest. As such, any differences in attention could not be solely attributed to differing prevalence of interests in the two groups. In both tasks, participants were asked to perform a central target-detection task while ignoring irrelevant distractors (related or unrelated to their interests). The level of distractor interference under various task conditions was taken as an indication of attentional priority. Neurotypical individuals showed the predicted attentional priority for the circumscribed interest images but not generic items, reflecting the impact of their interest and expertise. Contrary to predictions, autistic individuals did not show this priority: processing the interest-related stimuli only when task demands were low. Attention to images unrelated to circumscribed interests was equivalent in the two groups. These results suggest that despite autistic individuals holding an intense interest in a particular class of stimuli, there may be a reduced impact of this prior experience and expertise on attentional processing. The implications of this absence of automatic priority are discussed in terms of the behaviors associated with the condition.

Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prakash, P.; Zbijewski, W.; Gang, G. J.

2011-10-15

Purpose: This work applies a cascaded systems model for cone-beam CT imaging performance to the design and optimization of a system for musculoskeletal extremity imaging. The model provides a quantitative guide to the selection of system geometry, source and detector components, acquisition techniques, and reconstruction parameters. Methods: The model is based on cascaded systems analysis of the 3D noise-power spectrum (NPS) and noise-equivalent quanta (NEQ) combined with factors of system geometry (magnification, focal spot size, and scatter-to-primary ratio) and anatomical background clutter. The model was extended to task-based analysis of detectability index (d') for tasks ranging in contrast and frequencymore » content, and d' was computed as a function of system magnification, detector pixel size, focal spot size, kVp, dose, electronic noise, voxel size, and reconstruction filter to examine trade-offs and optima among such factors in multivariate analysis. The model was tested quantitatively versus the measured NPS and qualitatively in cadaver images as a function of kVp, dose, pixel size, and reconstruction filter under conditions corresponding to the proposed scanner. Results: The analysis quantified trade-offs among factors of spatial resolution, noise, and dose. System magnification (M) was a critical design parameter with strong effect on spatial resolution, dose, and x-ray scatter, and a fairly robust optimum was identified at M {approx} 1.3 for the imaging tasks considered. The results suggested kVp selection in the range of {approx}65-90 kVp, the lower end (65 kVp) maximizing subject contrast and the upper end maximizing NEQ (90 kVp). The analysis quantified fairly intuitive results--e.g., {approx}0.1-0.2 mm pixel size (and a sharp reconstruction filter) optimal for high-frequency tasks (bone detail) compared to {approx}0.4 mm pixel size (and a smooth reconstruction filter) for low-frequency (soft-tissue) tasks. This result suggests a specific protocol for 1 x 1 (full-resolution) projection data acquisition followed by full-resolution reconstruction with a sharp filter for high-frequency tasks along with 2 x 2 binning reconstruction with a smooth filter for low-frequency tasks. The analysis guided selection of specific source and detector components implemented on the proposed scanner. The analysis also quantified the potential benefits and points of diminishing return in focal spot size, reduced electronic noise, finer detector pixels, and low-dose limits of detectability. Theoretical results agreed quantitatively with the measured NPS and qualitatively with evaluation of cadaver images by a musculoskeletal radiologist. Conclusions: A fairly comprehensive model for 3D imaging performance in cone-beam CT combines factors of quantum noise, system geometry, anatomical background, and imaging task. The analysis provided a valuable, quantitative guide to design, optimization, and technique selection for a musculoskeletal extremities imaging system under development.« less

Sex-dependent age modulation of frontostriatal and temporo-parietal activation during cognitive control.

PubMed

Christakou, Anastasia; Halari, Rozmin; Smith, Anna B; Ifkovits, Eve; Brammer, Mick; Rubia, Katya

2009-10-15

Developmental functional imaging studies of cognitive control show progressive age-related increase in task-relevant fronto-striatal activation in male development from childhood to adulthood. Little is known, however, about how gender affects this functional development. In this study, we used event related functional magnetic resonance imaging to examine effects of sex, age, and their interaction on brain activation during attentional switching and interference inhibition, in 63 male and female adolescents and adults, aged 13 to 38. Linear age correlations were observed across all subjects in task-specific frontal, striatal and temporo-parietal activation. Gender analysis revealed increased activation in females relative to males in fronto-striatal areas during the Switch task, and laterality effects in the Simon task, with females showing increased left inferior prefrontal and temporal activation, and males showing increased right inferior prefrontal and parietal activation. Increased prefrontal activation clusters in females and increased parietal activation clusters in males furthermore overlapped with clusters that were age-correlated across the whole group, potentially reflecting more mature prefrontal brain activation patterns for females, and more mature parietal activation patterns for males. Gender by age interactions further supported this dissociation, revealing exclusive female-specific age correlations in inferior and medial prefrontal brain regions during both tasks, and exclusive male-specific age correlations in superior parietal (Switch task) and temporal regions (Simon task). These findings show increased recruitment of age-correlated prefrontal activation in females, and of age-correlated parietal activation in males, during tasks of cognitive control. Gender differences in frontal and parietal recruitment may thus be related to gender differences in the neurofunctional maturation of these brain regions.

American and Greek Children's Visual Images of Scientists: Enduring or Fading Stereotypes?

ERIC Educational Resources Information Center

Christidou, Vasilia; Bonoti, Fotini; Kontopoulou, Argiro

2016-01-01

This study explores American and Greek primary pupils' visual images of scientists by means of two nonverbal data collection tasks to identify possible convergences and divergences. Specifically, it aims to investigate whether their images of scientists vary according to the data collection instrument used and to gender. To this end, 91…

Material-specific difficulties in episodic memory tasks in mild traumatic brain injury.

PubMed

Tsirka, Vassiliki; Simos, Panagiotis; Vakis, Antonios; Vourkas, Michael; Arzoglou, Vasileios; Syrmos, Nikolaos; Stavropoulos, Stavros; Micheloyannis, Sifis

2010-03-01

The study examines acute, material-specific secondary memory performance in 26 patients with mild traumatic brain injury (MTBI) and 26 healthy controls, matched on demographic variables and indexes of crystallized intelligence. Neuropsychological tests were used to evaluate primary and secondary memory, executive functions, and verbal fluency. Participants were also tested on episodic memory tasks involving words, pseudowords, pictures of common objects, and abstract kaleidoscopic images. Patients showed reduced performance on episodic memory measures, and on tasks associated with visuospatial processing and executive function (Trail Making Test part B, semantic fluency). Significant differences between groups were also noted for correct rejections and response bias on the kaleidoscope task. MTBI patients' reduced performance on memory tasks for complex, abstract stimuli can be attributed to a dysfunction in the strategic component of memory process.

Persistent recruitment of somatosensory cortex during active maintenance of hand images in working memory.

PubMed

Galvez-Pol, A; Calvo-Merino, B; Capilla, A; Forster, B

2018-07-01

Working memory (WM) supports temporary maintenance of task-relevant information. This process is associated with persistent activity in the sensory cortex processing the information (e.g., visual stimuli activate visual cortex). However, we argue here that more multifaceted stimuli moderate this sensory-locked activity and recruit distinctive cortices. Specifically, perception of bodies recruits somatosensory cortex (SCx) beyond early visual areas (suggesting embodiment processes). Here we explore persistent activation in processing areas beyond the sensory cortex initially relevant to the modality of the stimuli. Using visual and somatosensory evoked-potentials in a visual WM task, we isolated different levels of visual and somatosensory involvement during encoding of body and non-body-related images. Persistent activity increased in SCx only when maintaining body images in WM, whereas visual/posterior regions' activity increased significantly when maintaining non-body images. Our results bridge WM and embodiment frameworks, supporting a dynamic WM process where the nature of the information summons specific processing resources. Copyright © 2018 Elsevier Inc. All rights reserved.

Set-relevance determines the impact of distractors on episodic memory retrieval.

PubMed

Kwok, Sze Chai; Shallice, Tim; Macaluso, Emiliano

2014-09-01

We investigated the interplay between stimulus-driven attention and memory retrieval with a novel interference paradigm that engaged both systems concurrently on each trial. Participants encoded a 45-min movie on Day 1 and, on Day 2, performed a temporal order judgment task during fMRI. Each retrieval trial comprised three images presented sequentially, and the task required participants to judge the temporal order of the first and the last images ("memory probes") while ignoring the second image, which was task irrelevant ("attention distractor"). We manipulated the content relatedness and the temporal proximity between the distractor and the memory probes, as well as the temporal distance between two probes. Behaviorally, short temporal distances between the probes led to reduced retrieval performance. Distractors that at encoding were temporally close to the first probe image reduced these costs, specifically when the distractor was content unrelated to the memory probes. The imaging results associated the distractor probe temporal proximity with activation of the right ventral attention network. By contrast, the precuneus was activated for high-content relatedness between distractors and probes and in trials including a short distance between the two memory probes. The engagement of the right ventral attention network by specific types of distractors suggests a link between stimulus-driven attention control and episodic memory retrieval, whereas the activation pattern of the precuneus implicates this region in memory search within knowledge/content-based hierarchies.

Image annotation based on positive-negative instances learning

NASA Astrophysics Data System (ADS)

Zhang, Kai; Hu, Jiwei; Liu, Quan; Lou, Ping

2017-07-01

Automatic image annotation is now a tough task in computer vision, the main sense of this tech is to deal with managing the massive image on the Internet and assisting intelligent retrieval. This paper designs a new image annotation model based on visual bag of words, using the low level features like color and texture information as well as mid-level feature as SIFT, and mixture the pic2pic, label2pic and label2label correlation to measure the correlation degree of labels and images. We aim to prune the specific features for each single label and formalize the annotation task as a learning process base on Positive-Negative Instances Learning. Experiments are performed using the Corel5K Dataset, and provide a quite promising result when comparing with other existing methods.

Using Proton Magnetic Resonance Imaging and Spectroscopy to Understand Brain "Activation"

ERIC Educational Resources Information Center

Baslow, Morris H.; Guilfoyle, David N.

2007-01-01

Upon stimulation, areas of the brain associated with specific cognitive processing tasks may undergo observable physiological changes, and measures of such changes have been used to create brain maps for visualization of stimulated areas in task-related brain "activation" studies. These perturbations usually continue throughout the period of the…

Event-Related fMRI of Category Learning: Differences in Classification and Feedback Networks

ERIC Educational Resources Information Center

Little, Deborah M.; Shin, Silvia S.; Sisco, Shannon M.; Thulborn, Keith R.

2006-01-01

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification…

Task-Dependent Modulations of Prefrontal and Hippocampal Activity during Intrinsic Word Production

ERIC Educational Resources Information Center

Whitney, Carin; Weis, Susanne; Krings, Timo; Huber, Walter; Grossman, Murray; Kircher, Tilo

2009-01-01

Functional imaging studies of single word production have consistently reported activation of the lateral prefrontal and cingulate cortex. Its contribution has been shown to be sensitive to task demands, which can be manipulated by the degree of response specification. Compared with classical verbal fluency, free word association relies less on…

A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging.

PubMed

Hong Kai Yap; Kamaldin, Nazir; Jeong Hoon Lim; Nasrallah, Fatima A; Goh, James Cho Hong; Chen-Hua Yeow

2017-06-01

In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.

Neuroticism related differences in the functional neuroanatomical correlates of multitasking. An fMRI study.

PubMed

Szameitat, Andre J; Saylik, Rahmi; Parton, Andrew

2016-12-02

It is known that neuroticism impairs cognitive performance mostly in difficult tasks, but not so much in easier tasks. One pervasive situation of this type is multitasking, in which the combination of two simple tasks creates a highly demanding dual-task, and consequently high neurotics show higher dual-task costs than low neurotics. However, the functional neuroanatomical correlates of these additional performance impairments in high neurotics are unknown. To test for this, we assessed brain activity by means of functional magnetic resonance imaging (fMRI) in 17 low and 15 high neurotics while they were performing a demanding dual-task and the less demanding component tasks as single-tasks. Behavioural results showed that performance (response times and error rates) was lower in the dual-task than in the single-tasks (dual-task costs), and that these dual-task costs were significantly higher in high neurotics. Imaging data showed that high neurotics showed less dual-task specific activation in lateral (mainly middle frontal gyrus) and medial prefrontal cortices. We conclude that high levels of neuroticism impair behavioural performance in demanding tasks, and that this impairment is accompanied by reduced activation of the task-associated brain areas. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

MO-D-213-06: Quantitative Image Quality Metrics Are for Physicists, Not Radiologists: How to Communicate to Your Radiologists Using Their Language

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szczykutowicz, T; Rubert, N; Ranallo, F

Purpose: A framework for explaining differences in image quality to non-technical audiences in medial imaging is needed. Currently, this task is something that is learned “on the job.” The lack of a formal methodology for communicating optimal acquisition parameters into the clinic effectively mitigates many technological advances. As a community, medical physicists need to be held responsible for not only advancing image science, but also for ensuring its proper use in the clinic. This work outlines a framework that bridges the gap between the results from quantitative image quality metrics like detectability, MTF, and NPS and their effect on specificmore » anatomical structures present in diagnostic imaging tasks. Methods: Specific structures of clinical importance were identified for a body, an extremity, a chest, and a temporal bone protocol. Using these structures, quantitative metrics were used to identify the parameter space that should yield optimal image quality constrained within the confines of clinical logistics and dose considerations. The reading room workflow for presenting the proposed changes for imaging each of these structures is presented. The workflow consists of displaying images for physician review consisting of different combinations of acquisition parameters guided by quantitative metrics. Examples of using detectability index, MTF, NPS, noise and noise non-uniformity are provided. During review, the physician was forced to judge the image quality solely on those features they need for diagnosis, not on the overall “look” of the image. Results: We found that in many cases, use of this framework settled mis-agreements between physicians. Once forced to judge images on the ability to detect specific structures inter reader agreement was obtained. Conclusion: This framework will provide consulting, research/industrial, or in-house physicists with clinically relevant imaging tasks to guide reading room image review. This framework avoids use of the overall “look” or “feel” to dictate acquisition parameter selection. Equipment grants GE Healthcare.« less

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention

PubMed Central

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L.

2012-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. Previous findings by our group strongly suggested that the changes in neural activity observed during increased cholinergic function may reflect an increase in neural efficiency that leads to improved task performance. The current study was designed to assess the effects of cholinergic enhancement on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover functional magnetic resonance imaging (fMRI) study. Following an infusion of physostigmine (1mg/hr) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions was reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Cholinergic enhancement also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions provide further support to the hypothesis that cholinergic augmentation results in enhanced neural efficiency. PMID:22906685

Characterization of the Structure and Function of the Normal Human Fovea Using Adaptive Optics Scanning Laser Ophthalmoscopy

NASA Astrophysics Data System (ADS)

Putnam, Nicole Marie

In order to study the limits of spatial vision in normal human subjects, it is important to look at and near the fovea. The fovea is the specialized part of the retina, the light-sensitive multi-layered neural tissue that lines the inner surface of the human eye, where the cone photoreceptors are smallest (approximately 2.5 microns or 0.5 arcmin) and cone density reaches a peak. In addition, there is a 1:1 mapping from the photoreceptors to the brain in this central region of the retina. As a result, the best spatial sampling is achieved in the fovea and it is the retinal location used for acuity and spatial vision tasks. However, vision is typically limited by the blur induced by the normal optics of the eye and clinical tests of foveal vision and foveal imaging are both limited due to the blur. As a result, it is unclear what the perceptual benefit of extremely high cone density is. Cutting-edge imaging technology, specifically Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO), can be utilized to remove this blur, zoom in, and as a result visualize individual cone photoreceptors throughout the central fovea. This imaging combined with simultaneous image stabilization and targeted stimulus delivery expands our understanding of both the anatomical structure of the fovea on a microscopic scale and the placement of stimuli within this retinal area during visual tasks. The final step is to investigate the role of temporal variables in spatial vision tasks since the eye is in constant motion even during steady fixation. In order to learn more about the fovea, it becomes important to study the effect of this motion on spatial vision tasks. This dissertation steps through many of these considerations, starting with a model of the foveal cone mosaic imaged with AOSLO. We then use this high resolution imaging to compare anatomical and functional markers of the center of the normal human fovea. Finally, we investigate the role of natural and manipulated fixational eye movements in foveal vision, specifically looking at a motion detection task, contrast sensitivity, and image fading.

Loneliness and Hypervigilance to Social Cues in Females: An Eye-Tracking Study

PubMed Central

Lodder, Gerine M. A.; Scholte, Ron H. J.; Clemens, Ivar A. H.; Engels, Rutger C. M. E.; Goossens, Luc; Verhagen, Maaike

2015-01-01

The goal of the present study was to examine whether lonely individuals differ from nonlonely individuals in their overt visual attention to social cues. Previous studies showed that loneliness was related to biased post-attentive processing of social cues (e.g., negative interpretation bias), but research on whether lonely and nonlonely individuals also show differences in an earlier information processing stage (gazing behavior) is very limited. A sample of 25 lonely and 25 nonlonely students took part in an eye-tracking study consisting of four tasks. We measured gazing (duration, number of fixations and first fixation) at the eyes, nose and mouth region of faces expressing emotions (Task 1), at emotion quadrants (anger, fear, happiness and neutral expression) (Task 2), at quadrants with positive and negative social and nonsocial images (Task 3), and at the facial area of actors in video clips with positive and negative content (Task 4). In general, participants tended to gaze most often and longest at areas that conveyed most social information, such as the eye region of the face (T1), and social images (T3). Participants gazed most often and longest at happy faces (T2) in still images, and more often and longer at the facial area in negative than in positive video clips (T4). No differences occurred between lonely and nonlonely participants in their gazing times and frequencies, nor at first fixations at social cues in the four different tasks. Based on this study, we found no evidence that overt visual attention to social cues differs between lonely and nonlonely individuals. This implies that biases in social information processing of lonely individuals may be limited to other phases of social information processing. Alternatively, biased overt attention to social cues may only occur under specific conditions, for specific stimuli or for specific lonely individuals. PMID:25915656

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

A New Semantic List Learning Task to Probe Functioning of the Papez Circuit

PubMed Central

Schallmo, Michael-Paul; Kassel, Michelle T.; Weisenbach, Sara L.; Walker, Sara J.; Guidotti-Breting, Leslie M.; Rao, Julia A.; Hazlett, Kathleen E.; Considine, Ciaran M.; Sethi, Gurpriya; Vats, Naalti; Pecina, Marta; Welsh, Robert C.; Starkman, Monica N.; Giordani, Bruno; Langenecker, Scott A.

2016-01-01

Introduction List learning tasks are powerful clinical tools for studying memory, yet have been relatively underutilized within the functional imaging literature. This limits understanding of regions such as the Papez circuit which support memory performance in healthy, non-demented adults. Method The current study characterized list learning performance in 40 adults who completed a Semantic List Learning Task (SLLT) with a Brown-Peterson manipulation during functional MRI (fMRI). Cued recall with semantic cues, and recognition memory were assessed after imaging. Internal reliability and convergent and discriminant validity were evaluated. Results Subjects averaged 38% accuracy in recall (62% for recognition), with primacy but no recency effects observed. Validity and reliability were demonstrated by showing that the SLLT was correlated with the California Verbal Learning test (CVLT), but not with executive functioning tests, and high intraclass correlation coefficient across lists for recall (.91). fMRI measurements during Encoding (vs. Silent Rehearsal) revealed significant activation in bilateral hippocampus, parahippocampus, and bilateral anterior and posterior cingulate cortex. Post-hoc analyses showed increased activation in anterior and middle hippocampus, subgenual cingulate, and mammillary bodies specific to Encoding. In addition, increasing age was positively associated with increased activation in a diffuse network, particularly frontal cortex and specific Papez regions for correctly recalled words. Gender differences were specific to left inferior and superior frontal cortex. Conclusions This is a clinically relevant list learning task that can be used in studies of groups for which the Papez circuit is damaged or disrupted, in mixed or crossover studies at imaging and clinical sites. PMID:26313512

Pre- and postprandial variation in implicit attention to food images reflects appetite and sensory-specific satiety.

PubMed

Davidson, Graeme R; Giesbrecht, Timo; Thomas, Anna M; Kirkham, Tim C

2018-06-01

Implicit attentional processes are biased toward food-related stimuli, with the extent of that bias reflecting relative motivation to eat. These interactions have typically been investigated by comparisons between fasted and sated individuals. In this study, temporal changes in implicit attention to food were assessed in relation to natural, spontaneous changes in appetite occurring before and after an anticipated midday meal. Non-fasted adults performed an emotional blink of attention (EBA) task at intervals, before and after consuming preferred, pre-selected sandwiches to satiety. Participants were required to detect targets within a rapid visual stream, presented after task-irrelevant food (preferred or non-preferred sandwiches, or desserts) or non-food distractor images. All categories of food distractor preferentially captured attention even when appetite levels were low, but became more distracting as appetite increased preprandially, reducing task accuracy maximally as hunger peaked before lunch. Postprandially, attentional capture was markedly reduced for images of the specific sandwich type consumed and, to a lesser extent, for images of other sandwich types that had not been eaten. Attentional capture by images of desserts was unaffected by satiation. These findings support an important role of selective visual attention in the guidance of motivated behaviour. Naturalistic, meal-related changes in appetite are accompanied by changes in implicit attention to visual food stimuli that are easily detected using the EBA paradigm. Preprandial enhancement of attention capture by food cues likely reflects increases in the incentive motivational value of all food stimuli, perhaps providing an implicit index of wanting. Postprandial EBA responses confirm that satiation on a particular food results in relative inattention to that food, supporting an important attentional component in the operation of sensory-specific satiety. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Visual mismatch negativity indicates automatic, task-independent detection of artistic image composition in abstract artworks.

PubMed

Menzel, Claudia; Kovács, Gyula; Amado, Catarina; Hayn-Leichsenring, Gregor U; Redies, Christoph

2018-05-06

In complex abstract art, image composition (i.e., the artist's deliberate arrangement of pictorial elements) is an important aesthetic feature. We investigated whether the human brain detects image composition in abstract artworks automatically (i.e., independently of the experimental task). To this aim, we studied whether a group of 20 original artworks elicited a visual mismatch negativity when contrasted with a group of 20 images that were composed of the same pictorial elements as the originals, but in shuffled arrangements, which destroy artistic composition. We used a passive oddball paradigm with parallel electroencephalogram recordings to investigate the detection of image type-specific properties. We observed significant deviant-standard differences for the shuffled and original images, respectively. Furthermore, for both types of images, differences in amplitudes correlated with the behavioral ratings of the images. In conclusion, we show that the human brain can detect composition-related image properties in visual artworks in an automatic fashion. Copyright © 2018 Elsevier B.V. All rights reserved.

Noise properties and task-based evaluation of diffraction-enhanced imaging

PubMed Central

Brankov, Jovan G.; Saiz-Herranz, Alejandro; Wernick, Miles N.

2014-01-01

Abstract. Diffraction-enhanced imaging (DEI) is an emerging x-ray imaging method that simultaneously yields x-ray attenuation and refraction images and holds great promise for soft-tissue imaging. The DEI has been mainly studied using synchrotron sources, but efforts have been made to transition the technology to more practical implementations using conventional x-ray sources. The main technical challenge of this transition lies in the relatively lower x-ray flux obtained from conventional sources, leading to photon-limited data contaminated by Poisson noise. Several issues that must be understood in order to design and optimize DEI imaging systems with respect to noise performance are addressed. Specifically, we: (a) develop equations describing the noise properties of DEI images, (b) derive the conditions under which the DEI algorithm is statistically optimal, (c) characterize the imaging performance that can be obtained as measured by task-based metrics, and (d) consider image-processing steps that may be employed to mitigate noise effects. PMID:26158056

Towards A Complete Model Of Photopic Visual Threshold Performance

NASA Astrophysics Data System (ADS)

Overington, I.

1982-02-01

Based on a wide variety of fragmentary evidence taken from psycho-physics, neurophysiology and electron microscopy, it has been possible to put together a very widely applicable conceptual model of photopic visual threshold performance. Such a model is so complex that a single comprehensive mathematical version is excessively cumbersome. It is, however, possible to set up a suite of related mathematical models, each of limited application but strictly known envelope of usage. Such models may be used for assessment of a variety of facets of visual performance when using display imagery, including effects and interactions of image quality, random and discrete display noise, viewing distance, image motion, etc., both for foveal interrogation tasks and for visual search tasks. The specific model may be selected from the suite according to the assessment task in hand. The paper discusses in some depth the major facets of preperceptual visual processing and their interaction with instrumental image quality and noise. It then highlights the statistical nature of visual performance before going on to consider a number of specific mathematical models of partial visual function. Where appropriate, these are compared with widely popular empirical models of visual function.

Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

PubMed

Diamant, Idit; Klang, Eyal; Amitai, Michal; Konen, Eli; Goldberger, Jacob; Greenspan, Hayit

2017-06-01

We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

Frontal–Occipital Connectivity During Visual Search

PubMed Central

Pantazatos, Spiro P.; Yanagihara, Ted K.; Zhang, Xian; Meitzler, Thomas

2012-01-01

Abstract Although expectation- and attention-related interactions between ventral and medial prefrontal cortex and stimulus category-selective visual regions have been identified during visual detection and discrimination, it is not known if similar neural mechanisms apply to other tasks such as visual search. The current work tested the hypothesis that high-level frontal regions, previously implicated in expectation and visual imagery of object categories, interact with visual regions associated with object recognition during visual search. Using functional magnetic resonance imaging, subjects searched for a specific object that varied in size and location within a complex natural scene. A model-free, spatial-independent component analysis isolated multiple task-related components, one of which included visual cortex, as well as a cluster within ventromedial prefrontal cortex (vmPFC), consistent with the engagement of both top-down and bottom-up processes. Analyses of psychophysiological interactions showed increased functional connectivity between vmPFC and object-sensitive lateral occipital cortex (LOC), and results from dynamic causal modeling and Bayesian Model Selection suggested bidirectional connections between vmPFC and LOC that were positively modulated by the task. Using image-guided diffusion-tensor imaging, functionally seeded, probabilistic white-matter tracts between vmPFC and LOC, which presumably underlie this effective interconnectivity, were also observed. These connectivity findings extend previous models of visual search processes to include specific frontal–occipital neuronal interactions during a natural and complex search task. PMID:22708993

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

NASA Astrophysics Data System (ADS)

Gang, Grace J.; Siewerdsen, Jeffrey H.; Webster Stayman, J.

2017-06-01

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)—each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d‧) of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d‧ of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Abnormal functional lateralization and activity of language brain areas in typical specific language impairment (developmental dysphasia)

PubMed Central

De Guibert, Clément; Maumet, Camille; Jannin, Pierre; Ferré, Jean-Christophe; Tréguier, Catherine; Barillot, Christian; Le Rumeur, Elisabeth; Allaire, Catherine; Biraben, Arnaud

2011-01-01

Atypical functional lateralization and specialization for language have been proposed to account for developmental language disorders, yet results from functional neuroimaging studies are sparse and inconsistent. This functional magnetic resonance imaging study compared children with a specific subtype of specific language impairment affecting structural language (n=21), to a matched group of typically-developing children using a panel of four language tasks neither requiring reading nor metalinguistic skills, including two auditory lexico-semantic tasks (category fluency and responsive naming) and two visual phonological tasks based on picture naming. Data processing involved normalizing the data with respect to a matched pairs pediatric template, groups and between-groups analysis, and laterality indexes assessment within regions of interest using single and combined task analysis. Children with specific language impairment exhibited a significant lack of left lateralization in all core language regions (inferior frontal gyrus-opercularis, inferior frontal gyrus-triangularis, supramarginal gyrus, superior temporal gyrus), across single or combined task analysis, but no difference of lateralization for the rest of the brain. Between-group comparisons revealed a left hypoactivation of Wernicke’s area at the posterior superior temporal/supramarginal junction during the responsive naming task, and a right hyperactivation encompassing the anterior insula with adjacent inferior frontal gyrus and the head of the caudate nucleus during the first phonological task. This study thus provides evidence that this specific subtype of specific language impairment is associated with atypical lateralization and functioning of core language areas. PMID:21719430

DQE and system optimization for indirect-detection flat-panel imagers in diagnostic radiology

NASA Astrophysics Data System (ADS)

Siewerdsen, Jeffrey H.; Antonuk, Larry E.

1998-07-01

The performance of indirect-detection flat-panel imagers incorporating CsI:Tl x-ray converters is examined through calculation of the detective quantum efficiency (DQE) under conditions of chest radiography, fluoroscopy, and mammography. Calculations are based upon a cascaded systems model which has demonstrated excellent agreement with empirical signal, noise- power spectra, and DQE results. For each application, the DQE is calculated as a function of spatial-frequency and CsI:Tl thickness. A preliminary investigation into the optimization of flat-panel imaging systems is described, wherein the x-ray converter thickness which provides optimal DQE for a given imaging task is estimated. For each application, a number of example tasks involving detection of an object of variable size and contrast against a noisy background are considered. The method described is fairly general and can be extended to account for a variety of imaging tasks. For the specific examples considered, the preliminary results estimate optimal CsI:Tl thicknesses of approximately 450 micrometer (approximately 200 mg/cm2), approximately 320 micrometer (approximately 140 mg/cm2), and approximately 200 micrometer (approximately 90 mg/cm2) for chest radiography, fluoroscopy, and mammography, respectively. These results are expected to depend upon the imaging task as well as upon the quality of available CsI:Tl, and future improvements in scintillator fabrication could result in increased optimal thickness and DQE.

Quality Control by Artificial Vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lam, Edmond Y.; Gleason, Shaun Scott; Niel, Kurt S.

2010-01-01

Computational technology has fundamentally changed many aspects of our lives. One clear evidence is the development of artificial-vision systems, which have effectively automated many manual tasks ranging from quality inspection to quantitative assessment. In many cases, these machine-vision systems are even preferred over manual ones due to their repeatability and high precision. Such advantages come from significant research efforts in advancing sensor technology, illumination, computational hardware, and image-processing algorithms. Similar to the Special Section on Quality Control by Artificial Vision published two years ago in Volume 17, Issue 3 of the Journal of Electronic Imaging, the present one invited papersmore » relevant to fundamental technology improvements to foster quality control by artificial vision, and fine-tuned the technology for specific applications. We aim to balance both theoretical and applied work pertinent to this special section theme. Consequently, we have seven high-quality papers resulting from the stringent peer-reviewing process in place at the Journal of Electronic Imaging. Some of the papers contain extended treatment of the authors work presented at the SPIE Image Processing: Machine Vision Applications conference and the International Conference on Quality Control by Artificial Vision. On the broad application side, Liu et al. propose an unsupervised texture image segmentation scheme. Using a multilayer data condensation spectral clustering algorithm together with wavelet transform, they demonstrate the effectiveness of their approach on both texture and synthetic aperture radar images. A problem related to image segmentation is image extraction. For this, O'Leary et al. investigate the theory of polynomial moments and show how these moments can be compared to classical filters. They also show how to use the discrete polynomial-basis functions for the extraction of 3-D embossed digits, demonstrating superiority over Fourier-basis functions for this task. Image registration is another important task for machine vision. Bingham and Arrowood investigate the implementation and results in applying Fourier phase matching for projection registration, with a particular focus on nondestructive testing using computed tomography. Readers interested in enriching their arsenal of image-processing algorithms for machine-vision tasks should find these papers enriching. Meanwhile, we have four papers dealing with more specific machine-vision tasks. The first one, Yahiaoui et al., is quantitative in nature, using machine vision for real-time passenger counting. Occulsion is a common problem in counting objects and people, and they circumvent this issue with a dense stereovision system, achieving 97 to 99% accuracy in their tests. On the other hand, the second paper by Oswald-Tranta et al. focuses on thermographic crack detection. An infrared camera is used to detect inhomogeneities, which may indicate surface cracks. They describe the various steps in developing fully automated testing equipment aimed at a high throughput. Another paper describing an inspection system is Molleda et al., which handles flatness inspection of rolled products. They employ optical-laser triangulation and 3-D surface reconstruction for this task, showing how these can be achieved in real time. Last but not least, Presles et al. propose a way to monitor the particle-size distribution of batch crystallization processes. This is achieved through a new in situ imaging probe and image-analysis methods. While it is unlikely any reader may be working on these four specific problems at the same time, we are confident that readers will find these papers inspiring and potentially helpful to their own machine-vision system developments.« less

Bag-of-visual-ngrams for histopathology image classification

NASA Astrophysics Data System (ADS)

López-Monroy, A. Pastor; Montes-y-Gómez, Manuel; Escalante, Hugo Jair; Cruz-Roa, Angel; González, Fabio A.

2013-11-01

This paper describes an extension of the Bag-of-Visual-Words (BoVW) representation for image categorization (IC) of histophatology images. This representation is one of the most used approaches in several high-level computer vision tasks. However, the BoVW representation has an important limitation: the disregarding of spatial information among visual words. This information may be useful to capture discriminative visual-patterns in specific computer vision tasks. In order to overcome this problem we propose the use of visual n-grams. N-grams based-representations are very popular in the field of natural language processing (NLP), in particular within text mining and information retrieval. We propose building a codebook of n-grams and then representing images by histograms of visual n-grams. We evaluate our proposal in the challenging task of classifying histopathology images. The novelty of our proposal lies in the fact that we use n-grams as attributes for a classification model (together with visual-words, i.e., 1-grams). This is common practice within NLP, although, to the best of our knowledge, this idea has not been explored yet within computer vision. We report experimental results in a database of histopathology images where our proposed method outperforms the traditional BoVWs formulation.

Segmentation of radiologic images with self-organizing maps: the segmentation problem transformed into a classification task

NASA Astrophysics Data System (ADS)

Pelikan, Erich; Vogelsang, Frank; Tolxdorff, Thomas

1996-04-01

The texture-based segmentation of x-ray images of focal bone lesions using topological maps is introduced. Texture characteristics are described by image-point correlation of feature images to feature vectors. For the segmentation, the topological map is labeled using an improved labeling strategy. Results of the technique are demonstrated on original and synthetic x-ray images and quantified with the aid of quality measures. In addition, a classifier-specific contribution analysis is applied for assessing the feature space.

MIA - A free and open source software for gray scale medical image analysis

PubMed Central

2013-01-01

Background Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large. Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers. One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development. Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don’t provide an clear approach when one wants to shape a new command line tool from a prototype shell script. Results The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code. Conclusion In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed. PMID:24119305

MIA - A free and open source software for gray scale medical image analysis.

PubMed

Wollny, Gert; Kellman, Peter; Ledesma-Carbayo, María-Jesus; Skinner, Matthew M; Hublin, Jean-Jaques; Hierl, Thomas

2013-10-11

Gray scale images make the bulk of data in bio-medical image analysis, and hence, the main focus of many image processing tasks lies in the processing of these monochrome images. With ever improving acquisition devices, spatial and temporal image resolution increases, and data sets become very large.Various image processing frameworks exists that make the development of new algorithms easy by using high level programming languages or visual programming. These frameworks are also accessable to researchers that have no background or little in software development because they take care of otherwise complex tasks. Specifically, the management of working memory is taken care of automatically, usually at the price of requiring more it. As a result, processing large data sets with these tools becomes increasingly difficult on work station class computers.One alternative to using these high level processing tools is the development of new algorithms in a languages like C++, that gives the developer full control over how memory is handled, but the resulting workflow for the prototyping of new algorithms is rather time intensive, and also not appropriate for a researcher with little or no knowledge in software development.Another alternative is in using command line tools that run image processing tasks, use the hard disk to store intermediate results, and provide automation by using shell scripts. Although not as convenient as, e.g. visual programming, this approach is still accessable to researchers without a background in computer science. However, only few tools exist that provide this kind of processing interface, they are usually quite task specific, and don't provide an clear approach when one wants to shape a new command line tool from a prototype shell script. The proposed framework, MIA, provides a combination of command line tools, plug-ins, and libraries that make it possible to run image processing tasks interactively in a command shell and to prototype by using the according shell scripting language. Since the hard disk becomes the temporal storage memory management is usually a non-issue in the prototyping phase. By using string-based descriptions for filters, optimizers, and the likes, the transition from shell scripts to full fledged programs implemented in C++ is also made easy. In addition, its design based on atomic plug-ins and single tasks command line tools makes it easy to extend MIA, usually without the requirement to touch or recompile existing code. In this article, we describe the general design of MIA, a general purpouse framework for gray scale image processing. We demonstrated the applicability of the software with example applications from three different research scenarios, namely motion compensation in myocardial perfusion imaging, the processing of high resolution image data that arises in virtual anthropology, and retrospective analysis of treatment outcome in orthognathic surgery. With MIA prototyping algorithms by using shell scripts that combine small, single-task command line tools is a viable alternative to the use of high level languages, an approach that is especially useful when large data sets need to be processed.

Research Priorities in Limb and Task-Specific Dystonias.

PubMed

Pirio Richardson, Sarah; Altenmüller, Eckart; Alter, Katharine; Alterman, Ron L; Chen, Robert; Frucht, Steven; Furuya, Shinichi; Jankovic, Joseph; Jinnah, H A; Kimberley, Teresa J; Lungu, Codrin; Perlmutter, Joel S; Prudente, Cecília N; Hallett, Mark

2017-01-01

Dystonia, which causes intermittent or sustained abnormal postures and movements, can present in a focal or a generalized manner. In the limbs, focal dystonia can occur in either the upper or lower limbs and may be task-specific causing abnormal motor performance for only a specific task, such as in writer's cramp, runner's dystonia, or musician's dystonia. Focal limb dystonia can be non-task-specific and may, in some circumstances, be associated with parkinsonian disorders. The true prevalence of focal limb dystonia is not known and is likely currently underestimated, leaving a knowledge gap and an opportunity for future research. The pathophysiology of focal limb dystonia shares some commonalities with other dystonias with a loss of inhibition in the central nervous system and a loss of the normal regulation of plasticity, called homeostatic plasticity. Functional imaging studies revealed abnormalities in several anatomical networks that involve the cortex, basal ganglia, and cerebellum. Further studies should focus on distinguishing cause from effect in both physiology and imaging studies to permit focus on most relevant biological correlates of dystonia. There is no specific therapy for the treatment of limb dystonia given the variability in presentation, but off-label botulinum toxin therapy is often applied to focal limb and task-specific dystonia. Various rehabilitation techniques have been applied and rehabilitation interventions may improve outcomes, but small sample size and lack of direct comparisons between methods to evaluate comparative efficacy limit conclusions. Finally, non-invasive and invasive therapeutic modalities have been explored in small studies with design limitations that do not yet clearly provide direction for larger clinical trials that could support new clinical therapies. Given these gaps in our clinical, pathophysiologic, and therapeutic knowledge, we have identified priorities for future research including: the development of diagnostic criteria for limb dystonia, more precise phenotypic characterization and innovative clinical trial design that considers clinical heterogeneity, and limited available number of participants.

Research Priorities in Limb and Task-Specific Dystonias

PubMed Central

Pirio Richardson, Sarah; Altenmüller, Eckart; Alter, Katharine; Alterman, Ron L.; Chen, Robert; Frucht, Steven; Furuya, Shinichi; Jankovic, Joseph; Jinnah, H. A.; Kimberley, Teresa J.; Lungu, Codrin; Perlmutter, Joel S.; Prudente, Cecília N.; Hallett, Mark

2017-01-01

Dystonia, which causes intermittent or sustained abnormal postures and movements, can present in a focal or a generalized manner. In the limbs, focal dystonia can occur in either the upper or lower limbs and may be task-specific causing abnormal motor performance for only a specific task, such as in writer’s cramp, runner’s dystonia, or musician’s dystonia. Focal limb dystonia can be non-task-specific and may, in some circumstances, be associated with parkinsonian disorders. The true prevalence of focal limb dystonia is not known and is likely currently underestimated, leaving a knowledge gap and an opportunity for future research. The pathophysiology of focal limb dystonia shares some commonalities with other dystonias with a loss of inhibition in the central nervous system and a loss of the normal regulation of plasticity, called homeostatic plasticity. Functional imaging studies revealed abnormalities in several anatomical networks that involve the cortex, basal ganglia, and cerebellum. Further studies should focus on distinguishing cause from effect in both physiology and imaging studies to permit focus on most relevant biological correlates of dystonia. There is no specific therapy for the treatment of limb dystonia given the variability in presentation, but off-label botulinum toxin therapy is often applied to focal limb and task-specific dystonia. Various rehabilitation techniques have been applied and rehabilitation interventions may improve outcomes, but small sample size and lack of direct comparisons between methods to evaluate comparative efficacy limit conclusions. Finally, non-invasive and invasive therapeutic modalities have been explored in small studies with design limitations that do not yet clearly provide direction for larger clinical trials that could support new clinical therapies. Given these gaps in our clinical, pathophysiologic, and therapeutic knowledge, we have identified priorities for future research including: the development of diagnostic criteria for limb dystonia, more precise phenotypic characterization and innovative clinical trial design that considers clinical heterogeneity, and limited available number of participants. PMID:28515706

Application development environment for advanced digital workstations

NASA Astrophysics Data System (ADS)

Valentino, Daniel J.; Harreld, Michael R.; Liu, Brent J.; Brown, Matthew S.; Huang, Lu J.

1998-06-01

One remaining barrier to the clinical acceptance of electronic imaging and information systems is the difficulty in providing intuitive access to the information needed for a specific clinical task (such as reaching a diagnosis or tracking clinical progress). The purpose of this research was to create a development environment that enables the design and implementation of advanced digital imaging workstations. We used formal data and process modeling to identify the diagnostic and quantitative data that radiologists use and the tasks that they typically perform to make clinical decisions. We studied a diverse range of radiology applications, including diagnostic neuroradiology in an academic medical center, pediatric radiology in a children's hospital, screening mammography in a breast cancer center, and thoracic radiology consultation for an oncology clinic. We used object- oriented analysis to develop software toolkits that enable a programmer to rapidly implement applications that closely match clinical tasks. The toolkits support browsing patient information, integrating patient images and reports, manipulating images, and making quantitative measurements on images. Collectively, we refer to these toolkits as the UCLA Digital ViewBox toolkit (ViewBox/Tk). We used the ViewBox/Tk to rapidly prototype and develop a number of diverse medical imaging applications. Our task-based toolkit approach enabled rapid and iterative prototyping of workstations that matched clinical tasks. The toolkit functionality and performance provided a 'hands-on' feeling for manipulating images, and for accessing textual information and reports. The toolkits directly support a new concept for protocol based-reading of diagnostic studies. The design supports the implementation of network-based application services (e.g., prefetching, workflow management, and post-processing) that will facilitate the development of future clinical applications.

Partially converted stereoscopic images and the effects on visual attention and memory

NASA Astrophysics Data System (ADS)

Kim, Sanghyun; Morikawa, Hiroyuki; Mitsuya, Reiko; Kawai, Takashi; Watanabe, Katsumi

2015-03-01

This study contained two experimental examinations of the cognitive activities such as visual attention and memory in viewing stereoscopic (3D) images. For this study, partially converted 3D images were used with binocular parallax added to a specific region of the image. In Experiment 1, change blindness was used as a presented stimulus. The visual attention and impact on memory were investigated by measuring the response time to accomplish the given task. In the change blindness task, an 80 ms blank was intersected between the original and altered images, and the two images were presented alternatingly for 240 ms each. Subjects were asked to temporarily memorize the two switching images and to compare them, visually recognizing the difference between the two. The stimuli for four conditions (2D, 3D, Partially converted 3D, distracted partially converted 3D) were randomly displayed for 20 subjects. The results of Experiment 1 showed that partially converted 3D images tend to attract visual attention and are prone to remain in viewer's memory in the area where moderate negative parallax has been added. In order to examine the impact of a dynamic binocular disparity on partially converted 3D images, an evaluation experiment was conducted that applied learning, distraction, and recognition tasks for 33 subjects. The learning task involved memorizing the location of cells in a 5 × 5 matrix pattern using two different colors. Two cells were positioned with alternating colors, and one of the gray cells was moved up, down, left, or right by one cell width. Experimental conditions was set as a partially converted 3D condition in which a gray cell moved diagonally for a certain period of time with a dynamic binocular disparity added, a 3D condition in which binocular disparity was added to all gray cells, and a 2D condition. The correct response rates for recognition of each task after the distraction task were compared. The results of Experiment 2 showed that the correct response rate in the partial 3D condition was significantly higher with the recognition task than in the other conditions. These results showed that partially converted 3D images tended to have a visual attraction and affect viewer's memory.

[Seeking the aetiology of autistic spectrum disorder. Part 2: Functional neuroimaging].

PubMed

Bryńska, Anita

2012-01-01

Multiple functional imaging techniques help to a better understanding of the neurobiological basis of autism-spectrum disorders (ASD). The early functional imaging studies on ASD focused on task-specific methods related to core symptom domains and explored patterns of activation in response to face processing, theory of mind tasks, language processing and executive function tasks. On the other hand, fMRI research in ASD focused on the development of functional connectivity methods and has provided evidence of alterations in cortical connectivity in ASD and establish autism as a disorder of under-connectivity among the brain regions participating in cortical networks. This atypical functional connectivity in ASD results in inefficiency and poor integration of processing in network connections to achieve task performance. The goal of this review is to summarise the actual neuroimaging functional data and examine their implication for understanding of the neurobiology of ASD.

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

Explaining the Timing of Natural Scene Understanding with a Computational Model of Perceptual Categorization

PubMed Central

Sofer, Imri; Crouzet, Sébastien M.; Serre, Thomas

2015-01-01

Observers can rapidly perform a variety of visual tasks such as categorizing a scene as open, as outdoor, or as a beach. Although we know that different tasks are typically associated with systematic differences in behavioral responses, to date, little is known about the underlying mechanisms. Here, we implemented a single integrated paradigm that links perceptual processes with categorization processes. Using a large image database of natural scenes, we trained machine-learning classifiers to derive quantitative measures of task-specific perceptual discriminability based on the distance between individual images and different categorization boundaries. We showed that the resulting discriminability measure accurately predicts variations in behavioral responses across categorization tasks and stimulus sets. We further used the model to design an experiment, which challenged previous interpretations of the so-called “superordinate advantage.” Overall, our study suggests that observed differences in behavioral responses across rapid categorization tasks reflect natural variations in perceptual discriminability. PMID:26335683

Architecture of distributed picture archiving and communication systems for storing and processing high resolution medical images

NASA Astrophysics Data System (ADS)

Tokareva, Victoria

2018-04-01

New generation medicine demands a better quality of analysis increasing the amount of data collected during checkups, and simultaneously decreasing the invasiveness of a procedure. Thus it becomes urgent not only to develop advanced modern hardware, but also to implement special software infrastructure for using it in everyday clinical practice, so-called Picture Archiving and Communication Systems (PACS). Developing distributed PACS is a challenging task for nowadays medical informatics. The paper discusses the architecture of distributed PACS server for processing large high-quality medical images, with respect to technical specifications of modern medical imaging hardware, as well as international standards in medical imaging software. The MapReduce paradigm is proposed for image reconstruction by server, and the details of utilizing the Hadoop framework for this task are being discussed in order to provide the design of distributed PACS as ergonomic and adapted to the needs of end users as possible.

Semantic Segmentation and Unregistered Building Detection from Uav Images Using a Deconvolutional Network

NASA Astrophysics Data System (ADS)

Ham, S.; Oh, Y.; Choi, K.; Lee, I.

2018-05-01

Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.

Gender differences in the motivational processing of facial beauty☆

PubMed Central

Levy, Boaz; Ariely, Dan; Mazar, Nina; Chi, Won; Lukas, Scott; Elman, Igor

2013-01-01

Gender may be involved in the motivational processing of facial beauty. This study applied a behavioral probe, known to activate brain motivational regions, to healthy heterosexual subjects. Matched samples of men and women were administered two tasks: (a) key pressing to change the viewing time of average or beautiful female or male facial images, and (b) rating the attractiveness of these images. Men expended more effort (via the key-press task) to extend the viewing time of the beautiful female faces. Women displayed similarly increased effort for beautiful male and female images, but the magnitude of this effort was substantially lower than that of men for beautiful females. Heterosexual facial attractiveness ratings were comparable in both groups. These findings demonstrate heterosexual specificity of facial motivational targets for men, but not for women. Moreover, heightened drive for the pursuit of heterosexual beauty in the face of regular valuational assessments, displayed by men, suggests a gender-specific incentive sensitization phenomenon. PMID:24282336

Examining a supramodal network for conflict processing: a systematic review and novel functional magnetic resonance imaging data for related visual and auditory stroop tasks.

PubMed

Roberts, Katherine L; Hall, Deborah A

2008-06-01

Cognitive control over conflicting information has been studied extensively using tasks such as the color-word Stroop, flanker, and spatial conflict task. Neuroimaging studies typically identify a fronto-parietal network engaged in conflict processing, but numerous additional regions are also reported. Ascribing putative functional roles to these regions is problematic because some may have less to do with conflict processing per se, but could be engaged in specific processes related to the chosen stimulus modality, stimulus feature, or type of conflict task. In addition, some studies contrast activation on incongruent and congruent trials, even though a neutral baseline is needed to separate the effect of inhibition from that of facilitation. In the first part of this article, we report a systematic review of 34 neuroimaging publications, which reveals that conflict-related activity is reliably reported in the anterior cingulate cortex and bilaterally in the lateral prefrontal cortex, the anterior insula, and the parietal lobe. In the second part, we further explore these candidate "conflict" regions through a novel functional magnetic resonance imaging experiment, in which the same group of subjects perform related visual and auditory Stroop tasks. By carefully controlling for the same task (Stroop), the same to-be-ignored stimulus dimension (word meaning), and by separating out inhibitory processes from those of facilitation, we attempt to minimize the potential differences between the two tasks. The results provide converging evidence that the regions identified by the systematic review are reliably engaged in conflict processing. Despite carefully matching the Stroop tasks, some regions of differential activity remained, particularly in the parietal cortex. We discuss some of the task-specific processes which might account for this finding.

Intensity-Based Registration for Lung Motion Estimation

NASA Astrophysics Data System (ADS)

Cao, Kunlin; Ding, Kai; Amelon, Ryan E.; Du, Kaifang; Reinhardt, Joseph M.; Raghavan, Madhavan L.; Christensen, Gary E.

Image registration plays an important role within pulmonary image analysis. The task of registration is to find the spatial mapping that brings two images into alignment. Registration algorithms designed for matching 4D lung scans or two 3D scans acquired at different inflation levels can catch the temporal changes in position and shape of the region of interest. Accurate registration is critical to post-analysis of lung mechanics and motion estimation. In this chapter, we discuss lung-specific adaptations of intensity-based registration methods for 3D/4D lung images and review approaches for assessing registration accuracy. Then we introduce methods for estimating tissue motion and studying lung mechanics. Finally, we discuss methods for assessing and quantifying specific volume change, specific ventilation, strain/ stretch information and lobar sliding.

XML-based scripting of multimodality image presentations in multidisciplinary clinical conferences

NASA Astrophysics Data System (ADS)

Ratib, Osman M.; Allada, Vivekanand; Dahlbom, Magdalena; Marcus, Phillip; Fine, Ian; Lapstra, Lorelle

2002-05-01

We developed a multi-modality image presentation software for display and analysis of images and related data from different imaging modalities. The software is part of a cardiac image review and presentation platform that supports integration of digital images and data from digital and analog media such as videotapes, analog x-ray films and 35 mm cine films. The software supports standard DICOM image files as well as AVI and PDF data formats. The system is integrated in a digital conferencing room that includes projections of digital and analog sources, remote videoconferencing capabilities, and an electronic whiteboard. The goal of this pilot project is to: 1) develop a new paradigm for image and data management for presentation in a clinically meaningful sequence adapted to case-specific scenarios, 2) design and implement a multi-modality review and conferencing workstation using component technology and customizable 'plug-in' architecture to support complex review and diagnostic tasks applicable to all cardiac imaging modalities and 3) develop an XML-based scripting model of image and data presentation for clinical review and decision making during routine clinical tasks and multidisciplinary clinical conferences.

Prefrontal vulnerabilities and whole brain connectivity in aging and depression.

PubMed

Lamar, Melissa; Charlton, Rebecca A; Ajilore, Olusola; Zhang, Aifeng; Yang, Shaolin; Barrick, Thomas R; Rhodes, Emma; Kumar, Anand

2013-07-01

Studies exploring the underpinnings of age-related neurodegeneration suggest fronto-limbic alterations that are increasingly vulnerable in the presence of disease including late life depression. Less work has assessed the impact of this specific vulnerability on widespread brain circuitry. Seventy-nine older adults (healthy controls=45; late life depression=34) completed translational tasks shown in non-human primates to rely on fronto-limbic networks involving dorsolateral (Self-Ordered Pointing Task) or orbitofrontal (Object Alternation Task) cortices. A sub-sample of participants also completed diffusion tensor imaging for white matter tract quantification (uncinate and cingulum bundle; n=58) and whole brain tract-based spatial statistics (n=62). Despite task associations to specific white matter tracts across both groups, only healthy controls demonstrated significant correlations between widespread tract integrity and cognition. Thus, increasing Object Alternation Task errors were associated with decreasing fractional anisotropy in the uncinate in late life depression; however, only in healthy controls was the uncinate incorporated into a larger network of white matter vulnerability associating fractional anisotropy with Object Alternation Task errors using whole brain tract-based spatial statistics. It appears that the whole brain impact of specific fronto-limbic vulnerabilities in aging may be eclipsed in the presence of disease-specific neuropathology like that seen in late life depression. Copyright © 2013 Elsevier Ltd. All rights reserved.

The role of body image and self-perception in anorexia nervosa: the neuroimaging perspective.

PubMed

Esposito, Roberto; Cieri, Filippo; di Giannantonio, Massimo; Tartaro, Armando

2018-03-01

Anorexia nervosa is a severe psychiatric illness characterized by intense fear of gaining weight, relentless pursuit of thinness, deep concerns about food and a pervasive disturbance of body image. Functional magnetic resonance imaging tries to shed light on the neurobiological underpinnings of anorexia nervosa. This review aims to evaluate the empirical neuroimaging literature about self-perception in anorexia nervosa. This narrative review summarizes a number of task-based and resting-state functional magnetic resonance imaging studies in anorexia nervosa about body image and self-perception. The articles listed in references were searched using electronic databases (PubMed and Google Scholar) from 1990 to February 2016 using specific key words. All studies were reviewed with regard to their quality and eligibility for the review. Differences in brain activity were observed using body image perception and body size estimation tasks showing significant modifications in activity of specific brain areas (extrastriate body area, fusiform body area, inferior parietal lobule). Recent studies highlighted the role of emotions and self-perception in anorexia nervosa and their neural substrate involving resting-state networks and particularly frontal and posterior midline cortical structures within default mode network and insula. These findings open new horizons to understand the neural substrate of anorexia nervosa. © 2016 The British Psychological Society.

Parcellating the neuroanatomical basis of impaired decision-making in traumatic brain injury.

PubMed

Newcombe, Virginia F J; Outtrim, Joanne G; Chatfield, Doris A; Manktelow, Anne; Hutchinson, Peter J; Coles, Jonathan P; Williams, Guy B; Sahakian, Barbara J; Menon, David K

2011-03-01

Cognitive dysfunction is a devastating consequence of traumatic brain injury that affects the majority of those who survive with moderate-to-severe injury, and many patients with mild head injury. Disruption of key monoaminergic neurotransmitter systems, such as the dopaminergic system, may play a key role in the widespread cognitive dysfunction seen after traumatic axonal injury. Manifestations of injury to this system may include impaired decision-making and impulsivity. We used the Cambridge Gambling Task to characterize decision-making and risk-taking behaviour, outside of a learning context, in a cohort of 44 patients at least six months post-traumatic brain injury. These patients were found to have broadly intact processing of risk adjustment and probability judgement, and to bet similar amounts to controls. However, a patient preference for consistently early bets indicated a higher level of impulsiveness. These behavioural measures were compared with imaging findings on diffusion tensor magnetic resonance imaging. Performance in specific domains of the Cambridge Gambling Task correlated inversely and specifically with the severity of diffusion tensor imaging abnormalities in regions that have been implicated in these cognitive processes. Thus, impulsivity was associated with increased apparent diffusion coefficient bilaterally in the orbitofrontal gyrus, insula and caudate; abnormal risk adjustment with increased apparent diffusion coefficient in the right thalamus and dorsal striatum and left caudate; and impaired performance on rational choice with increased apparent diffusion coefficient in the bilateral dorsolateral prefrontal cortices, and the superior frontal gyri, right ventrolateral prefrontal cortex, the dorsal and ventral striatum, and left hippocampus. Importantly, performance in specific cognitive domains of the task did not correlate with diffusion tensor imaging abnormalities in areas not implicated in their performance. The ability to dissociate the location and extent of damage with performance on the various task components using diffusion tensor imaging allows important insights into the neuroanatomical basis of impulsivity following traumatic brain injury. The ability to detect such damage in vivo may have important implications for patient management, patient selection for trials, and to help understand complex neurocognitive pathways.

Parcellating the neuroanatomical basis of impaired decision-making in traumatic brain injury

PubMed Central

Outtrim, Joanne G.; Chatfield, Doris A.; Manktelow, Anne; Hutchinson, Peter J.; Coles, Jonathan P.; Williams, Guy B.; Sahakian, Barbara J.; Menon, David K.

2011-01-01

Cognitive dysfunction is a devastating consequence of traumatic brain injury that affects the majority of those who survive with moderate-to-severe injury, and many patients with mild head injury. Disruption of key monoaminergic neurotransmitter systems, such as the dopaminergic system, may play a key role in the widespread cognitive dysfunction seen after traumatic axonal injury. Manifestations of injury to this system may include impaired decision-making and impulsivity. We used the Cambridge Gambling Task to characterize decision-making and risk-taking behaviour, outside of a learning context, in a cohort of 44 patients at least six months post-traumatic brain injury. These patients were found to have broadly intact processing of risk adjustment and probability judgement, and to bet similar amounts to controls. However, a patient preference for consistently early bets indicated a higher level of impulsiveness. These behavioural measures were compared with imaging findings on diffusion tensor magnetic resonance imaging. Performance in specific domains of the Cambridge Gambling Task correlated inversely and specifically with the severity of diffusion tensor imaging abnormalities in regions that have been implicated in these cognitive processes. Thus, impulsivity was associated with increased apparent diffusion coefficient bilaterally in the orbitofrontal gyrus, insula and caudate; abnormal risk adjustment with increased apparent diffusion coefficient in the right thalamus and dorsal striatum and left caudate; and impaired performance on rational choice with increased apparent diffusion coefficient in the bilateral dorsolateral prefrontal cortices, and the superior frontal gyri, right ventrolateral prefrontal cortex, the dorsal and ventral striatum, and left hippocampus. Importantly, performance in specific cognitive domains of the task did not correlate with diffusion tensor imaging abnormalities in areas not implicated in their performance. The ability to dissociate the location and extent of damage with performance on the various task components using diffusion tensor imaging allows important insights into the neuroanatomical basis of impulsivity following traumatic brain injury. The ability to detect such damage in vivo may have important implications for patient management, patient selection for trials, and to help understand complex neurocognitive pathways. PMID:21310727

Targeted brain activation using an MR-compatible wrist torque measurement device and isometric motor tasks during functional magnetic resonance imaging.

PubMed

Vlaar, Martijn P; Mugge, Winfred; Groot, Paul F C; Sharifi, Sarvi; Bour, Lo J; van der Helm, Frans C T; van Rootselaar, Anne-Fleur; Schouten, Alfred C

2016-07-01

Dedicated pairs of isometric wrist flexion tasks, with and without visual feedback of the exerted torque, were designed to target activation of the CBL and BG in healthy subjects during functional magnetic resonance imaging (fMRI). Selective activation of the cerebellum (CBL) and basal ganglia (BG), often implicated in movement disorders such as tremor and dystonia, may help identify pathological changes and expedite diagnosis. A prototyped MR-compatible wrist torque measurement device, free of magnetic and conductive materials, allowed safe execution of tasks during fMRI without causing artifacts. A significant increase of activity in CBL and BG was found in healthy volunteers during a constant torque task with visual feedback compared to a constant torque task without visual feedback. This study shows that specific pairs of motor tasks using MR-compatible equipment at the wrist allow for targeted activation of CBL and BG, paving a new way for research into the pathophysiology of movement disorders. Copyright © 2016 Elsevier Inc. All rights reserved.

SPARX, a new environment for Cryo-EM image processing.

PubMed

Hohn, Michael; Tang, Grant; Goodyear, Grant; Baldwin, P R; Huang, Zhong; Penczek, Pawel A; Yang, Chao; Glaeser, Robert M; Adams, Paul D; Ludtke, Steven J

2007-01-01

SPARX (single particle analysis for resolution extension) is a new image processing environment with a particular emphasis on transmission electron microscopy (TEM) structure determination. It includes a graphical user interface that provides a complete graphical programming environment with a novel data/process-flow infrastructure, an extensive library of Python scripts that perform specific TEM-related computational tasks, and a core library of fundamental C++ image processing functions. In addition, SPARX relies on the EMAN2 library and cctbx, the open-source computational crystallography library from PHENIX. The design of the system is such that future inclusion of other image processing libraries is a straightforward task. The SPARX infrastructure intelligently handles retention of intermediate values, even those inside programming structures such as loops and function calls. SPARX and all dependencies are free for academic use and available with complete source.

Canine companionship is associated with modification of attentional bias in posttraumatic stress disorder.

PubMed

Woodward, Steven H; Jamison, Andrea L; Gala, Sasha; Holmes, Tyson H

2017-01-01

Attentional bias towards aversive stimuli has been demonstrated in the anxiety disorders and in posttraumatic stress disorder, and attentional bias modification has been proposed as a candidate treatment. This study rigorously assessed attentional bias towards aversive and pleasant visual imagery associated with the presence or absence of a familiar service canine in 23 veterans with chronic military-related posttraumatic stress disorder. Participants were repeatedly tested with and without their service canines present on two tasks designed to elicit spontaneous visual attention to facial and scenic image pairs, respectively. Each stimulus contrasted an emotive image with a neutral image. Via eye-tracking, the difference in visual attention directed to each image was analyzed as a function of the valence contrast and presence/absence of the canine. Across both tasks, the presence of a familiar service canine attenuated the normative attentional bias towards aversive image content. In the facial task, presence of the service canine specifically reduced attention toward angry faces. In that task, as well, accumulated days with the service canine similarly modulated attention toward facial emotion. The results suggest that the presence of a familiar service canine is associated with attenuation of attentional bias to aversive stimuli in chronic military-service-related posttraumatic stress disorder. Questions remain regarding the generalization of such effects to other populations, their dependence on the familiarity, breed, and training of the canine, and on social context.

Learning Trajectory for Transforming Teachers' Knowledge for Teaching Mathematics and Science with Digital Image and Video Technologies in an Online Learning Experience

ERIC Educational Resources Information Center

Niess, Margaret L.; Gillow-Wiles, Henry

2014-01-01

This qualitative cross-case study explores the influence of a designed learning trajectory on transforming teachers' technological pedagogical content knowledge (TPACK) for teaching with digital image and video technologies. The TPACK Learning Trajectory embeds tasks with specific instructional strategies within a social metacognitive…

Blackboard architecture for medical image interpretation

NASA Astrophysics Data System (ADS)

Davis, Darryl N.; Taylor, Christopher J.

1991-06-01

There is a growing interest in using sophisticated knowledge-based systems for biomedical image interpretation. We present a principled attempt to use artificial intelligence methodologies in interpreting lateral skull x-ray images. Such radiographs are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. Manual and interactive methods of analysis are known to be error prone and previous attempts to automate this analysis typically fail to capture the expertise and adaptability required to cope with the variability in biological structure and image quality. An integrated model-based system has been developed which makes use of a blackboard architecture and multiple knowledge sources. A model definition interface allows quantitative models, of feature appearance and location, to be built from examples as well as more qualitative modelling constructs. Visual task definition and blackboard control modules allow task-specific knowledge sources to act on information available to the blackboard in a hypothesise and test reasoning cycle. Further knowledge-based modules include object selection, location hypothesis, intelligent segmentation, and constraint propagation systems. Alternative solutions to given tasks are permitted.

How do we watch images? A case of change detection and quality estimation

NASA Astrophysics Data System (ADS)

Radun, Jenni; Leisti, Tuomas; Virtanen, Toni; Nyman, Göte

2012-01-01

The most common tasks in subjective image estimation are change detection (a detection task) and image quality estimation (a preference task). We examined how the task influences the gaze behavior when comparing detection and preference tasks. The eye movements of 16 naïve observers were recorded with 8 observers in both tasks. The setting was a flicker paradigm, where the observers see a non-manipulated image, a manipulated version of the image and again the non-manipulated image and estimate the difference they perceived in them. The material was photographic material with different image distortions and contents. To examine the spatial distribution of fixations, we defined the regions of interest using a memory task and calculated information entropy to estimate how concentrated the fixations were on the image plane. The quality task was faster and needed fewer fixations and the first eight fixations were more concentrated on certain image areas than the change detection task. The bottom-up influences of the image also caused more variation to the gaze behavior in the quality estimation task than in the change detection task The results show that the quality estimation is faster and the regions of interest are emphasized more on certain images compared with the change detection task that is a scan task where the whole image is always thoroughly examined. In conclusion, in subjective image estimation studies it is important to think about the task.

New head equivalent phantom for task and image performance evaluation representative for neurovascular procedures occurring in the Circle of Willis

NASA Astrophysics Data System (ADS)

Ionita, Ciprian N.; Loughran, Brendan; Jain, Amit; Swetadri Vasan, S. N.; Bednarek, Daniel R.; Levy, Elad; Siddiqui, Adnan H.; Snyder, Kenneth V.; Hopkins, L. N.; Rudin, Stephen

2012-03-01

Phantom equivalents of different human anatomical parts are routinely used for imaging system evaluation or dose calculations. The various recommendations on the generic phantom structure given by organizations such as the AAPM, are not always accurate when evaluating a very specific task. When we compared the AAPM head phantom containing 3 mm of aluminum to actual neuro-endovascular image guided interventions (neuro-EIGI) occurring in the Circle of Willis, we found that the system automatic exposure rate control (AERC) significantly underestimated the x-ray parameter selection. To build a more accurate phantom for neuro-EIGI, we reevaluated the amount of aluminum which must be included in the phantom. Human skulls were imaged at different angles, using various angiographic exposures, at kV's relevant to neuro-angiography. An aluminum step wedge was also imaged under identical conditions, and a correlation between the gray values of the imaged skulls and those of the aluminum step thicknesses was established. The average equivalent aluminum thickness for the skull samples for frontal projections in the Circle of Willis region was found to be about 13 mm. The results showed no significant changes in the average equivalent aluminum thickness with kV or mAs variation. When a uniform phantom using 13 mm aluminum and 15 cm acrylic was compared with an anthropomorphic head phantom the x-ray parameters selected by the AERC system were practically identical. These new findings indicate that for this specific task, the amount of aluminum included in the head equivalent must be increased substantially from 3 mm to a value of 13 mm.

Looking to Score: The Dissociation of Goal Influence on Eye Movement and Meta-Attentional Allocation in a Complex Dynamic Natural Scene

PubMed Central

Taya, Shuichiro; Windridge, David; Osman, Magda

2012-01-01

Several studies have reported that task instructions influence eye-movement behavior during static image observation. In contrast, during dynamic scene observation we show that while the specificity of the goal of a task influences observers’ beliefs about where they look, the goal does not in turn influence eye-movement patterns. In our study observers watched short video clips of a single tennis match and were asked to make subjective judgments about the allocation of visual attention to the items presented in the clip (e.g., ball, players, court lines, and umpire). However, before attending to the clips, observers were either told to simply watch clips (non-specific goal), or they were told to watch the clips with a view to judging which of the two tennis players was awarded the point (specific goal). The results of subjective reports suggest that observers believed that they allocated their attention more to goal-related items (e.g. court lines) if they performed the goal-specific task. However, we did not find the effect of goal specificity on major eye-movement parameters (i.e., saccadic amplitudes, inter-saccadic intervals, and gaze coherence). We conclude that the specificity of a task goal can alter observer’s beliefs about their attention allocation strategy, but such task-driven meta-attentional modulation does not necessarily correlate with eye-movement behavior. PMID:22768058

Strategies for medical data extraction and presentation part 2: creating a customizable context and user-specific patient reference database.

PubMed

Reiner, Bruce

2015-06-01

One of the greatest challenges facing healthcare professionals is the ability to directly and efficiently access relevant data from the patient's healthcare record at the point of care; specific to both the context of the task being performed and the specific needs and preferences of the individual end-user. In radiology practice, the relative inefficiency of imaging data organization and manual workflow requirements serves as an impediment to historical imaging data review. At the same time, clinical data retrieval is even more problematic due to the quality and quantity of data recorded at the time of order entry, along with the relative lack of information system integration. One approach to address these data deficiencies is to create a multi-disciplinary patient referenceable database which consists of high-priority, actionable data within the cumulative patient healthcare record; in which predefined criteria are used to categorize and classify imaging and clinical data in accordance with anatomy, technology, pathology, and time. The population of this referenceable database can be performed through a combination of manual and automated methods, with an additional step of data verification introduced for data quality control. Once created, these referenceable databases can be filtered at the point of care to provide context and user-specific data specific to the task being performed and individual end-user requirements.

Neurons in the human hippocampus and amygdala respond to both low- and high-level image properties

PubMed Central

Cabrales, Elaine; Wilson, Michael S.; Baker, Christopher P.; Thorp, Christopher K.; Smith, Kris A.; Treiman, David M.

2011-01-01

A large number of studies have demonstrated that structures within the medial temporal lobe, such as the hippocampus, are intimately involved in declarative memory for objects and people. Although these items are abstractions of the visual scene, specific visual details can change the speed and accuracy of their recall. By recording from 415 neurons in the hippocampus and amygdala of human epilepsy patients as they viewed images drawn from 10 image categories, we showed that the firing rates of 8% of these neurons encode image illuminance and contrast, low-level properties not directly pertinent to task performance, whereas in 7% of the neurons, firing rates encode the category of the item depicted in the image, a high-level property pertinent to the task. This simultaneous representation of high- and low-level image properties within the same brain areas may serve to bind separate aspects of visual objects into a coherent percept and allow episodic details of objects to influence mnemonic performance. PMID:21471400

Involuntary memories after a positive film are dampened by a visuospatial task: unhelpful in depression but helpful in mania?

PubMed

Davies, Charlotte; Malik, Aiysha; Pictet, Arnaud; Blackwell, Simon E; Holmes, Emily A

2012-01-01

Spontaneous negative mental images have been extensively researched due to the crucial role they play in conditions such as post-traumatic stress disorder. However, people can also experience spontaneous positive mental images, and these are little understood. Positive images may play a role in promoting healthy positive mood and may be lacking in conditions such as depression. However, they may also occur in problematic states of elevated mood, such as in bipolar disorder. Can we apply an understanding of spontaneous imagery gained by the study of spontaneous negative images to spontaneous positive images? In an analogue of the trauma film studies, 69 volunteers viewed an explicitly positive (rather than traumatic) film. Participants were randomly allocated post-film either to perform a visuospatial task (the computer game 'Tetris') or to a no-task control condition. Viewing the film enhanced positive mood and immediately post-film increased goal setting on a questionnaire measure. The film was successful in generating involuntary memories of specific scenes over the following week. As predicted, compared with the control condition, participants in the visuospatial task condition reported significantly fewer involuntary memories from the film in a diary over the subsequent week. Furthermore, scores on a recognition memory test at 1 week indicated an impairment in voluntary recall of the film in the visuospatial task condition. Clinical implications regarding the modulation of positive imagery after a positive emotional experience are discussed. Generally, boosting positive imagery may be a useful strategy for the recovery of depressed mood. Copyright © 2012 John Wiley & Sons, Ltd.

Involuntary Memories after a Positive Film Are Dampened by a Visuospatial Task: Unhelpful in Depression but Helpful in Mania?

PubMed Central

Charlotte, Davies; Malik, Aiysha; Pictet, Arnaud; Blackwell, Simon E; Holmes, Emily A

2012-01-01

Spontaneous negative mental images have been extensively researched due to the crucial role they play in conditions such as post-traumatic stress disorder. However, people can also experience spontaneous positive mental images, and these are little understood. Positive images may play a role in promoting healthy positive mood and may be lacking in conditions such as depression. However, they may also occur in problematic states of elevated mood, such as in bipolar disorder. Can we apply an understanding of spontaneous imagery gained by the study of spontaneous negative images to spontaneous positive images? In an analogue of the trauma film studies, 69 volunteers viewed an explicitly positive (rather than traumatic) film. Participants were randomly allocated post-film either to perform a visuospatial task (the computer game ‘Tetris’) or to a no-task control condition. Viewing the film enhanced positive mood and immediately post-film increased goal setting on a questionnaire measure. The film was successful in generating involuntary memories of specific scenes over the following week. As predicted, compared with the control condition, participants in the visuospatial task condition reported significantly fewer involuntary memories from the film in a diary over the subsequent week. Furthermore, scores on a recognition memory test at 1 week indicated an impairment in voluntary recall of the film in the visuospatial task condition. Clinical implications regarding the modulation of positive imagery after a positive emotional experience are discussed. Generally, boosting positive imagery may be a useful strategy for the recovery of depressed mood. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22570062

Vision function testing for a suprachoroidal retinal prosthesis: effects of image filtering

NASA Astrophysics Data System (ADS)

Barnes, Nick; Scott, Adele F.; Lieby, Paulette; Petoe, Matthew A.; McCarthy, Chris; Stacey, Ashley; Ayton, Lauren N.; Sinclair, Nicholas C.; Shivdasani, Mohit N.; Lovell, Nigel H.; McDermott, Hugh J.; Walker, Janine G.; BVA Consortium,the

2016-06-01

Objective. One strategy to improve the effectiveness of prosthetic vision devices is to process incoming images to ensure that key information can be perceived by the user. This paper presents the first comprehensive results of vision function testing for a suprachoroidal retinal prosthetic device utilizing of 20 stimulating electrodes. Further, we investigate whether using image filtering can improve results on a light localization task for implanted participants compared to minimal vision processing. No controlled implanted participant studies have yet investigated whether vision processing methods that are not task-specific can lead to improved results. Approach. Three participants with profound vision loss from retinitis pigmentosa were implanted with a suprachoroidal retinal prosthesis. All three completed multiple trials of a light localization test, and one participant completed multiple trials of acuity tests. The visual representations used were: Lanczos2 (a high quality Nyquist bandlimited downsampling filter); minimal vision processing (MVP); wide view regional averaging filtering (WV); scrambled; and, system off. Main results. Using Lanczos2, all three participants successfully completed a light localization task and obtained a significantly higher percentage of correct responses than using MVP (p≤slant 0.025) or with system off (p\\lt 0.0001). Further, in a preliminary result using Lanczos2, one participant successfully completed grating acuity and Landolt C tasks, and showed significantly better performance (p=0.004) compared to WV, scrambled and system off on the grating acuity task. Significance. Participants successfully completed vision tasks using a 20 electrode suprachoroidal retinal prosthesis. Vision processing with a Nyquist bandlimited image filter has shown an advantage for a light localization task. This result suggests that this and targeted, more advanced vision processing schemes may become important components of retinal prostheses to enhance performance. ClinicalTrials.gov Identifier: NCT01603576.

American and Greek Children's Visual Images of Scientists

NASA Astrophysics Data System (ADS)

Christidou, Vasilia; Bonoti, Fotini; Kontopoulou, Argiro

2016-08-01

This study explores American and Greek primary pupils' visual images of scientists by means of two nonverbal data collection tasks to identify possible convergences and divergences. Specifically, it aims to investigate whether their images of scientists vary according to the data collection instrument used and to gender. To this end, 91 third-grade American ( N = 46) and Greek ( N = 45) pupils were examined. Data collection was conducted through a drawing task based on Chambers (1983) `Draw-A-Scientist-Test' (DAST) and a picture selection task during which the children selected between 14 pairs of illustrations those that were most probable to represent scientists. Analysis focused on stereotype indicators related with scientists' appearance and work setting. Results showed that the two groups' performance varied significantly across the tasks used to explore their stereotypic perceptions, although the overall stereotypy was not differentiated according to participants' ethnic group. Moreover, boys were found to use more stereotypic indicators than girls, while the picture selection task elicited more stereotypic responses than the drawing task. In general, data collected by the two instruments revealed convergences and divergences concerning the stereotypic indicators preferred. Similarities and differences between national groups point to the influence of a globalized popular culture on the one hand and of the different sociocultural contexts underlying science curricula and their implementation on the other. Implications for science education are discussed.

Cholinergic enhancement reduces functional connectivity and BOLD variability in visual extrastriate cortex during selective attention.

PubMed

Ricciardi, Emiliano; Handjaras, Giacomo; Bernardi, Giulio; Pietrini, Pietro; Furey, Maura L

2013-01-01

Enhancing cholinergic function improves performance on various cognitive tasks and alters neural responses in task specific brain regions. We have hypothesized that the changes in neural activity observed during increased cholinergic function reflect an increase in neural efficiency that leads to improved task performance. The current study tested this hypothesis by assessing neural efficiency based on cholinergically-mediated effects on regional brain connectivity and BOLD signal variability. Nine subjects participated in a double-blind, placebo-controlled crossover fMRI study. Following an infusion of physostigmine (1 mg/h) or placebo, echo-planar imaging (EPI) was conducted as participants performed a selective attention task. During the task, two images comprised of superimposed pictures of faces and houses were presented. Subjects were instructed periodically to shift their attention from one stimulus component to the other and to perform a matching task using hand held response buttons. A control condition included phase-scrambled images of superimposed faces and houses that were presented in the same temporal and spatial manner as the attention task; participants were instructed to perform a matching task. Cholinergic enhancement improved performance during the selective attention task, with no change during the control task. Functional connectivity analyses showed that the strength of connectivity between ventral visual processing areas and task-related occipital, parietal and prefrontal regions reduced significantly during cholinergic enhancement, exclusively during the selective attention task. Physostigmine administration also reduced BOLD signal temporal variability relative to placebo throughout temporal and occipital visual processing areas, again during the selective attention task only. Together with the observed behavioral improvement, the decreases in connectivity strength throughout task-relevant regions and BOLD variability within stimulus processing regions support the hypothesis that cholinergic augmentation results in enhanced neural efficiency. This article is part of a Special Issue entitled 'Cognitive Enhancers'. Copyright © 2012 Elsevier Ltd. All rights reserved.

The posterior parietal paradox: Why do functional magnetic resonance imaging and lesion studies on episodic memory produce conflicting results?

PubMed

Schoo, L A; van Zandvoort, M J E; Biessels, G J; Kappelle, L J; Postma, A; de Haan, E H F

2011-03-01

Recent functional magnetic resonance imaging (fMRI) studies addressing healthy subjects point towards posterior parietal cortex (PPC) involvement in episodic memory tasks. This is noteworthy, since neuropsychological studies usually do not connect parietal lesions to episodic memory impairments. Therefore an inventory of the possible factors behind this apparent paradox is warranted. This review compared fMRI studies which demonstrated PPC activity in episodic memory tasks, with findings with studies of patients with PPC lesions. A systematic evaluation of possible explanations for the posterior parietal paradox indicates that PPC activation in fMRI studies does not appear to be attributable to confounding cognitive/psychomotor processes, such as button pressing or stimulus processing. What may be of more importance is the extent to which an episodic memory task loads on three closely related cognitive processes: effort and attention, self-related activity, and scene and image construction. We discuss to what extent these cognitive processes can account for the paradox between lesion and fMRI results. They are strongly intertwined with the episodic memory and may critically determine in how far the PPC plays a role in a given memory task. Future patient studies might profit from specifically taking these cognitive factors into consideration in the task design. ©2010 The British Psychological Society.

Cerebral networks of sustained attention and working memory: a functional magnetic resonance imaging study based on the Continuous Performance Test.

PubMed

Bartés-Serrallonga, M; Adan, A; Solé-Casals, J; Caldú, X; Falcón, C; Pérez-Pàmies, M; Bargalló, N; Serra-Grabulosa, J M

2014-04-01

One of the most used paradigms in the study of attention is the Continuous Performance Test (CPT). The identical pairs version (CPT-IP) has been widely used to evaluate attention deficits in developmental, neurological and psychiatric disorders. However, the specific locations and the relative distribution of brain activation in networks identified with functional imaging, varies significantly with differences in task design. To design a task to evaluate sustained attention using functional magnetic resonance imaging (fMRI), and thus to provide data for research concerned with the role of these functions. Forty right-handed, healthy students (50% women; age range: 18-25 years) were recruited. A CPT-IP implemented as a block design was used to assess sustained attention during the fMRI session. The behavioural results from the CPT-IP task showed a good performance in all subjects, higher than 80% of hits. fMRI results showed that the used CPT-IP task activates a network of frontal, parietal and occipital areas, and that these are related to executive and attentional functions. In relation to the use of the CPT to study of attention and working memory, this task provides normative data in healthy adults, and it could be useful to evaluate disorders which have attentional and working memory deficits.

Computer vision barrel inspection

NASA Astrophysics Data System (ADS)

Wolfe, William J.; Gunderson, James; Walworth, Matthew E.

1994-02-01

One of the Department of Energy's (DOE) ongoing tasks is the storage and inspection of a large number of waste barrels containing a variety of hazardous substances. Martin Marietta is currently contracted to develop a robotic system -- the Intelligent Mobile Sensor System (IMSS) -- for the automatic monitoring and inspection of these barrels. The IMSS is a mobile robot with multiple sensors: video cameras, illuminators, laser ranging and barcode reader. We assisted Martin Marietta in this task, specifically in the development of image processing algorithms that recognize and classify the barrel labels. Our subsystem uses video images to detect and locate the barcode, so that the barcode reader can be pointed at the barcode.

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

NASA Astrophysics Data System (ADS)

Kalayeh, Mahdi M.; Marin, Thibault; Pretorius, P. Hendrik; Wernick, Miles N.; Yang, Yongyi; Brankov, Jovan G.

2011-03-01

In this paper, we present a numerical observer for image quality assessment, aiming to predict human observer accuracy in a cardiac perfusion defect detection task for single-photon emission computed tomography (SPECT). In medical imaging, image quality should be assessed by evaluating the human observer accuracy for a specific diagnostic task. This approach is known as task-based assessment. Such evaluations are important for optimizing and testing imaging devices and algorithms. Unfortunately, human observer studies with expert readers are costly and time-demanding. To address this problem, numerical observers have been developed as a surrogate for human readers to predict human diagnostic performance. The channelized Hotelling observer (CHO) with internal noise model has been found to predict human performance well in some situations, but does not always generalize well to unseen data. We have argued in the past that finding a model to predict human observers could be viewed as a machine learning problem. Following this approach, in this paper we propose a channelized relevance vector machine (CRVM) to predict human diagnostic scores in a detection task. We have previously used channelized support vector machines (CSVM) to predict human scores and have shown that this approach offers better and more robust predictions than the classical CHO method. The comparison of the proposed CRVM with our previously introduced CSVM method suggests that CRVM can achieve similar generalization accuracy, while dramatically reducing model complexity and computation time.

Alignment of multimodality, 2D and 3D breast images

NASA Astrophysics Data System (ADS)

Grevera, George J.; Udupa, Jayaram K.

2003-05-01

In a larger effort, we are studying methods to improve the specificity of the diagnosis of breast cancer by combining the complementary information available from multiple imaging modalities. Merging information is important for a number of reasons. For example, contrast uptake curves are an indication of malignancy. The determination of anatomical locations in corresponding images from various modalities is necessary to ascertain the extent of regions of tissue. To facilitate this fusion, registration becomes necessary. We describe in this paper a framework in which 2D and 3D breast images from MRI, PET, Ultrasound, and Digital Mammography can be registered to facilitate this goal. Briefly, prior to image acquisition, an alignment grid is drawn on the breast skin. Modality-specific markers are then placed at the indicated grid points. Images are then acquired by a specific modality with the modality specific external markers in place causing the markers to appear in the images. This is the first study that we are aware of that has undertaken the difficult task of registering 2D and 3D images of such a highly deformable (the breast) across such a wide variety of modalities. This paper reports some very preliminary results from this project.

Automatic Identification and Quantification of Extra-Well Fluorescence in Microarray Images.

PubMed

Rivera, Robert; Wang, Jie; Yu, Xiaobo; Demirkan, Gokhan; Hopper, Marika; Bian, Xiaofang; Tahsin, Tasnia; Magee, D Mitchell; Qiu, Ji; LaBaer, Joshua; Wallstrom, Garrick

2017-11-03

In recent studies involving NAPPA microarrays, extra-well fluorescence is used as a key measure for identifying disease biomarkers because there is evidence to support that it is better correlated with strong antibody responses than statistical analysis involving intraspot intensity. Because this feature is not well quantified by traditional image analysis software, identification and quantification of extra-well fluorescence is performed manually, which is both time-consuming and highly susceptible to variation between raters. A system that could automate this task efficiently and effectively would greatly improve the process of data acquisition in microarray studies, thereby accelerating the discovery of disease biomarkers. In this study, we experimented with different machine learning methods, as well as novel heuristics, for identifying spots exhibiting extra-well fluorescence (rings) in microarray images and assigning each ring a grade of 1-5 based on its intensity and morphology. The sensitivity of our final system for identifying rings was found to be 72% at 99% specificity and 98% at 92% specificity. Our system performs this task significantly faster than a human, while maintaining high performance, and therefore represents a valuable tool for microarray image analysis.

A new e-learning platform for radiology education (RadEd).

PubMed

Xiberta, Pau; Boada, Imma

2016-04-01

One of the key elements of e-learning platforms is the content provided to the students. Content creation is a time demanding task that requires teachers to prepare material taking into account that it will be accessed on-line. Moreover, the teacher is restricted by the functionalities provided by the e-learning platforms. In contexts such as radiology where images have a key role, the required functionalities are still more specific and difficult to be provided by these platforms. Our purpose is to create a framework to make teacher's tasks easier, specially when he has to deal with contents where images have a main role. In this paper, we present RadEd, a new web-based teaching framework that integrates a smart editor to create case-based exercises that support image interaction such as changing the window width and the grey scale used to render the image, taking measurements on the image, attaching labels to images and selecting parts of the images, amongst others. It also provides functionalities to prepare courses with different topics, exercises and theory material, and also functionalities to control students' work. Different experts have used RadEd and all of them have considered it a very useful and valuable tool to prepare courses where radiological images are the main component. RadEd provides teachers functionalities to prepare more realistic cases and students the ability to make a more specific diagnosis. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Long-range functional interactions of anterior insula and medial frontal cortex are differently modulated by visuospatial and inductive reasoning tasks.

PubMed

Ebisch, Sjoerd J H; Mantini, Dante; Romanelli, Roberta; Tommasi, Marco; Perrucci, Mauro G; Romani, Gian Luca; Colom, Roberto; Saggino, Aristide

2013-09-01

The brain is organized into functionally specific networks as characterized by intrinsic functional relationships within discrete sets of brain regions. However, it is poorly understood whether such functional networks are dynamically organized according to specific task-states. The anterior insular cortex (aIC)-dorsal anterior cingulate cortex (dACC)/medial frontal cortex (mFC) network has been proposed to play a central role in human cognitive abilities. The present functional magnetic resonance imaging (fMRI) study aimed at testing whether functional interactions of the aIC-dACC/mFC network in terms of temporally correlated patterns of neural activity across brain regions are dynamically modulated by transitory, ongoing task demands. For this purpose, functional interactions of the aIC-dACC/mFC network are compared during two distinguishable fluid reasoning tasks, Visualization and Induction. The results show an increased functional coupling of bilateral aIC with visual cortices in the occipital lobe during the Visualization task, whereas coupling of mFC with right anterior frontal cortex was enhanced during the Induction task. These task-specific modulations of functional interactions likely reflect ability related neural processing. Furthermore, functional connectivity strength between right aIC and right dACC/mFC reliably predicts general task performance. The findings suggest that the analysis of long-range functional interactions may provide complementary information about brain-behavior relationships. On the basis of our results, it is proposed that the aIC-dACC/mFC network contributes to the integration of task-common and task-specific information based on its within-network as well as its between-network dynamic functional interactions. Copyright © 2013 Elsevier Inc. All rights reserved.

Constrained surface controllers for three-dimensional image data reformatting.

PubMed

Graves, Martin J; Black, Richard T; Lomas, David J

2009-07-01

This study did not require ethical approval in the United Kingdom. The aim of this work was to create two controllers for navigating a two-dimensional image plane through a volumetric data set, providing two important features of the ultrasonographic paradigm: orientation matching of the navigation device and the desired image plane in the three-dimensional (3D) data and a constraining surface to provide a nonvisual reference for the image plane location in the 3D data. The first constrained surface controller (CSC) uses a planar constraining surface, while the second CSC uses a hemispheric constraining surface. Ten radiologists were asked to obtain specific image reformations by using both controllers and a commercially available medical imaging workstation. The time taken to perform each reformatting task was recorded. The users were also asked structured questions comparing the utility of both methods. There was a significant reduction in the time taken to perform the specified reformatting tasks by using the simpler planar controller as compared with a standard workstation, whereas there was no significant difference for the more complex hemispheric controller. The majority of users reported that both controllers allowed them to concentrate entirely on the reformatting task and the related image rather than being distracted by the need for interaction with the workstation interface. In conclusion, the CSCs provide an intuitive paradigm for interactive reformatting of volumetric data. (c) RSNA, 2009.

Random forest regression for magnetic resonance image synthesis.

PubMed

Jog, Amod; Carass, Aaron; Roy, Snehashis; Pham, Dzung L; Prince, Jerry L

2017-01-01

By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T 2 -weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T 2 -weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets. Copyright © 2016 Elsevier B.V. All rights reserved.

Metric Learning for Hyperspectral Image Segmentation

NASA Technical Reports Server (NTRS)

Bue, Brian D.; Thompson, David R.; Gilmore, Martha S.; Castano, Rebecca

2011-01-01

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Visual affective classification by combining visual and text features.

PubMed

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task.

Visual affective classification by combining visual and text features

PubMed Central

Liu, Ningning; Wang, Kai; Jin, Xin; Gao, Boyang; Dellandréa, Emmanuel; Chen, Liming

2017-01-01

Affective analysis of images in social networks has drawn much attention, and the texts surrounding images are proven to provide valuable semantic meanings about image content, which can hardly be represented by low-level visual features. In this paper, we propose a novel approach for visual affective classification (VAC) task. This approach combines visual representations along with novel text features through a fusion scheme based on Dempster-Shafer (D-S) Evidence Theory. Specifically, we not only investigate different types of visual features and fusion methods for VAC, but also propose textual features to effectively capture emotional semantics from the short text associated to images based on word similarity. Experiments are conducted on three public available databases: the International Affective Picture System (IAPS), the Artistic Photos and the MirFlickr Affect set. The results demonstrate that the proposed approach combining visual and textual features provides promising results for VAC task. PMID:28850566

Image quality and dose differences caused by vendor-specific image processing of neonatal radiographs.

PubMed

Sensakovic, William F; O'Dell, M Cody; Letter, Haley; Kohler, Nathan; Rop, Baiywo; Cook, Jane; Logsdon, Gregory; Varich, Laura

2016-10-01

Image processing plays an important role in optimizing image quality and radiation dose in projection radiography. Unfortunately commercial algorithms are black boxes that are often left at or near vendor default settings rather than being optimized. We hypothesize that different commercial image-processing systems, when left at or near default settings, create significant differences in image quality. We further hypothesize that image-quality differences can be exploited to produce images of equivalent quality but lower radiation dose. We used a portable radiography system to acquire images on a neonatal chest phantom and recorded the entrance surface air kerma (ESAK). We applied two image-processing systems (Optima XR220amx, by GE Healthcare, Waukesha, WI; and MUSICA(2) by Agfa HealthCare, Mortsel, Belgium) to the images. Seven observers (attending pediatric radiologists and radiology residents) independently assessed image quality using two methods: rating and matching. Image-quality ratings were independently assessed by each observer on a 10-point scale. Matching consisted of each observer matching GE-processed images and Agfa-processed images with equivalent image quality. A total of 210 rating tasks and 42 matching tasks were performed and effective dose was estimated. Median Agfa-processed image-quality ratings were higher than GE-processed ratings. Non-diagnostic ratings were seen over a wider range of doses for GE-processed images than for Agfa-processed images. During matching tasks, observers matched image quality between GE-processed images and Agfa-processed images acquired at a lower effective dose (11 ± 9 μSv; P < 0.0001). Image-processing methods significantly impact perceived image quality. These image-quality differences can be exploited to alter protocols and produce images of equivalent image quality but lower doses. Those purchasing projection radiography systems or third-party image-processing software should be aware that image processing can significantly impact image quality when settings are left near default values.

Developmental changes in category-specific brain responses to numbers and letters in a working memory task

PubMed Central

Libertus, Melissa E.; Brannon, Elizabeth M.; Pelphrey, Kevin A.

2009-01-01

Neuroimaging studies have identified a common network of brain regions involving the prefrontal and parietal cortices across a variety of working memory (WM) tasks. However, previous studies have also reported category-specific dissociations of activation within this network. In this study, we investigated the development of category-specific activation in a WM task with digits, letters, and faces. Eight-year-old children and adults performed a 2-back WM task while their brain activity was measured using functional magnetic resonance imaging (fMRI). Overall, children were significantly slower and less accurate than adults on all three WM conditions (digits, letters, and faces); however, within each age group, behavioral performance across the three conditions was very similar. FMRI results revealed category-specific activation in adults but not children in the intraparietal sulcus for the digit condition. Likewise, during the letter condition, category-specific activation was observed in adults but not children in the left occipital–temporal cortex. In contrast, children and adults showed highly similar brain-activity patterns in the lateral fusiform gyri when solving the 2-back WM task with face stimuli. Our results suggest that 8-year-old children do not yet engage the typical brain regions that have been associated with abstract or semantic processing of numerical symbols and letters when these processes are task-irrelevant and the primary task is demanding. Nevertheless, brain activity in letter-responsive areas predicted children’s spelling performance underscoring the relationship between abstract processing of letters and linguistic abilities. Lastly, behavioral performance on the WM task was predictive of math and language abilities highlighting the connection between WM and other cognitive abilities in development. PMID:19027079

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

2016-03-01

Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate this approach,using a publicly available head and neck CT dataset. We also show that a deep neural network of similar depth, if trained directly using backpropagation, cannot acheive the tasks achieved using our layer wise training paradigm.

Choice-specific sequences in parietal cortex during a virtual-navigation decision task

PubMed Central

Harvey, Christopher D.; Coen, Philip; Tank, David W.

2012-01-01

The posterior parietal cortex (PPC) plays an important role in many cognitive behaviors; however, the neural circuit dynamics underlying PPC function are not well understood. Here we optically imaged the spatial and temporal activity patterns of neuronal populations in mice performing a PPC-dependent task that combined a perceptual decision and memory-guided navigation in a virtual environment. Individual neurons had transient activation staggered relative to one another in time, forming a sequence of neuronal activation spanning the entire length of a task trial. Distinct sequences of neurons were triggered on trials with opposite behavioral choices and defined divergent, choice-specific trajectories through a state space of neuronal population activity. Cells participating in the different sequences and at distinct time points in the task were anatomically intermixed over microcircuit length scales (< 100 micrometers). During working memory decision tasks the PPC may therefore perform computations through sequence-based circuit dynamics, rather than long-lived stable states, implemented using anatomically intermingled microcircuits. PMID:22419153

Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations

NASA Astrophysics Data System (ADS)

Lapuebla-Ferri, Andrés; Cegoñino-Banzo, José; Jiménez-Mocholí, Antonio-José; Pérez del Palomar, Amaya

2017-11-01

In breast cancer screening or diagnosis, it is usual to combine different images in order to locate a lesion as accurately as possible. These images are generated using a single or several imaging techniques. As x-ray-based mammography is widely used, a breast lesion is located in the same plane of the image (mammogram), but tracking it across mammograms corresponding to different views is a challenging task for medical physicians. Accordingly, simulation tools and methodologies that use patient-specific numerical models can facilitate the task of fusing information from different images. Additionally, these tools need to be as straightforward as possible to facilitate their translation to the clinical area. This paper presents a patient-specific, finite-element-based and semi-automated simulation methodology to track breast lesions across mammograms. A realistic three-dimensional computer model of a patient’s breast was generated from magnetic resonance imaging to simulate mammographic compressions in cranio-caudal (CC, head-to-toe) and medio-lateral oblique (MLO, shoulder-to-opposite hip) directions. For each compression being simulated, a virtual mammogram was obtained and posteriorly superimposed to the corresponding real mammogram, by sharing the nipple as a common feature. Two-dimensional rigid-body transformations were applied, and the error distance measured between the centroids of the tumors previously located on each image was 3.84 mm and 2.41 mm for CC and MLO compression, respectively. Considering that the scope of this work is to conceive a methodology translatable to clinical practice, the results indicate that it could be helpful in supporting the tracking of breast lesions.

Attentional performance is correlated with the local regional efficiency of intrinsic brain networks.

PubMed

Xu, Junhai; Yin, Xuntao; Ge, Haitao; Han, Yan; Pang, Zengchang; Tang, Yuchun; Liu, Baolin; Liu, Shuwei

2015-01-01

Attention is a crucial brain function for human beings. Using neuropsychological paradigms and task-based functional brain imaging, previous studies have indicated that widely distributed brain regions are engaged in three distinct attention subsystems: alerting, orienting and executive control (EC). Here, we explored the potential contribution of spontaneous brain activity to attention by examining whether resting-state activity could account for individual differences of the attentional performance in normal individuals. The resting-state functional images and behavioral data from attention network test (ANT) task were collected in 59 healthy subjects. Graph analysis was conducted to obtain the characteristics of functional brain networks and linear regression analyses were used to explore their relationships with behavioral performances of the three attentional components. We found that there was no significant relationship between the attentional performance and the global measures, while the attentional performance was associated with specific local regional efficiency. These regions related to the scores of alerting, orienting and EC largely overlapped with the regions activated in previous task-related functional imaging studies, and were consistent with the intrinsic dorsal and ventral attention networks (DAN/VAN). In addition, the strong associations between the attentional performance and specific regional efficiency suggested that there was a possible relationship between the DAN/VAN and task performances in the ANT. We concluded that the intrinsic activity of the human brain could reflect the processing efficiency of the attention system. Our findings revealed a robust evidence for the functional significance of the efficiently organized intrinsic brain network for highly productive cognitions and the hypothesized role of the DAN/VAN at rest.

Relative expertise affects N170 during selective attention to superimposed face-character images.

PubMed

Ip, Chengteng; Wang, Hailing; Fu, Shimin

2017-07-01

It remains unclear whether the N170 of ERPs reflects domain-specific or domain-general visual object processing. In this study, we used superimposed images of a face and a Chinese character such that participants' relative expertise for the two object types was either similar (Experiment 1 and 2) or different (Experiment 3). Experiment 1 showed that N170 amplitude was larger when participants attended to the character instead of the face of a face-character combination. This result was unchanged in Experiment 2, in which task difficulty was selectively increased for the face component of the combined stimuli. Experiment 3 showed that, although this N170 enhancement for attending to characters relative to faces persisted for false characters with recognizable parts, it disappeared for unrecognizable characters. Therefore, N170 amplitude was significantly greater for Chinese characters than for faces presented within a combined image, independent of the relative task difficulty. This result strongly calls N170 face selectivity into question, demonstrating that, contrary to the expectations established by a domain-specific account, N170 is modulated by expertise. © 2017 Society for Psychophysiological Research.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gang, G; Siewerdsen, J; Stayman, J

Purpose: There has been increasing interest in integrating fluence field modulation (FFM) devices with diagnostic CT scanners for dose reduction purposes. Conventional FFM strategies, however, are often either based on heuristics or the analysis of filtered-backprojection (FBP) performance. This work investigates a prospective task-driven optimization of FFM for model-based iterative reconstruction (MBIR) in order to improve imaging performance at the same total dose as conventional strategies. Methods: The task-driven optimization framework utilizes an ultra-low dose 3D scout as a patient-specific anatomical model and a mathematical formation of the imaging task. The MBIR method investigated is quadratically penalized-likelihood reconstruction. The FFMmore » objective function uses detectability index, d’, computed as a function of the predicted spatial resolution and noise in the image. To optimize performance throughout the object, a maxi-min objective was adopted where the minimum d’ over multiple locations is maximized. To reduce the dimensionality of the problem, FFM is parameterized as a linear combination of 2D Gaussian basis functions over horizontal detector pixels and projection angles. The coefficients of these bases are found using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. The task-driven design was compared with three other strategies proposed for FBP reconstruction for a calcification cluster discrimination task in an abdomen phantom. Results: The task-driven optimization yielded FFM that was significantly different from those designed for FBP. Comparing all four strategies, the task-based design achieved the highest minimum d’ with an 8–48% improvement, consistent with the maxi-min objective. In addition, d’ was improved to a greater extent over a larger area within the entire phantom. Conclusion: Results from this investigation suggests the need to re-evaluate conventional FFM strategies for MBIR. The task-based optimization framework provides a promising approach that maximizes imaging performance under the same total dose constraint.« less

Drawing enhances cross-modal memory plasticity in the human brain: a case study in a totally blind adult

PubMed Central

Likova, Lora T.

2012-01-01

In a memory-guided drawing task under blindfolded conditions, we have recently used functional Magnetic Resonance Imaging (fMRI) to demonstrate that the primary visual cortex (V1) may operate as the visuo-spatial buffer, or “sketchpad,” for working memory. The results implied, however, a modality-independent or amodal form of its operation. In the present study, to validate the role of V1 in non-visual memory, we eliminated not only the visual input but all levels of visual processing by replicating the paradigm in a congenitally blind individual. Our novel Cognitive-Kinesthetic method was used to train this totally blind subject to draw complex images guided solely by tactile memory. Control tasks of tactile exploration and memorization of the image to be drawn, and memory-free scribbling were also included. FMRI was run before training and after training. Remarkably, V1 of this congenitally blind individual, which before training exhibited noisy, immature, and non-specific responses, after training produced full-fledged response time-courses specific to the tactile-memory drawing task. The results reveal the operation of a rapid training-based plasticity mechanism that recruits the resources of V1 in the process of learning to draw. The learning paradigm allowed us to investigate for the first time the evolution of plastic re-assignment in V1 in a congenitally blind subject. These findings are consistent with a non-visual memory involvement of V1, and specifically imply that the observed cortical reorganization can be empowered by the process of learning to draw. PMID:22593738

Drawing enhances cross-modal memory plasticity in the human brain: a case study in a totally blind adult.

PubMed

Likova, Lora T

2012-01-01

In a memory-guided drawing task under blindfolded conditions, we have recently used functional Magnetic Resonance Imaging (fMRI) to demonstrate that the primary visual cortex (V1) may operate as the visuo-spatial buffer, or "sketchpad," for working memory. The results implied, however, a modality-independent or amodal form of its operation. In the present study, to validate the role of V1 in non-visual memory, we eliminated not only the visual input but all levels of visual processing by replicating the paradigm in a congenitally blind individual. Our novel Cognitive-Kinesthetic method was used to train this totally blind subject to draw complex images guided solely by tactile memory. Control tasks of tactile exploration and memorization of the image to be drawn, and memory-free scribbling were also included. FMRI was run before training and after training. Remarkably, V1 of this congenitally blind individual, which before training exhibited noisy, immature, and non-specific responses, after training produced full-fledged response time-courses specific to the tactile-memory drawing task. The results reveal the operation of a rapid training-based plasticity mechanism that recruits the resources of V1 in the process of learning to draw. The learning paradigm allowed us to investigate for the first time the evolution of plastic re-assignment in V1 in a congenitally blind subject. These findings are consistent with a non-visual memory involvement of V1, and specifically imply that the observed cortical reorganization can be empowered by the process of learning to draw.

Deficits in agency in schizophrenia, and additional deficits in body image, body schema, and internal timing, in passivity symptoms.

PubMed

Graham, Kyran T; Martin-Iverson, Mathew T; Holmes, Nicholas P; Jablensky, Assen; Waters, Flavie

2014-01-01

Individuals with schizophrenia, particularly those with passivity symptoms, may not feel in control of their actions, believing them to be controlled by external agents. Cognitive operations that contribute to these symptoms may include abnormal processing in agency as well as body representations that deal with body schema and body image. However, these operations in schizophrenia are not fully understood, and the questions of general versus specific deficits in individuals with different symptom profiles remain unanswered. Using the projected-hand illusion (a digital video version of the rubber-hand illusion) with synchronous and asynchronous stroking (500 ms delay), and a hand laterality judgment task, we assessed sense of agency, body image, and body schema in 53 people with clinically stable schizophrenia (with a current, past, and no history of passivity symptoms) and 48 healthy controls. The results revealed a stable trait in schizophrenia with no difference between clinical subgroups (sense of agency) and some quantitative (specific) differences depending on the passivity symptom profile (body image and body schema). Specifically, a reduced sense of self-agency was a common feature of all clinical subgroups. However, subgroup comparisons showed that individuals with passivity symptoms (both current and past) had significantly greater deficits on tasks assessing body image and body schema, relative to the other groups. In addition, patients with current passivity symptoms failed to demonstrate the normal reduction in body illusion typically seen with a 500 ms delay in visual feedback (asynchronous condition), suggesting internal timing problems. Altogether, the results underscore self-abnormalities in schizophrenia, provide evidence for both trait abnormalities and state changes specific to passivity symptoms, and point to a role for internal timing deficits as a mechanistic explanation for external cues becoming a possible source of self-body input.

Motor Imagery in Asperger Syndrome: Testing Action Simulation by the Hand Laterality Task

PubMed Central

Conson, Massimiliano; Mazzarella, Elisabetta; Frolli, Alessandro; Esposito, Dalila; Marino, Nicoletta; Trojano, Luigi; Massagli, Angelo; Gison, Giovanna; Aprea, Nellantonio; Grossi, Dario

2013-01-01

Asperger syndrome (AS) is a neurodevelopmental condition within the Autism Spectrum Disorders (ASD) characterized by specific difficulties in social interaction, communication and behavioural control. In recent years, it has been suggested that ASD is related to a dysfunction of action simulation processes, but studies employing imitation or action observation tasks provided mixed results. Here, we addressed action simulation processes in adolescents with AS by means of a motor imagery task, the classical hand laterality task (to decide whether a rotated hand image is left or right); mental rotation of letters was also evaluated. As a specific marker of action simulation in hand rotation, we assessed the so-called biomechanical effect, that is the advantage for judging hand pictures showing physically comfortable versus physically awkward positions. We found the biomechanical effect in typically-developing participants but not in participants with AS. Overall performance on both hand laterality and letter rotation tasks, instead, did not differ in the two groups. These findings demonstrated a specific alteration of motor imagery skills in AS. We suggest that impaired mental simulation and imitation of goal-less movements in ASD could be related to shared cognitive mechanisms. PMID:23894683

Watermarked cardiac CT image segmentation using deformable models and the Hermite transform

NASA Astrophysics Data System (ADS)

Gomez-Coronel, Sandra L.; Moya-Albor, Ernesto; Escalante-Ramírez, Boris; Brieva, Jorge

2015-01-01

Medical image watermarking is an open area for research and is a solution for the protection of copyright and intellectual property. One of the main challenges of this problem is that the marked images should not differ perceptually from the original images allowing a correct diagnosis and authentication. Furthermore, we also aim at obtaining watermarked images with very little numerical distortion so that computer vision tasks such as segmentation of important anatomical structures do not be impaired or affected. We propose a preliminary watermarking application in cardiac CT images based on a perceptive approach that includes a brightness model to generate a perceptive mask and identify the image regions where the watermark detection becomes a difficult task for the human eye. We propose a normalization scheme of the image in order to improve robustness against geometric attacks. We follow a spread spectrum technique to insert an alphanumeric code, such as patient's information, within the watermark. The watermark scheme is based on the Hermite transform as a bio-inspired image representation model. In order to evaluate the numerical integrity of the image data after watermarking, we perform a segmentation task based on deformable models. The segmentation technique is based on a vector-value level sets method such that, given a curve in a specific image, and subject to some constraints, the curve can evolve in order to detect objects. In order to stimulate the curve evolution we introduce simultaneously some image features like the gray level and the steered Hermite coefficients as texture descriptors. Segmentation performance was assessed by means of the Dice index and the Hausdorff distance. We tested different mark sizes and different insertion schemes on images that were later segmented either automatic or manual by physicians.

Gender differences in colour naming performance for gender specific body shape images.

PubMed

Elliman, N A; Green, M W; Wan, W K

1998-03-01

Males are increasingly subjected to pressures to conform to aesthetic body stereotypes. There is, however, comparatively little published research on the aetiology of male body shape concerns. Two experiments are presented, which investigate the relationship between gender specific body shape concerns and colour-naming performance. Each study comprised a between subject design, in which each subject was tested on a single occasion. A pictorial version of a modified Stroop task was used in both studies. Subjects colour-named gender specific obese and thin body shape images and semantically homogeneous neutral images (birds) presented in a blocked format. The first experiment investigated female subjects (N = 68) and the second investigated males (N = 56). Subjects also completed a self-report measure of eating behaviour. Currently dieting female subjects exhibited significant colour-naming differences between obese and neutral images. A similar pattern of colour-naming performance was found to be related to external eating in the male subjects.

Attribute-based classification for zero-shot visual object categorization.

PubMed

Lampert, Christoph H; Nickisch, Hannes; Harmeling, Stefan

2014-03-01

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.

fMRI Validation of fNIRS Measurements During a Naturalistic Task

PubMed Central

Noah, J. Adam; Ono, Yumie; Nomoto, Yasunori; Shimada, Sotaro; Tachibana, Atsumichi; Zhang, Xian; Bronner, Shaw; Hirsch, Joy

2015-01-01

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson’s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning. PMID:26132365

Computer assisted optical biopsy for colorectal polyps

NASA Astrophysics Data System (ADS)

Navarro-Avila, Fernando J.; Saint-Hill-Febles, Yadira; Renner, Janis; Klare, Peter; von Delius, Stefan; Navab, Nassir; Mateus, Diana

2017-03-01

We propose a method for computer-assisted optical biopsy for colorectal polyps, with the final goal of assisting the medical expert during the colonoscopy. In particular, we target the problem of automatic classification of polyp images in two classes: adenomatous vs non-adenoma. Our approach is based on recent advancements in convolutional neural networks (CNN) for image representation. In the paper, we describe and compare four different methodologies to address the binary classification task: a baseline with classical features and a Random Forest classifier, two methods based on features obtained from a pre-trained network, and finally, the end-to-end training of a CNN. With the pre-trained network, we show the feasibility of transferring a feature extraction mechanism trained on millions of natural images, to the task of classifying adenomatous polyps. We then demonstrate further performance improvements when training the CNN for our specific classification task. In our study, 776 polyp images were acquired and histologically analyzed after polyp resection. We report a performance increase of the CNN-based approaches with respect to both, the conventional engineered features and to a state-of-the-art method based on videos and 3D shape features.

Task-selective memory effects for successfully implemented encoding strategies.

PubMed

Leshikar, Eric D; Duarte, Audrey; Hertzog, Christopher

2012-01-01

Previous behavioral evidence suggests that instructed strategy use benefits associative memory formation in paired associate tasks. Two such effective encoding strategies--visual imagery and sentence generation--facilitate memory through the production of different types of mediators (e.g., mental images and sentences). Neuroimaging evidence suggests that regions of the brain support memory reflecting the mental operations engaged at the time of study. That work, however, has not taken into account self-reported encoding task success (i.e., whether participants successfully generated a mediator). It is unknown, therefore, whether task-selective memory effects specific to each strategy might be found when encoding strategies are successfully implemented. In this experiment, participants studied pairs of abstract nouns under either visual imagery or sentence generation encoding instructions. At the time of study, participants reported their success at generating a mediator. Outside of the scanner, participants further reported the quality of the generated mediator (e.g., images, sentences) for each word pair. We observed task-selective memory effects for visual imagery in the left middle occipital gyrus, the left precuneus, and the lingual gyrus. No such task-selective effects were observed for sentence generation. Intriguingly, activity at the time of study in the left precuneus was modulated by the self-reported quality (vividness) of the generated mental images with greater activity for trials given higher ratings of quality. These data suggest that regions of the brain support memory in accord with the encoding operations engaged at the time of study.

Task-Selective Memory Effects for Successfully Implemented Encoding Strategies

PubMed Central

Leshikar, Eric D.; Duarte, Audrey; Hertzog, Christopher

2012-01-01

Previous behavioral evidence suggests that instructed strategy use benefits associative memory formation in paired associate tasks. Two such effective encoding strategies–visual imagery and sentence generation–facilitate memory through the production of different types of mediators (e.g., mental images and sentences). Neuroimaging evidence suggests that regions of the brain support memory reflecting the mental operations engaged at the time of study. That work, however, has not taken into account self-reported encoding task success (i.e., whether participants successfully generated a mediator). It is unknown, therefore, whether task-selective memory effects specific to each strategy might be found when encoding strategies are successfully implemented. In this experiment, participants studied pairs of abstract nouns under either visual imagery or sentence generation encoding instructions. At the time of study, participants reported their success at generating a mediator. Outside of the scanner, participants further reported the quality of the generated mediator (e.g., images, sentences) for each word pair. We observed task-selective memory effects for visual imagery in the left middle occipital gyrus, the left precuneus, and the lingual gyrus. No such task-selective effects were observed for sentence generation. Intriguingly, activity at the time of study in the left precuneus was modulated by the self-reported quality (vividness) of the generated mental images with greater activity for trials given higher ratings of quality. These data suggest that regions of the brain support memory in accord with the encoding operations engaged at the time of study. PMID:22693593

Fractionation of the component processes underlying successful episodic encoding: a combined fMRI and divided-attention study.

PubMed

Uncapher, Melina R; Rugg, Michael D

2008-02-01

Considerable evidence suggests that attentional resources are necessary for the encoding of episodic memories, but the nature of the relationship between attention and neural correlates of encoding is unclear. Here we address this question using functional magnetic resonance imaging and a divided-attention paradigm in which competition for different types of attentional resources was manipulated. Fifteen volunteers were scanned while making animacy judgments to visually presented words and concurrently performing one of three tasks on auditorily presented words: male/female voice discrimination (control task), 1-back voice comparison (1-back task), or indoor/outdoor judgment (semantic task). The 1-back and semantic tasks were designed to compete for task-generic and task-specific attentional resources, respectively. Using the "remember/know" procedure, memory for the study words was assessed after 15 min. In the control condition, subsequent memory effects associated with later recollection were identified in the left dorsal inferior frontal gyrus and in the left hippocampus. These effects were differentially attenuated in the two more difficult divided-attention conditions. The effects of divided attention seem, therefore, to reflect impairments due to limitations at both task-generic and task-specific levels. Additionally, each of the two more difficult divided-attention conditions was associated with subsequent memory effects in regions distinct from those showing effects in the control condition. These findings suggest the engagement of alternative encoding processes to those engaged in the control task. The overall pattern of findings suggests that divided attention can impact later memory in different ways, and accordingly, that different attentional resources, including task-generic and task-specific resources, make distinct contributions to successful episodic encoding.

Parallel processing considerations for image recognition tasks

NASA Astrophysics Data System (ADS)

Simske, Steven J.

2011-01-01

Many image recognition tasks are well-suited to parallel processing. The most obvious example is that many imaging tasks require the analysis of multiple images. From this standpoint, then, parallel processing need be no more complicated than assigning individual images to individual processors. However, there are three less trivial categories of parallel processing that will be considered in this paper: parallel processing (1) by task; (2) by image region; and (3) by meta-algorithm. Parallel processing by task allows the assignment of multiple workflows-as diverse as optical character recognition [OCR], document classification and barcode reading-to parallel pipelines. This can substantially decrease time to completion for the document tasks. For this approach, each parallel pipeline is generally performing a different task. Parallel processing by image region allows a larger imaging task to be sub-divided into a set of parallel pipelines, each performing the same task but on a different data set. This type of image analysis is readily addressed by a map-reduce approach. Examples include document skew detection and multiple face detection and tracking. Finally, parallel processing by meta-algorithm allows different algorithms to be deployed on the same image simultaneously. This approach may result in improved accuracy.

Early Visual Word Processing Is Flexible: Evidence from Spatiotemporal Brain Dynamics.

PubMed

Chen, Yuanyuan; Davis, Matthew H; Pulvermüller, Friedemann; Hauk, Olaf

2015-09-01

Visual word recognition is often described as automatic, but the functional locus of top-down effects is still a matter of debate. Do task demands modulate how information is retrieved, or only how it is used? We used EEG/MEG recordings to assess whether, when, and how task contexts modify early retrieval of specific psycholinguistic information in occipitotemporal cortex, an area likely to contribute to early stages of visual word processing. Using a parametric approach, we analyzed the spatiotemporal response patterns of occipitotemporal cortex for orthographic, lexical, and semantic variables in three psycholinguistic tasks: silent reading, lexical decision, and semantic decision. Task modulation of word frequency and imageability effects occurred simultaneously in ventral occipitotemporal regions-in the vicinity of the putative visual word form area-around 160 msec, following task effects on orthographic typicality around 100 msec. Frequency and typicality also produced task-independent effects in anterior temporal lobe regions after 200 msec. The early task modulation for several specific psycholinguistic variables indicates that occipitotemporal areas integrate perceptual input with prior knowledge in a task-dependent manner. Still, later task-independent effects in anterior temporal lobes suggest that word recognition eventually leads to retrieval of semantic information irrespective of task demands. We conclude that even a highly overlearned visual task like word recognition should be described as flexible rather than automatic.

[Registration and 3D rendering of serial tissue section images].

PubMed

Liu, Zhexing; Jiang, Guiping; Dong, Wu; Zhang, Yu; Xie, Xiaomian; Hao, Liwei; Wang, Zhiyuan; Li, Shuxiang

2002-12-01

It is an important morphological research method to reconstruct the 3D imaging from serial section tissue images. Registration of serial images is a key step to 3D reconstruction. Firstly, an introduction to the segmentation-counting registration algorithm is presented, which is based on the joint histogram. After thresholding of the two images to be registered, the criterion function is defined as counting in a specific region of the joint histogram, which greatly speeds up the alignment process. Then, the method is used to conduct the serial tissue image matching task, and lies a solid foundation for 3D rendering. Finally, preliminary surface rendering results are presented.

Functional and Neuroanatomical Specificity of Episodic Memory Dysfunction in Schizophrenia: An fMRI study of the Relational and Item-Specific Encoding Task

PubMed Central

Ragland, J. Daniel; Ranganath, Charan; Harms, Michael P.; Barch, Deanna M.; Gold, James M.; Layher, Evan; Lesh, Tyler A.; MacDonald, Angus W.; Niendam, Tara A.; Phillips, Joshua; Silverstein, Steven M.; Yonelinas, Andrew P.; Carter, Cameron S.

2015-01-01

Importance Individuals with schizophrenia (SZ) can encode item-specific information to support familiarity-based recognition, but are disproportionately impaired encoding inter-item relationships (relational encoding) and recollecting information. The Relational and Item-Specific Encoding (RiSE) paradigm has been used to disentangle these encoding and retrieval processes, which may be dependent on specific medial temporal lobe (MTL) and prefrontal cortex (PFC) subregions. Functional imaging during RiSE task performance could help to specify dysfunctional neural circuits in SZ that can be targeted for interventions to improve memory and functioning in the illness. Objectives To use functional magnetic resonance imaging (fMRI) to test the hypothesis that SZ disproportionately affects MTL and PFC subregions during relational encoding and retrieval, relative to item-specific memory processes. Imaging results from healthy comparison subjects (HC) will also be used to establish neural construct validity for RiSE. Design, Setting, and Participants This multi-site, case-control, cross-sectional fMRI study was conducted at five CNTRACS sites. The final sample included 52 clinically stable outpatients with SZ, and 57 demographically matched HC. Main Outcomes and Measures Behavioral performance speed and accuracy (d’) on item recognition and associative recognition tasks. Voxelwise statistical parametric maps for a priori MTL and PFC regions of interest (ROI), testing activation differences between relational and item-specific memory during encoding and retrieval. Results Item recognition was disproportionately impaired in SZ patients relative to controls following relational encoding. The differential deficit was accompanied by reduced dorsolateral prefrontal cortex (DLPFC) activation during relational encoding in SZ, relative to HC. Retrieval success (hits > misses) was associated with hippocampal (HI) activation in HC during relational item recognition and associative recognition conditions, and HI activation was specifically reduced in SZ for recognition of relational but not item-specific information. Conclusions In this unique, multi-site fMRI study, HC results supported RiSE construct validity by revealing expected memory effects in PFC and MTL subregions during encoding and retrieval. Comparison of SZ and HC revealed disproportionate memory deficits in SZ for relational versus item-specific information, accompanied by regionally and functionally specific deficits in DLPFC and HI activation. PMID:26200928

Metadata requirements for results of diagnostic imaging procedures: a BIIF profile to support user applications

NASA Astrophysics Data System (ADS)

Brown, Nicholas J.; Lloyd, David S.; Reynolds, Melvin I.; Plummer, David L.

2002-05-01

A visible digital image is rendered from a set of digital image data. Medical digital image data can be stored as either: (a) pre-rendered format, corresponding to a photographic print, or (b) un-rendered format, corresponding to a photographic negative. The appropriate image data storage format and associated header data (metadata) required by a user of the results of a diagnostic procedure recorded electronically depends on the task(s) to be performed. The DICOM standard provides a rich set of metadata that supports the needs of complex applications. Many end user applications, such as simple report text viewing and display of a selected image, are not so demanding and generic image formats such as JPEG are sometimes used. However, these are lacking some basic identification requirements. In this paper we make specific proposals for minimal extensions to generic image metadata of value in various domains, which enable safe use in the case of two simple healthcare end user scenarios: (a) viewing of text and a selected JPEG image activated by a hyperlink and (b) viewing of one or more JPEG images together with superimposed text and graphics annotation using a file specified by a profile of the ISO/IEC Basic Image Interchange Format (BIIF).

Effective color design for displays

NASA Astrophysics Data System (ADS)

MacDonald, Lindsay W.

2002-06-01

Visual communication is a key aspect of human-computer interaction, which contributes to the satisfaction of user and application needs. For effective design of presentations on computer displays, color should be used in conjunction with the other visual variables. The general needs of graphic user interfaces are discussed, followed by five specific tasks with differing criteria for display color specification - advertising, text, information, visualization and imaging.

Modality-Specific Imagery Reduces Cravings for Food: An Application of the Elaborated Intrusion Theory of Desire to Food Craving

ERIC Educational Resources Information Center

Kemps, Eva; Tiggemann, Marika

2007-01-01

Based on converging evidence that visual and olfactory images are key components of food cravings, the authors tested a central prediction of the elaborated intrusion theory of desire, that mutual competition between modality-specific tasks and desire-related imagery can suppress such cravings. In each of Experiments 1 and 2, 90 undergraduate…

Reward Activates Stimulus-Specific and Task-Dependent Representations in Visual Association Cortices

PubMed Central

Muller, Timothy; Yeung, Nick; Waszak, Florian

2014-01-01

Humans reliably learn which actions lead to rewards. One prominent question is how credit is assigned to environmental stimuli that are acted upon. Recent functional magnetic resonance imaging (fMRI) studies have provided evidence that representations of rewarded stimuli are activated upon reward delivery, providing possible eligibility traces for credit assignment. Our study sought evidence of postreward activation in sensory cortices satisfying two conditions of instrumental learning: postreward activity should reflect the stimulus category that preceded reward (stimulus specificity), and should occur only if the stimulus was acted on to obtain reward (task dependency). Our experiment implemented two tasks in the fMRI scanner. The first was a perceptual decision-making task on degraded face and house stimuli. Stimulus specificity was evident as rewards activated the sensory cortices associated with face versus house perception more strongly after face versus house decisions, respectively, particularly in the fusiform face area. Stimulus specificity was further evident in a psychophysiological interaction analysis wherein face-sensitive areas correlated with nucleus accumbens activity after face-decision rewards, whereas house-sensitive areas correlated with nucleus accumbens activity after house-decision rewards. The second task required participants to make an instructed response. The criterion of task dependency was fulfilled as rewards after face versus house responses activated the respective association cortices to a larger degree when faces and houses were relevant to the performed task. Our study is the first to show that postreward sensory cortex activity meets these two key criteria of credit assignment, and does so independently from bottom-up perceptual processing. PMID:25411489

The Neural Basis of Typewriting: A Functional MRI Study.

PubMed

Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

2015-01-01

To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

The Neural Basis of Typewriting: A Functional MRI Study

PubMed Central

Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

2015-01-01

To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner’s area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting. PMID:26218431

Minimization of annotation work: diagnosis of mammographic masses via active learning

NASA Astrophysics Data System (ADS)

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-06-01

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

Minimization of annotation work: diagnosis of mammographic masses via active learning.

PubMed

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-05-22

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

Center for Innovative Minimally Invasive Therapy

DTIC Science & Technology

1999-11-01

discrete layers within the image. In vivo OCT image of a stent deployed in a swine coronary artery. Shadowing of the metallic stent is seen as areas of...Task 3: Vascular Stent -Grafts Specific Aim 1: Develop novel procedures for the treatment of aneurysms and vascular trauma using percutaneous...applied to coronary anastomoses in chronic studies. One particularly interesting application may be as an external stent to maintain or increase the

Multifunctional PSCA antibody fragments for PET and optical prostate cancer imaging

DTIC Science & Technology

2017-10-01

INVESTIGATOR: Anna M. Wu CONTRACTING ORGANIZATION: University of California, Los Angeles Los Angeles, CA 90095-1406 REPORT DATE : October 2017 TYPE OF...cys- minibodies and cys-diabodies) can be labeled with radioisotopes for non-invasive PET imaging for use at multiple points in the prostate cancer...optimize and test multifunctional, F-18, and alternatively labeled fragments Major Task 3. New technologies: alternative site-specific labeling methods

Impairment of visuospatial/visuoconstructional skills in multiple sclerosis patients: the correlation with regional lesion load and subcortical atrophy.

PubMed

Marasescu, R; Cerezo Garcia, M; Aladro Benito, Y

2016-04-01

About 20% to 26% of patients with multiple sclerosis (MS) show alterations in visuospatial/visuoconstructive (VS-VC) skills even though temporo-parieto-occipital impairment is a frequent finding in magnetic resonance imaging. No studies have specifically analysed the relationship between these functions and lesion volume (LV) in these specific brain areas. To evaluate the relationship between VS-VC impairment and magnetic resonance imaging temporo-parieto-occipital LV with subcortical atrophy in patients with MS. Of 100 MS patients undergoing a routine neuropsychological evaluation, 21 were selected because they displayed VS-VC impairments in the following tests: Incomplete picture, Block design (WAIS-III), and Rey-Osterrieth complex figure test. We also selected 13 MS patients without cognitive impairment (control group). Regional LV was measured in FLAIR and T1-weighted images using a semiautomated method; subcortical atrophy was measured by bicaudate ratio and third ventricle width. Partial correlations (controlling for age and years of school) and linear regression analysis were employed to analyse correlations between magnetic resonance imaging parameters and cognitive performance. All measures of LV and brain atrophy were significantly higher in patients with cognitive impairment. Regional LV, bicaudate ratio, and third ventricle width are significantly and inversely correlated with cognitive performance; the strongest correlation was between third ventricle width and VC performance (Block design: P=.001; Rey-Osterrieth complex figure: P<.000). In the multivariate analysis, third ventricle width only had a significant effect on performance of VC tasks (Block design: P=.000; Rey-Osterrieth complex figure: P=.000), and regional FLAIR VL was linked to the VS task (Incomplete picture; P=.002). Measures of subcortical atrophy explain the variations in performance on visuocostructive tasks, and regional FLAIR VL measures are linked to VS tasks. Copyright © 2015 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

Mobile C-arm cone-beam CT for guidance of spine surgery: Image quality, radiation dose, and integration with interventional guidance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schafer, S.; Nithiananthan, S.; Mirota, D. J.

Purpose: A flat-panel detector based mobile isocentric C-arm for cone-beam CT (CBCT) has been developed to allow intraoperative 3D imaging with sub-millimeter spatial resolution and soft-tissue visibility. Image quality and radiation dose were evaluated in spinal surgery, commonly relying on lower-performance image intensifier based mobile C-arms. Scan protocols were developed for task-specific imaging at minimum dose, in-room exposure was evaluated, and integration of the imaging system with a surgical guidance system was demonstrated in preclinical studies of minimally invasive spine surgery. Methods: Radiation dose was assessed as a function of kilovolt (peak) (80-120 kVp) and milliampere second using thoracic andmore » lumbar spine dosimetry phantoms. In-room radiation exposure was measured throughout the operating room for various CBCT scan protocols. Image quality was assessed using tissue-equivalent inserts in chest and abdomen phantoms to evaluate bone and soft-tissue contrast-to-noise ratio as a function of dose, and task-specific protocols (i.e., visualization of bone or soft-tissues) were defined. Results were applied in preclinical studies using a cadaveric torso simulating minimally invasive, transpedicular surgery. Results: Task-specific CBCT protocols identified include: thoracic bone visualization (100 kVp; 60 mAs; 1.8 mGy); lumbar bone visualization (100 kVp; 130 mAs; 3.2 mGy); thoracic soft-tissue visualization (100 kVp; 230 mAs; 4.3 mGy); and lumbar soft-tissue visualization (120 kVp; 460 mAs; 10.6 mGy) - each at (0.3 x 0.3 x 0.9 mm{sup 3}) voxel size. Alternative lower-dose, lower-resolution soft-tissue visualization protocols were identified (100 kVp; 230 mAs; 5.1 mGy) for the lumbar region at (0.3 x 0.3 x 1.5 mm{sup 3}) voxel size. Half-scan orbit of the C-arm (x-ray tube traversing under the table) was dosimetrically advantageous (prepatient attenuation) with a nonuniform dose distribution ({approx}2 x higher at the entrance side than at isocenter, and {approx}3-4 lower at the exit side). The in-room dose (microsievert) per unit scan dose (milligray) ranged from {approx}21 {mu}Sv/mGy on average at tableside to {approx}0.1 {mu}Sv/mGy at 2.0 m distance to isocenter. All protocols involve surgical staff stepping behind a shield wall for each CBCT scan, therefore imparting {approx}zero dose to staff. Protocol implementation in preclinical cadaveric studies demonstrate integration of the C-arm with a navigation system for spine surgery guidance-specifically, minimally invasive vertebroplasty in which the system provided accurate guidance and visualization of needle placement and bone cement distribution. Cumulative dose including multiple intraoperative scans was {approx}11.5 mGy for CBCT-guided thoracic vertebroplasty and {approx}23.2 mGy for lumbar vertebroplasty, with dose to staff at tableside reduced to {approx}1 min of fluoroscopy time ({approx}40-60 {mu}Sv), compared to 5-11 min for the conventional approach. Conclusions: Intraoperative CBCT using a high-performance mobile C-arm prototype demonstrates image quality suitable to guidance of spine surgery, with task-specific protocols providing an important basis for minimizing radiation dose, while maintaining image quality sufficient for surgical guidance. Images demonstrate a significant advance in spatial resolution and soft-tissue visibility, and CBCT guidance offers the potential to reduce fluoroscopy reliance, reducing cumulative dose to patient and staff. Integration with a surgical guidance system demonstrates precise tracking and visualization in up-to-date images (alleviating reliance on preoperative images only), including detection of errors or suboptimal surgical outcomes in the operating room.« less

Fine-grained recognition of plants from images.

PubMed

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

Face identity matching is selectively impaired in developmental prosopagnosia.

PubMed

Fisher, Katie; Towler, John; Eimer, Martin

2017-04-01

Individuals with developmental prosopagnosia (DP) have severe face recognition deficits, but the mechanisms that are responsible for these deficits have not yet been fully identified. We assessed whether the activation of visual working memory for individual faces is selectively impaired in DP. Twelve DPs and twelve age-matched control participants were tested in a task where they reported whether successively presented faces showed the same or two different individuals, and another task where they judged whether the faces showed the same or different facial expressions. Repetitions versus changes of the other currently irrelevant attribute were varied independently. DPs showed impaired performance in the identity task, but performed at the same level as controls in the expression task. An electrophysiological marker for the activation of visual face memory by identity matches (N250r component) was strongly attenuated in the DP group, and the size of this attenuation was correlated with poor performance in a standardized face recognition test. Results demonstrate an identity-specific deficit of visual face memory in DPs. Their reduced sensitivity to identity matches in the presence of other image changes could result from earlier deficits in the perceptual extraction of image-invariant visual identity cues from face images. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

MoDOT pavement preservation research program volume V, site-specific pavement condition assessment.

DOT National Transportation Integrated Search

2015-11-01

The overall objective of Task 4 was to thoroughly assess the cost-effectiveness and utility of selected non-invasive technologies as : applicable to MoDOT roadways. Non-invasive imaging technologies investigated in this project were Ultrasonic Surfac...

High Speed Digital Camera Technology Review

NASA Technical Reports Server (NTRS)

Clements, Sandra D.

2009-01-01

A High Speed Digital Camera Technology Review (HSD Review) is being conducted to evaluate the state-of-the-shelf in this rapidly progressing industry. Five HSD cameras supplied by four camera manufacturers participated in a Field Test during the Space Shuttle Discovery STS-128 launch. Each camera was also subjected to Bench Tests in the ASRC Imaging Development Laboratory. Evaluation of the data from the Field and Bench Tests is underway. Representatives from the imaging communities at NASA / KSC and the Optical Systems Group are participating as reviewers. A High Speed Digital Video Camera Draft Specification was updated to address Shuttle engineering imagery requirements based on findings from this HSD Review. This draft specification will serve as the template for a High Speed Digital Video Camera Specification to be developed for the wider OSG imaging community under OSG Task OS-33.

Motion tracking and electromyography assist the removal of mirror hand contributions to fNIRS images acquired during a finger tapping task performed by children with cerebral palsy

NASA Astrophysics Data System (ADS)

Hervey, Nathan; Khan, Bilal; Shagman, Laura; Tian, Fenghua; Delgado, Mauricio R.; Tulchin-Francis, Kirsten; Shierk, Angela; Smith, Linsley; Reid, Dahlia; Clegg, Nancy J.; Liu, Hanli; MacFarlane, Duncan; Alexandrakis, George

2013-03-01

Functional neurological imaging has been shown to be valuable in evaluating brain plasticity in children with cerebral palsy (CP). In recent studies it has been demonstrated that functional near-infrared spectroscopy (fNIRS) is a viable and sensitive method for imaging motor cortex activities in children with CP. However, during unilateral finger tapping tasks children with CP often exhibit mirror motions (unintended motions in the non-tapping hand), and current fNIRS image formation techniques do not account for this. Therefore, the resulting fNIRS images contain activation from intended and unintended motions. In this study, cortical activity was mapped with fNIRS on four children with CP and five controls during a finger tapping task. Finger motion and arm muscle activation were concurrently measured using motion tracking cameras and electromyography (EMG). Subject-specific regressors were created from motion capture and EMG data and used in a general linear model (GLM) analysis in an attempt to create fNIRS images representative of different motions. The analysis provided an fNIRS image representing activation due to motion and muscle activity for each hand. This method could prove to be valuable in monitoring brain plasticity in children with CP by providing more consistent images between measurements. Additionally, muscle effort versus cortical effort was compared between control and CP subjects. More cortical effort was required to produce similar muscle effort in children with CP. It is possible this metric could be a valuable diagnostic tool in determining response to treatment.

PyDBS: an automated image processing workflow for deep brain stimulation surgery.

PubMed

D'Albis, Tiziano; Haegelen, Claire; Essert, Caroline; Fernández-Vidal, Sara; Lalys, Florent; Jannin, Pierre

2015-02-01

Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery. PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases. The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient. The results obtained are compatible with the adoption of PyDBS in clinical practice.

Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

NASA Astrophysics Data System (ADS)

Li, Chongyi; Guo, Jichang; Guo, Chunle

2018-03-01

Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of vision tasks. Different from existing methods which either ignore the wavelength dependency of the attenuation or assume a specific spectral profile, we tackle color distortion problem of underwater image from a new view. In this letter, we propose a weakly supervised color transfer method to correct color distortion, which relaxes the need of paired underwater images for training and allows for the underwater images unknown where were taken. Inspired by Cycle-Consistent Adversarial Networks, we design a multi-term loss function including adversarial loss, cycle consistency loss, and SSIM (Structural Similarity Index Measure) loss, which allows the content and structure of the corrected result the same as the input, but the color as if the image was taken without the water. Experiments on underwater images captured under diverse scenes show that our method produces visually pleasing results, even outperforms the art-of-the-state methods. Besides, our method can improve the performance of vision tasks.

Impact of topographic mask models on scanner matching solutions

NASA Astrophysics Data System (ADS)

Tyminski, Jacek K.; Pomplun, Jan; Renwick, Stephen P.

2014-03-01

Of keen interest to the IC industry are advanced computational lithography applications such as Optical Proximity Correction of IC layouts (OPC), scanner matching by optical proximity effect matching (OPEM), and Source Optimization (SO) and Source-Mask Optimization (SMO) used as advanced reticle enhancement techniques. The success of these tasks is strongly dependent on the integrity of the lithographic simulators used in computational lithography (CL) optimizers. Lithographic mask models used by these simulators are key drivers impacting the accuracy of the image predications, and as a consequence, determine the validity of these CL solutions. Much of the CL work involves Kirchhoff mask models, a.k.a. thin masks approximation, simplifying the treatment of the mask near-field images. On the other hand, imaging models for hyper-NA scanner require that the interactions of the illumination fields with the mask topography be rigorously accounted for, by numerically solving Maxwell's Equations. The simulators used to predict the image formation in the hyper-NA scanners must rigorously treat the masks topography and its interaction with the scanner illuminators. Such imaging models come at a high computational cost and pose challenging accuracy vs. compute time tradeoffs. Additional complication comes from the fact that the performance metrics used in computational lithography tasks show highly non-linear response to the optimization parameters. Finally, the number of patterns used for tasks such as OPC, OPEM, SO, or SMO range from tens to hundreds. These requirements determine the complexity and the workload of the lithography optimization tasks. The tools to build rigorous imaging optimizers based on first-principles governing imaging in scanners are available, but the quantifiable benefits they might provide are not very well understood. To quantify the performance of OPE matching solutions, we have compared the results of various imaging optimization trials obtained with Kirchhoff mask models to those obtained with rigorous models involving solutions of Maxwell's Equations. In both sets of trials, we used sets of large numbers of patterns, with specifications representative of CL tasks commonly encountered in hyper-NA imaging. In this report we present OPEM solutions based on various mask models and discuss the models' impact on hyper- NA scanner matching accuracy. We draw conclusions on the accuracy of results obtained with thin mask models vs. the topographic OPEM solutions. We present various examples representative of the scanner image matching for patterns representative of the current generation of IC designs.

Examination of Cognitive Fatigue in Multiple Sclerosis using Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging

PubMed Central

Genova, Helen M.; Rajagopalan, Venkateswaran; DeLuca, John; Das, Abhijit; Binder, Allison; Arjunan, Aparna; Chiaravalloti, Nancy; Wylie, Glenn

2013-01-01

The present study investigated the neural correlates of cognitive fatigue in Multiple Sclerosis (MS), looking specifically at the relationship between self-reported fatigue and objective measures of cognitive fatigue. In Experiment 1, functional magnetic resonance imaging (fMRI) was used to examine where in the brain BOLD activity covaried with “state” fatigue, assessed during performance of a task designed to induce cognitive fatigue while in the scanner. In Experiment 2, diffusion tensor imaging (DTI) was used to examine where in the brain white matter damage correlated with increased “trait” fatigue in individuals with MS, assessed by the Fatigue Severity Scale (FSS) completed outside the scanning session. During the cognitively fatiguing task, the MS group had increased brain activity associated with fatigue in the caudate as compared with HCs. DTI findings revealed that reduced fractional anisotropy in the anterior internal capsule was associated with increased self-reported fatigue on the FSS. Results are discussed in terms of identifying a “fatigue-network” in MS. PMID:24223850

Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging.

PubMed

Genova, Helen M; Rajagopalan, Venkateswaran; Deluca, John; Das, Abhijit; Binder, Allison; Arjunan, Aparna; Chiaravalloti, Nancy; Wylie, Glenn

2013-01-01

The present study investigated the neural correlates of cognitive fatigue in Multiple Sclerosis (MS), looking specifically at the relationship between self-reported fatigue and objective measures of cognitive fatigue. In Experiment 1, functional magnetic resonance imaging (fMRI) was used to examine where in the brain BOLD activity covaried with "state" fatigue, assessed during performance of a task designed to induce cognitive fatigue while in the scanner. In Experiment 2, diffusion tensor imaging (DTI) was used to examine where in the brain white matter damage correlated with increased "trait" fatigue in individuals with MS, assessed by the Fatigue Severity Scale (FSS) completed outside the scanning session. During the cognitively fatiguing task, the MS group had increased brain activity associated with fatigue in the caudate as compared with HCs. DTI findings revealed that reduced fractional anisotropy in the anterior internal capsule was associated with increased self-reported fatigue on the FSS. Results are discussed in terms of identifying a "fatigue-network" in MS.

Face-n-Food: Gender Differences in Tuning to Faces.

PubMed

Pavlova, Marina A; Scheffler, Klaus; Sokolov, Alexander N

2015-01-01

Faces represent valuable signals for social cognition and non-verbal communication. A wealth of research indicates that women tend to excel in recognition of facial expressions. However, it remains unclear whether females are better tuned to faces. We presented healthy adult females and males with a set of newly created food-plate images resembling faces (slightly bordering on the Giuseppe Arcimboldo style). In a spontaneous recognition task, participants were shown a set of images in a predetermined order from the least to most resembling a face. Females not only more readily recognized the images as a face (they reported resembling a face on images, on which males still did not), but gave on overall more face responses. The findings are discussed in the light of gender differences in deficient face perception. As most neuropsychiatric, neurodevelopmental and psychosomatic disorders characterized by social brain abnormalities are sex specific, the task may serve as a valuable tool for uncovering impairments in visual face processing.

Face-n-Food: Gender Differences in Tuning to Faces

PubMed Central

Pavlova, Marina A.; Scheffler, Klaus; Sokolov, Alexander N.

2015-01-01

Faces represent valuable signals for social cognition and non-verbal communication. A wealth of research indicates that women tend to excel in recognition of facial expressions. However, it remains unclear whether females are better tuned to faces. We presented healthy adult females and males with a set of newly created food-plate images resembling faces (slightly bordering on the Giuseppe Arcimboldo style). In a spontaneous recognition task, participants were shown a set of images in a predetermined order from the least to most resembling a face. Females not only more readily recognized the images as a face (they reported resembling a face on images, on which males still did not), but gave on overall more face responses. The findings are discussed in the light of gender differences in deficient face perception. As most neuropsychiatric, neurodevelopmental and psychosomatic disorders characterized by social brain abnormalities are sex specific, the task may serve as a valuable tool for uncovering impairments in visual face processing. PMID:26154177

Computer vision applications for coronagraphic optical alignment and image processing.

PubMed

Savransky, Dmitry; Thomas, Sandrine J; Poyneer, Lisa A; Macintosh, Bruce A

2013-05-10

Modern coronagraphic systems require very precise alignment between optical components and can benefit greatly from automated image processing. We discuss three techniques commonly employed in the fields of computer vision and image analysis as applied to the Gemini Planet Imager, a new facility instrument for the Gemini South Observatory. We describe how feature extraction and clustering methods can be used to aid in automated system alignment tasks, and also present a search algorithm for finding regular features in science images used for calibration and data processing. Along with discussions of each technique, we present our specific implementation and show results of each one in operation.

Tutte polynomial in functional magnetic resonance imaging

NASA Astrophysics Data System (ADS)

García-Castillón, Marlly V.

2015-09-01

Methods of graph theory are applied to the processing of functional magnetic resonance images. Specifically the Tutte polynomial is used to analyze such kind of images. Functional Magnetic Resonance Imaging provide us connectivity networks in the brain which are represented by graphs and the Tutte polynomial will be applied. The problem of computing the Tutte polynomial for a given graph is #P-hard even for planar graphs. For a practical application the maple packages "GraphTheory" and "SpecialGraphs" will be used. We will consider certain diagram which is depicting functional connectivity, specifically between frontal and posterior areas, in autism during an inferential text comprehension task. The Tutte polynomial for the resulting neural networks will be computed and some numerical invariants for such network will be obtained. Our results show that the Tutte polynomial is a powerful tool to analyze and characterize the networks obtained from functional magnetic resonance imaging.

Sleep stages, memory and learning.

PubMed Central

Dotto, L

1996-01-01

Learning and memory can be impaired by sleep loss during specific vulnerable "windows" for several days after new tasks have been learned. Different types of tasks are differentially vulnerable to the loss of different stages of sleep. Memory required to perform cognitive procedural tasks is affected by the loss of rapid-eye-movement (REM) sleep on the first night after learning occurs and again on the third night after learning. REM-sleep deprivation on the second night after learning does not produce memory deficits. Declarative memory, which is used for the recall of specific facts, is not similarly affected by REM-sleep loss. The learning of procedural motor tasks, including those required in many sports, is impaired by the loss of stage 2 sleep, which occurs primarily in the early hours of the morning. These findings have implications for the academic and athletic performance of students and for anyone whose work involves ongoing learning and demands high standards of performance. Images p1194-a PMID:8612256

Applications of neural networks to landmark detection in 3-D surface data

NASA Astrophysics Data System (ADS)

Arndt, Craig M.

1992-09-01

The problem of identifying key landmarks in 3-dimensional surface data is of considerable interest in solving a number of difficult real-world tasks, including object recognition and image processing. The specific problem that we address in this research is to identify the specific landmarks (anatomical) in human surface data. This is a complex task, currently performed visually by an expert human operator. In order to replace these human operators and increase reliability of the data acquisition, we need to develop a computer algorithm which will utilize the interrelations between the 3-dimensional data to identify the landmarks of interest. The current presentation describes a method for designing, implementing, training, and testing a custom architecture neural network which will perform the landmark identification task. We discuss the performance of the net in relationship to human performance on the same task and how this net has been integrated with other AI and traditional programming methods to produce a powerful analysis tool for computer anthropometry.

OsiriX: an open-source software for navigating in multidimensional DICOM images.

PubMed

Rosset, Antoine; Spadola, Luca; Ratib, Osman

2004-09-01

A multidimensional image navigation and display software was designed for display and interpretation of large sets of multidimensional and multimodality images such as combined PET-CT studies. The software is developed in Objective-C on a Macintosh platform under the MacOS X operating system using the GNUstep development environment. It also benefits from the extremely fast and optimized 3D graphic capabilities of the OpenGL graphic standard widely used for computer games optimized for taking advantage of any hardware graphic accelerator boards available. In the design of the software special attention was given to adapt the user interface to the specific and complex tasks of navigating through large sets of image data. An interactive jog-wheel device widely used in the video and movie industry was implemented to allow users to navigate in the different dimensions of an image set much faster than with a traditional mouse or on-screen cursors and sliders. The program can easily be adapted for very specific tasks that require a limited number of functions, by adding and removing tools from the program's toolbar and avoiding an overwhelming number of unnecessary tools and functions. The processing and image rendering tools of the software are based on the open-source libraries ITK and VTK. This ensures that all new developments in image processing that could emerge from other academic institutions using these libraries can be directly ported to the OsiriX program. OsiriX is provided free of charge under the GNU open-source licensing agreement at http://homepage.mac.com/rossetantoine/osirix.

Assessment of image quality in soft tissue and bone visualization tasks for a dedicated extremity cone-beam CT system.

PubMed

Demehri, S; Muhit, A; Zbijewski, W; Stayman, J W; Yorkston, J; Packard, N; Senn, R; Yang, D; Foos, D; Thawait, G K; Fayad, L M; Chhabra, A; Carrino, J A; Siewerdsen, J H

2015-06-01

To assess visualization tasks using cone-beam CT (CBCT) compared to multi-detector CT (MDCT) for musculoskeletal extremity imaging. Ten cadaveric hands and ten knees were examined using a dedicated CBCT prototype and a clinical multi-detector CT using nominal protocols (80 kVp-108mAs for CBCT; 120 kVp- 300 mAs for MDCT). Soft tissue and bone visualization tasks were assessed by four radiologists using five-point satisfaction (for CBCT and MDCT individually) and five-point preference (side-by-side CBCT versus MDCT image quality comparison) rating tests. Ratings were analyzed using Kruskal-Wallis and Wilcoxon signed-rank tests, and observer agreement was assessed using the Kappa-statistic. Knee CBCT images were rated "excellent" or "good" (median scores 5 and 4) for "bone" and "soft tissue" visualization tasks. Hand CBCT images were rated "excellent" or "adequate" (median scores 5 and 3) for "bone" and "soft tissue" visualization tasks. Preference tests rated CBCT equivalent or superior to MDCT for bone visualization and favoured the MDCT for soft tissue visualization tasks. Intraobserver agreement for CBCT satisfaction tests was fair to almost perfect (κ ~ 0.26-0.92), and interobserver agreement was fair to moderate (κ ~ 0.27-0.54). CBCT provided excellent image quality for bone visualization and adequate image quality for soft tissue visualization tasks. • CBCT provided adequate image quality for diagnostic tasks in extremity imaging. • CBCT images were "excellent" for "bone" and "good/adequate" for "soft tissue" visualization tasks. • CBCT image quality was equivalent/superior to MDCT for bone visualization tasks.

Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

NASA Astrophysics Data System (ADS)

Zhou, Weifeng; Cai, Jian-Feng; Gao, Hao

2013-12-01

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l1-regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method.

Biometric identification

NASA Astrophysics Data System (ADS)

Syryamkim, V. I.; Kuznetsov, D. N.; Kuznetsova, A. S.

2018-05-01

Image recognition is an information process implemented by some information converter (intelligent information channel, recognition system) having input and output. The input of the system is fed with information about the characteristics of the objects being presented. The output of the system displays information about which classes (generalized images) the recognized objects are assigned to. When creating and operating an automated system for pattern recognition, a number of problems are solved, while for different authors the formulations of these tasks, and the set itself, do not coincide, since it depends to a certain extent on the specific mathematical model on which this or that recognition system is based. This is the task of formalizing the domain, forming a training sample, learning the recognition system, reducing the dimensionality of space.

78 FR 14797 - Findings of Research Misconduct

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-07

...) ``Incentive Induced Changes in Neural Patterns During Task-Switching.'' Organization for Human Brain Mapping... categories in Figure 9. 3. Falsified data in J Neurosci. 2010 and mislabeled brain images to show that... brain regions, behavioral performance, and trial outcomes. Specifically, Respondent modified the data so...

Automated simultaneous multiple feature classification of MTI data

NASA Astrophysics Data System (ADS)

Harvey, Neal R.; Theiler, James P.; Balick, Lee K.; Pope, Paul A.; Szymanski, John J.; Perkins, Simon J.; Porter, Reid B.; Brumby, Steven P.; Bloch, Jeffrey J.; David, Nancy A.; Galassi, Mark C.

2002-08-01

Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.

Using Highlighting to Train Attentional Expertise

PubMed Central

Roads, Brett; Mozer, Michael C.; Busey, Thomas A.

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure. PMID:26744839

Using Highlighting to Train Attentional Expertise.

PubMed

Roads, Brett; Mozer, Michael C; Busey, Thomas A

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure.

Adaptive illumination source for multispectral vision system applied to material discrimination

NASA Astrophysics Data System (ADS)

Conde, Olga M.; Cobo, Adolfo; Cantero, Paulino; Conde, David; Mirapeix, Jesús; Cubillas, Ana M.; López-Higuera, José M.

2008-04-01

A multispectral system based on a monochrome camera and an adaptive illumination source is presented in this paper. Its preliminary application is focused on material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach, in which the relevant wavelengths for the required discrimination task are selected in advance using a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based on Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illuminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illumination produces multispectral imaging with a simple monochrome camera, and cold illumination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry.

Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex.

PubMed

Ulloa, Antonio; Horwitz, Barry

2016-01-01

A number of recent efforts have used large-scale, biologically realistic, neural models to help understand the neural basis for the patterns of activity observed in both resting state and task-related functional neural imaging data. An example of the former is The Virtual Brain (TVB) software platform, which allows one to apply large-scale neural modeling in a whole brain framework. TVB provides a set of structural connectomes of the human cerebral cortex, a collection of neural processing units for each connectome node, and various forward models that can convert simulated neural activity into a variety of functional brain imaging signals. In this paper, we demonstrate how to embed a previously or newly constructed task-based large-scale neural model into the TVB platform. We tested our method on a previously constructed large-scale neural model (LSNM) of visual object processing that consisted of interconnected neural populations that represent, primary and secondary visual, inferotemporal, and prefrontal cortex. Some neural elements in the original model were "non-task-specific" (NS) neurons that served as noise generators to "task-specific" neurons that processed shapes during a delayed match-to-sample (DMS) task. We replaced the NS neurons with an anatomical TVB connectome model of the cerebral cortex comprising 998 regions of interest interconnected by white matter fiber tract weights. We embedded our LSNM of visual object processing into corresponding nodes within the TVB connectome. Reciprocal connections between TVB nodes and our task-based modules were included in this framework. We ran visual object processing simulations and showed that the TVB simulator successfully replaced the noise generation originally provided by NS neurons; i.e., the DMS tasks performed with the hybrid LSNM/TVB simulator generated equivalent neural and fMRI activity to that of the original task-based models. Additionally, we found partial agreement between the functional connectivities using the hybrid LSNM/TVB model and the original LSNM. Our framework thus presents a way to embed task-based neural models into the TVB platform, enabling a better comparison between empirical and computational data, which in turn can lead to a better understanding of how interacting neural populations give rise to human cognitive behaviors.

Impact of voxel size variation on CBCT-based diagnostic outcome in dentistry: a systematic review.

PubMed

Spin-Neto, Rubens; Gotfredsen, Erik; Wenzel, Ann

2013-08-01

The objective of this study was to make a systematic review on the impact of voxel size in cone beam computed tomography (CBCT)-based image acquisition, retrieving evidence regarding the diagnostic outcome of those images. The MEDLINE bibliographic database was searched from 1950 to June 2012 for reports comparing diverse CBCT voxel sizes. The search strategy was limited to English-language publications using the following combined terms in the search strategy: (voxel or FOV or field of view or resolution) and (CBCT or cone beam CT). The results from the review identified 20 publications that qualitatively or quantitatively assessed the influence of voxel size on CBCT-based diagnostic outcome, and in which the methodology/results comprised at least one of the expected parameters (image acquisition, reconstruction protocols, type of diagnostic task, and presence of a gold standard). The diagnostic task assessed in the studies was diverse, including the detection of root fractures, the detection of caries lesions, and accuracy of 3D surface reconstruction and of bony measurements, among others. From the studies assessed, it is clear that no general protocol can be yet defined for CBCT examination of specific diagnostic tasks in dentistry. Rationale in this direction is an important step to define the utility of CBCT imaging.

Divided versus selective attention: evidence for common processing mechanisms.

PubMed

Hahn, Britta; Wolkenberg, Frank A; Ross, Thomas J; Myers, Carol S; Heishman, Stephen J; Stein, Dan J; Kurup, Pradeep K; Stein, Elliot A

2008-06-18

The current study revisited the question of whether there are brain mechanisms specific to divided attention that differ from those used in selective attention. Increased neuronal activity required to simultaneously process two stimulus dimensions as compared with each separate dimension has often been observed, but rarely has activity induced by a divided attention condition exceeded the sum of activity induced by the component tasks. Healthy participants performed a selective-divided attention paradigm while undergoing functional Magnetic Resonance Imaging (fMRI). The task required participants to make a same-different judgment about either one of two simultaneously presented stimulus dimensions, or about both dimensions. Performance accuracy was equated between tasks by dynamically adjusting the stimulus display time. Blood Oxygenation Level Dependent (BOLD) signal differences between tasks were identified by whole-brain voxel-wise comparisons and by region-specific analyses of all areas modulated by the divided attention task (DIV). No region displayed greater activation or deactivation by DIV than the sum of signal change by the two selective attention tasks. Instead, regional activity followed the tasks' processing demands as reflected by reaction time. Only a left cerebellar region displayed a correlation between participants' BOLD signal intensity and reaction time that was selective for DIV. The correlation was positive, reflecting slower responding with greater activation. Overall, the findings do not support the existence of functional brain activity specific to DIV. Increased activity appears to reflect additional processing demands by introducing a secondary task, but those demands do not appear to qualitatively differ from processes of selective attention.

TU-B-19A-01: Image Registration II: TG132-Quality Assurance for Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brock, K; Mutic, S

2014-06-15

AAPM Task Group 132 was charged with a review of the current approaches and solutions for image registration in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. As the results of image registration are always used as the input of another process for planning or delivery, it is important for the user to understand and document the uncertainty associate with the algorithm in general and the Result of a specific registration. The recommendations of this task group, which at the time of abstract submission are currently being reviewed by the AAPM, include themore » following components. The user should understand the basic image registration techniques and methods of visualizing image fusion. The disclosure of basic components of the image registration by commercial vendors is critical in this respect. The physicists should perform end-to-end tests of imaging, registration, and planning/treatment systems if image registration is performed on a stand-alone system. A comprehensive commissioning process should be performed and documented by the physicist prior to clinical use of the system. As documentation is important to the safe implementation of this process, a request and report system should be integrated into the clinical workflow. Finally, a patient specific QA practice should be established for efficient evaluation of image registration results. The implementation of these recommendations will be described and illustrated during this educational session. Learning Objectives: Highlight the importance of understanding the image registration techniques used in their clinic. Describe the end-to-end tests needed for stand-alone registration systems. Illustrate a comprehensive commissioning program using both phantom data and clinical images. Describe a request and report system to ensure communication and documentation. Demonstrate an clinically-efficient patient QA practice for efficient evaluation of image registration.« less

Earth resources data analysis program, phase 3

NASA Technical Reports Server (NTRS)

1975-01-01

Tasks were performed in two areas: (1) systems analysis and (2) algorithmic development. The major effort in the systems analysis task was the development of a recommended approach to the monitoring of resource utilization data for the Large Area Crop Inventory Experiment (LACIE). Other efforts included participation in various studies concerning the LACIE Project Plan, the utility of the GE Image 100, and the specifications for a special purpose processor to be used in the LACIE. In the second task, the major effort was the development of improved algorithms for estimating proportions of unclassified remotely sensed data. Also, work was performed on optimal feature extraction and optimal feature extraction for proportion estimation.

Task-independent effects are potential confounders in longitudinal imaging studies of learning in schizophrenia.

PubMed

Korostil, Michele; Fatima, Zainab; Kovacevic, Natasha; Menon, Mahesh; McIntosh, Anthony Randal

2016-01-01

Learning impairment is a core deficit in schizophrenia that impacts on real-world functioning and yet, elucidating its underlying neural basis remains a challenge. A key issue when interpreting learning-task experiments is that task-independent changes may confound interpretation of task-related signal changes in neuroimaging studies. The nature of these task-independent changes in schizophrenia is unknown. Therefore, we examined task-independent "time effects" in a group of participants with schizophrenia contrasted with healthy participants in a longitudinal fMRI learning-experiment designed to allow for examination of non-specific effects of time. Flanking the learning portions of the experiment with a task-of-no-interest allowed us to extract task-independent BOLD changes. Task-independent effects occurred in both groups, but were more robust in the schizophrenia group. There was a significant interaction effect between group and time in a distributed activity pattern that included inferior and superior temporal regions, frontal areas (left anterior insula and superior medial gyri), and parietal areas (posterior cingulate cortices and precuneus). This pattern showed task-independent linear decrease in BOLD amplitude over the two scanning sessions for the schizophrenia group, but showed either opposite effect or no activity changes for the control group. There was a trend towards a correlation between task-independent effects and the presence of more negative symptoms in the schizophrenia group. The strong interaction between group and time suggests that both the scanning experience as a whole and the transition between task-types evokes a different response in persons with schizophrenia and may confound interpretation of learning-related longitudinal imaging experiments if not explicitly considered.

Task-independent effects are potential confounders in longitudinal imaging studies of learning in schizophrenia

PubMed Central

Korostil, Michele; Fatima, Zainab; Kovacevic, Natasha; Menon, Mahesh; McIntosh, Anthony Randal

2015-01-01

Learning impairment is a core deficit in schizophrenia that impacts on real-world functioning and yet, elucidating its underlying neural basis remains a challenge. A key issue when interpreting learning-task experiments is that task-independent changes may confound interpretation of task-related signal changes in neuroimaging studies. The nature of these task-independent changes in schizophrenia is unknown. Therefore, we examined task-independent “time effects” in a group of participants with schizophrenia contrasted with healthy participants in a longitudinal fMRI learning-experiment designed to allow for examination of non-specific effects of time. Flanking the learning portions of the experiment with a task-of-no-interest allowed us to extract task-independent BOLD changes. Task-independent effects occurred in both groups, but were more robust in the schizophrenia group. There was a significant interaction effect between group and time in a distributed activity pattern that included inferior and superior temporal regions, frontal areas (left anterior insula and superior medial gyri), and parietal areas (posterior cingulate cortices and precuneus). This pattern showed task-independent linear decrease in BOLD amplitude over the two scanning sessions for the schizophrenia group, but showed either opposite effect or no activity changes for the control group. There was a trend towards a correlation between task-independent effects and the presence of more negative symptoms in the schizophrenia group. The strong interaction between group and time suggests that both the scanning experience as a whole and the transition between task-types evokes a different response in persons with schizophrenia and may confound interpretation of learning-related longitudinal imaging experiments if not explicitly considered. PMID:26759790

A neuronal model of a global workspace in effortful cognitive tasks.

PubMed

Dehaene, S; Kerszberg, M; Changeux, J P

1998-11-24

A minimal hypothesis is proposed concerning the brain processes underlying effortful tasks. It distinguishes two main computational spaces: a unique global workspace composed of distributed and heavily interconnected neurons with long-range axons, and a set of specialized and modular perceptual, motor, memory, evaluative, and attentional processors. Workspace neurons are mobilized in effortful tasks for which the specialized processors do not suffice. They selectively mobilize or suppress, through descending connections, the contribution of specific processor neurons. In the course of task performance, workspace neurons become spontaneously coactivated, forming discrete though variable spatio-temporal patterns subject to modulation by vigilance signals and to selection by reward signals. A computer simulation of the Stroop task shows workspace activation to increase during acquisition of a novel task, effortful execution, and after errors. We outline predictions for spatio-temporal activation patterns during brain imaging, particularly about the contribution of dorsolateral prefrontal cortex and anterior cingulate to the workspace.

Recall and recognition of verbal paired associates in early Alzheimer's disease.

PubMed

Lowndes, G J; Saling, M M; Ames, D; Chiu, E; Gonzalez, L M; Savage, G R

2008-07-01

The primary impairment in early Alzheimer's disease (AD) is encoding/consolidation, resulting from medial temporal lobe (MTL) pathology. AD patients perform poorly on cued-recall paired associate learning (PAL) tasks, which assess the ability of the MTLs to encode relational memory. Since encoding and retrieval processes are confounded within performance indexes on cued-recall PAL, its specificity for AD is limited. Recognition paradigms tend to show good specificity for AD, and are well tolerated, but are typically less sensitive than recall tasks. Associate-recognition is a novel PAL task requiring a combination of recall and recognition processes. We administered a verbal associate-recognition test and cued-recall analogue to 22 early AD patients and 55 elderly controls to compare their ability to discriminate these groups. Both paradigms used eight arbitrarily related word pairs (e.g., pool-teeth) with varying degrees of imageability. Associate-recognition was equally effective as the cued-recall analogue in discriminating the groups, and logistic regression demonstrated classification rates by both tasks were equivalent. These preliminary findings provide support for the clinical value of this recognition tool. Conceptually it has potential for greater specificity in informing neuropsychological diagnosis of AD in clinical samples but this requires further empirical support.

Kinematics of self-initiated and reactive karate punches.

PubMed

Martinez de Quel, Oscar; Bennett, Simon J

2014-03-01

This study investigated whether within-task expertise affects the reported asymmetry in execution time exhibited in reactive and self-initiated movements. Karate practitioners and no-karate practitioners were compared performing a reverse punch in reaction to an external stimulus or following the intention to produce a response (self-initiated). The task was completed following the presentation of a specific (i.e., life-size image of opponent) or general stimulus and in the presence of click trains or white noise. Kinematic analyses indicated reactive movement had shorter time to peak velocity and movement time, as well as greater accuracy than self-initiated movement. These differences were independent of participant skill level although peak velocity was higher in the karate practice group than in the no-karate practice group. Reaction time (RT) of skilled participants was facilitated by a specific stimulus. There was no effect on RT or kinematic variables of the different type of auditory cues. The results of this study indicate that asymmetry in execution time of reactive and self-initiated movement holds irrespective of within-task expertise and stimulus specificity. This could have implications for training of sports and/or relearning of tasks that require rapid and accurate movements to intercept/contact a target.

Auto-SEIA: simultaneous optimization of image processing and machine learning algorithms

NASA Astrophysics Data System (ADS)

Negro Maggio, Valentina; Iocchi, Luca

2015-02-01

Object classification from images is an important task for machine vision and it is a crucial ingredient for many computer vision applications, ranging from security and surveillance to marketing. Image based object classification techniques properly integrate image processing and machine learning (i.e., classification) procedures. In this paper we present a system for automatic simultaneous optimization of algorithms and parameters for object classification from images. More specifically, the proposed system is able to process a dataset of labelled images and to return a best configuration of image processing and classification algorithms and of their parameters with respect to the accuracy of classification. Experiments with real public datasets are used to demonstrate the effectiveness of the developed system.

Control over the strength of connections between modules: a double dissociation between stimulus format and task revealed by Granger causality mapping in fMRI

PubMed Central

Anderson, Britt; Soliman, Sherif; O’Malley, Shannon; Danckert, James; Besner, Derek

2015-01-01

Drawing on theoretical and computational work with the localist dual route reading model and results from behavioral studies, Besner et al. (2011) proposed that the ability to perform tasks that require overriding stimulus-specific defaults (e.g., semantics when naming Arabic numerals, and phonology when evaluating the parity of number words) necessitate the ability to modulate the strength of connections between cognitive modules for lexical representation, semantics, and phonology on a task- and stimulus-specific basis. We used functional magnetic resonance imaging to evaluate this account by assessing changes in functional connectivity while participants performed tasks that did and did not require such stimulus-task default overrides. The occipital region showing the greatest modulation of BOLD signal strength for the two stimulus types was used as the seed region for Granger causality mapping (GCM). Our GCM analysis revealed a region of rostromedial frontal cortex with a crossover interaction. When participants performed tasks that required overriding stimulus type defaults (i.e., parity judgments of number words and naming Arabic numerals) functional connectivity between the occipital region and rostromedial frontal cortex was present. Statistically significant functional connectivity was absent when the tasks were the default for the stimulus type (i.e., parity judgments of Arabic numerals and reading number words). This frontal region (BA 10) has previously been shown to be involved in goal-directed behavior and maintenance of a specific task set. We conclude that overriding stimulus-task defaults requires a modulation of connection strengths between cognitive modules and that the override mechanism predicted from cognitive theory is instantiated by frontal modulation of neural activity of brain regions specialized for sensory processing. PMID:25870571

Specialization in the default mode: Task-induced brain deactivations dissociate between visual working memory and attention.

PubMed

Mayer, Jutta S; Roebroeck, Alard; Maurer, Konrad; Linden, David E J

2010-01-01

The idea of an organized mode of brain function that is present as default state and suspended during goal-directed behaviors has recently gained much interest in the study of human brain function. The default mode hypothesis is based on the repeated observation that certain brain areas show task-induced deactivations across a wide range of cognitive tasks. In this event-related functional resonance imaging study we tested the default mode hypothesis by comparing common and selective patterns of BOLD deactivation in response to the demands on visual attention and working memory (WM) that were independently modulated within one task. The results revealed task-induced deactivations within regions of the default mode network (DMN) with a segregation of areas that were additively deactivated by an increase in the demands on both attention and WM, and areas that were selectively deactivated by either high attentional demand or WM load. Attention-selective deactivations appeared in the left ventrolateral and medial prefrontal cortex and the left lateral temporal cortex. Conversely, WM-selective deactivations were found predominantly in the right hemisphere including the medial-parietal, the lateral temporo-parietal, and the medial prefrontal cortex. Moreover, during WM encoding deactivated regions showed task-specific functional connectivity. These findings demonstrate that task-induced deactivations within parts of the DMN depend on the specific characteristics of the attention and WM components of the task. The DMN can thus be subdivided into a set of brain regions that deactivate indiscriminately in response to cognitive demand ("the core DMN") and a part whose deactivation depends on the specific task. 2009 Wiley-Liss, Inc.

Motion tracking and electromyography-assisted identification of mirror hand contributions to functional near-infrared spectroscopy images acquired during a finger-tapping task performed by children with cerebral palsy.

PubMed

Hervey, Nathan; Khan, Bilal; Shagman, Laura; Tian, Fenghua; Delgado, Mauricio R; Tulchin-Francis, Kirsten; Shierk, Angela; Roberts, Heather; Smith, Linsley; Reid, Dahlia; Clegg, Nancy J; Liu, Hanli; MacFarlane, Duncan; Alexandrakis, George

2014-10-01

Recent studies have demonstrated functional near-infrared spectroscopy (fNIRS) to be a viable and sensitive method for imaging sensorimotor cortex activity in children with cerebral palsy (CP). However, during unilateral finger tapping, children with CP often exhibit unintended motions in the nontapping hand, known as mirror motions, which confuse the interpretation of resulting fNIRS images. This work presents a method for separating some of the mirror motion contributions to fNIRS images and demonstrates its application to fNIRS data from four children with CP performing a finger-tapping task with mirror motions. Finger motion and arm muscle activity were measured simultaneously with fNIRS signals using motion tracking and electromyography (EMG), respectively. Subsequently, subject-specific regressors were created from the motion capture or EMG data and independent component analysis was combined with a general linear model to create an fNIRS image representing activation due to the tapping hand and one image representing activation due to the mirror hand. The proposed method can provide information on how mirror motions contribute to fNIRS images, and in some cases, it helps remove mirror motion contamination from the tapping hand activation images.

Motion tracking and electromyography-assisted identification of mirror hand contributions to functional near-infrared spectroscopy images acquired during a finger-tapping task performed by children with cerebral palsy

PubMed Central

Hervey, Nathan; Khan, Bilal; Shagman, Laura; Tian, Fenghua; Delgado, Mauricio R.; Tulchin-Francis, Kirsten; Shierk, Angela; Roberts, Heather; Smith, Linsley; Reid, Dahlia; Clegg, Nancy J.; Liu, Hanli; MacFarlane, Duncan; Alexandrakis, George

2014-01-01

Abstract. Recent studies have demonstrated functional near-infrared spectroscopy (fNIRS) to be a viable and sensitive method for imaging sensorimotor cortex activity in children with cerebral palsy (CP). However, during unilateral finger tapping, children with CP often exhibit unintended motions in the nontapping hand, known as mirror motions, which confuse the interpretation of resulting fNIRS images. This work presents a method for separating some of the mirror motion contributions to fNIRS images and demonstrates its application to fNIRS data from four children with CP performing a finger-tapping task with mirror motions. Finger motion and arm muscle activity were measured simultaneously with fNIRS signals using motion tracking and electromyography (EMG), respectively. Subsequently, subject-specific regressors were created from the motion capture or EMG data and independent component analysis was combined with a general linear model to create an fNIRS image representing activation due to the tapping hand and one image representing activation due to the mirror hand. The proposed method can provide information on how mirror motions contribute to fNIRS images, and in some cases, it helps remove mirror motion contamination from the tapping hand activation images. PMID:26157980

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

PubMed

Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

2014-02-01

This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

PubMed

Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-11-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

Visual information processing in the lion-tailed macaque (Macaca silenus): mental rotation or rotational invariance?

PubMed

Burmann, Britta; Dehnhardt, Guido; Mauck, Björn

2005-01-01

Mental rotation is a widely accepted concept indicating an image-like mental representation of visual information and an analogue mode of information processing in certain visuospatial tasks. In the task of discriminating between image and mirror-image of rotated figures, human reaction times increase with the angular disparity between the figures. In animals, tests of this kind yield inconsistent results. Pigeons were found to use a time-independent rotational invariance, possibly indicating a non-analogue information processing system that evolved in response to the horizontal plane of reference birds perceive during flight. Despite similar ecological demands concerning the visual reference plane, a sea lion was found to use mental rotation in similar tasks, but its processing speed while rotating three-dimensional stimuli seemed to depend on the axis of rotation in a different way than found for humans in similar tasks. If ecological demands influence the way information processing systems evolve, hominids might have secondarily lost the ability of rotational invariance while retreating from arboreal living and evolving an upright gait in which the vertical reference plane is more important. We therefore conducted mental rotation experiments with an arboreal living primate species, the lion-tailed macaque. Performing a two-alternative matching-to-sample procedure, the animal had to decide between rotated figures representing image and mirror-image of a previously shown upright sample. Although non-rotated stimuli were recognized faster than rotated ones, the animal's mean reaction times did not clearly increase with the angle of rotation. These results are inconsistent with the mental rotation concept but also cannot be explained assuming a mere rotational invariance. Our study thus seems to support the idea of information processing systems evolving gradually in response to specific ecological demands.

Neural substrates of cognitive switching and inhibition in a face processing task.

PubMed

Piguet, Camille; Sterpenich, Virginie; Desseilles, Martin; Cojan, Yann; Bertschy, Gilles; Vuilleumier, Patrik

2013-11-15

We frequently need to change our current occupation, an operation requiring additional effortful cognitive demands. Switching from one task to another may involve two distinct processes: inhibition of the previously relevant task-set, and initiation of a new one. Here we tested whether these two processes are underpinned by separate neural substrates, and whether they differ depending on the nature of the task and the emotional content of stimuli. We used functional magnetic resonance imaging in healthy human volunteers who categorize emotional faces according to three different judgment rules (color, gender, or emotional expression). Our paradigm allowed us to separate neural activity associated with inhibition and switching based on the sequence of the tasks required on successive trials. We found that the bilateral medial superior parietal lobule and left intraparietal sulcus showed consistent activation during switching regardless of the task. On the other hand, no common region was activated (or suppressed) as a consequence of inhibition across all tasks. Rather, task-specific effects were observed in brain regions that were more activated when switching to a particular task but less activated after inhibition of the same task. In addition, compared to other conditions, the emotional task elicited a similar switching cost but lower inhibition cost, accompanied by selective decrease in the anterior cingulate cortex when returning to this task shortly after inhibiting it. These results demonstrate that switching relies on domain-general processes mediated by postero-medial parietal areas, engaged across all tasks, but also provide novel evidence that task inhibition produces domain-specific decreases as a function of particular task demands, with only the latter inhibition component being modulated by emotional information. Copyright © 2013 Elsevier Inc. All rights reserved.

Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: task and statistical threshold effects on language lateralization.

PubMed

Nadkarni, Tanvi N; Andreoli, Matthew J; Nair, Veena A; Yin, Peng; Young, Brittany M; Kundu, Bornali; Pankratz, Joshua; Radtke, Andrew; Holdsworth, Ryan; Kuo, John S; Field, Aaron S; Baskaya, Mustafa K; Moritz, Chad H; Meyerand, M Elizabeth; Prabhakaran, Vivek

2015-01-01

Functional magnetic resonance imaging (fMRI) is a non-invasive pre-surgical tool used to assess localization and lateralization of language function in brain tumor and vascular lesion patients in order to guide neurosurgeons as they devise a surgical approach to treat these lesions. We investigated the effect of varying the statistical thresholds as well as the type of language tasks on functional activation patterns and language lateralization. We hypothesized that language lateralization indices (LIs) would be threshold- and task-dependent. Imaging data were collected from brain tumor patients (n = 67, average age 48 years) and vascular lesion patients (n = 25, average age 43 years) who received pre-operative fMRI scanning. Both patient groups performed expressive (antonym and/or letter-word generation) and receptive (tumor patients performed text-reading; vascular lesion patients performed text-listening) language tasks. A control group (n = 25, average age 45 years) performed the letter-word generation task. Brain tumor patients showed left-lateralization during the antonym-word generation and text-reading tasks at high threshold values and bilateral activation during the letter-word generation task, irrespective of the threshold values. Vascular lesion patients showed left-lateralization during the antonym and letter-word generation, and text-listening tasks at high threshold values. Our results suggest that the type of task and the applied statistical threshold influence LI and that the threshold effects on LI may be task-specific. Thus identifying critical functional regions and computing LIs should be conducted on an individual subject basis, using a continuum of threshold values with different tasks to provide the most accurate information for surgical planning to minimize post-operative language deficits.

Evaluating the Specificity of Cognitive Control Deficits in Schizophrenia Using Antisaccades, Functional Magnetic Resonance Imaging, and Healthy Individuals With Poor Cognitive Control.

PubMed

Rodrigue, Amanda L; Schaeffer, David J; Pierce, Jordan E; Clementz, Brett A; McDowell, Jennifer E

2018-01-01

Cognitive control impairments in schizophrenia (SZ) can be evaluated using antisaccade tasks and functional magnetic resonance imaging (fMRI). Studies, however, often compare people with SZ to high performing healthy people, making it unclear if antisaccade-related disruptions are specific to the disease or due to generalized deficits in cognitive control. We included two healthy comparison groups in addition to people with SZ: healthy people with high cognitive control (HCC), who represent a more typical comparison group, and healthy people with low cognitive control (LCC), who perform similarly on antisaccade measures as people with SZ. Using two healthy comparison groups may help determine which antisaccade-related deficits are specific to SZ (distinguish SZ from LCC and HCC groups) and which are due to poor cognitive control (distinguish the LCC and SZ groups from the HCC group). People with SZ and healthy people with HCC or LCC performed an antisaccade task during fMRI acquisition. LCC and SZ groups showed under-activation of saccade circuitry. SZ-specific disruptions were observed in the left superior temporal gyrus and insula during error trials (suppression of activation in the SZ group compared to the LCC and HCC group). Differences related to antisaccade errors may distinguish people with SZ from healthy people with LCC.

There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task

PubMed Central

Miconi, Thomas; Groomes, Laura; Kreiman, Gabriel

2016-01-01

When searching for an object in a scene, how does the brain decide where to look next? Visual search theories suggest the existence of a global “priority map” that integrates bottom-up visual information with top-down, target-specific signals. We propose a mechanistic model of visual search that is consistent with recent neurophysiological evidence, can localize targets in cluttered images, and predicts single-trial behavior in a search task. This model posits that a high-level retinotopic area selective for shape features receives global, target-specific modulation and implements local normalization through divisive inhibition. The normalization step is critical to prevent highly salient bottom-up features from monopolizing attention. The resulting activity pattern constitues a priority map that tracks the correlation between local input and target features. The maximum of this priority map is selected as the locus of attention. The visual input is then spatially enhanced around the selected location, allowing object-selective visual areas to determine whether the target is present at this location. This model can localize objects both in array images and when objects are pasted in natural scenes. The model can also predict single-trial human fixations, including those in error and target-absent trials, in a search task involving complex objects. PMID:26092221

Classification of galaxy type from images using Microsoft R Server

NASA Astrophysics Data System (ADS)

de Vries, Andrie

2017-06-01

Many astronomers working in the field of AstroInformatics write code as part of their work. Although the programming language of choice is Python, a small number (8%) use R. R has its specific strengths in the domain of statistics, and is often viewed as limited in the size of data it can handle. However, Microsoft R Server is a product that removes these limitations by being able to process much larger amounts of data. I present some highlights of R Server, by illustrating how to fit a convolutional neural network using R. The specific task is to classify galaxies, using only images extracted from the Sloan Digital Skyserver.

The occipital face area is causally involved in the formation of identity-specific face representations.

PubMed

Ambrus, Géza Gergely; Dotzer, Maria; Schweinberger, Stefan R; Kovács, Gyula

2017-12-01

Transcranial magnetic stimulation (TMS) and neuroimaging studies suggest a role of the right occipital face area (rOFA) in early facial feature processing. However, the degree to which rOFA is necessary for the encoding of facial identity has been less clear. Here we used a state-dependent TMS paradigm, where stimulation preferentially facilitates attributes encoded by less active neural populations, to investigate the role of the rOFA in face perception and specifically in image-independent identity processing. Participants performed a familiarity decision task for famous and unknown target faces, preceded by brief (200 ms) or longer (3500 ms) exposures to primes which were either an image of a different identity (DiffID), another image of the same identity (SameID), the same image (SameIMG), or a Fourier-randomized noise pattern (NOISE) while either the rOFA or the vertex as control was stimulated by single-pulse TMS. Strikingly, TMS to the rOFA eliminated the advantage of SameID over DiffID condition, thereby disrupting identity-specific priming, while leaving image-specific priming (better performance for SameIMG vs. SameID) unaffected. Our results suggest that the role of rOFA is not limited to low-level feature processing, and emphasize its role in image-independent facial identity processing and the formation of identity-specific memory traces.

Dynamic whole body PET parametric imaging: II. Task-oriented statistical estimation

PubMed Central

Karakatsanis, Nicolas A.; Lodge, Martin A.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection. PMID:24080994

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

PubMed

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

Quantifying attention shifts in augmented reality image-guided neurosurgery

PubMed Central

Drouin, Simon; Collins, D. Louis; Popa, Tiberiu; Kersten-Oertel, Marta

2017-01-01

Image-guided surgery (IGS) has allowed for more minimally invasive procedures, leading to better patient outcomes, reduced risk of infection, less pain, shorter hospital stays and faster recoveries. One drawback that has emerged with IGS is that the surgeon must shift their attention from the patient to the monitor for guidance. Yet both cognitive and motor tasks are negatively affected with attention shifts. Augmented reality (AR), which merges the realworld surgical scene with preoperative virtual patient images and plans, has been proposed as a solution to this drawback. In this work, we studied the impact of two different types of AR IGS set-ups (mobile AR and desktop AR) and traditional navigation on attention shifts for the specific task of craniotomy planning. We found a significant difference in terms of the time taken to perform the task and attention shifts between traditional navigation, but no significant difference between the different AR set-ups. With mobile AR, however, users felt that the system was easier to use and that their performance was better. These results suggest that regardless of where the AR visualisation is shown to the surgeon, AR may reduce attention shifts, leading to more streamlined and focused procedures. PMID:29184663

Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantifying attention shifts in augmented reality image-guided neurosurgery.

PubMed

Léger, Étienne; Drouin, Simon; Collins, D Louis; Popa, Tiberiu; Kersten-Oertel, Marta

2017-10-01

Image-guided surgery (IGS) has allowed for more minimally invasive procedures, leading to better patient outcomes, reduced risk of infection, less pain, shorter hospital stays and faster recoveries. One drawback that has emerged with IGS is that the surgeon must shift their attention from the patient to the monitor for guidance. Yet both cognitive and motor tasks are negatively affected with attention shifts. Augmented reality (AR), which merges the realworld surgical scene with preoperative virtual patient images and plans, has been proposed as a solution to this drawback. In this work, we studied the impact of two different types of AR IGS set-ups (mobile AR and desktop AR) and traditional navigation on attention shifts for the specific task of craniotomy planning. We found a significant difference in terms of the time taken to perform the task and attention shifts between traditional navigation, but no significant difference between the different AR set-ups. With mobile AR, however, users felt that the system was easier to use and that their performance was better. These results suggest that regardless of where the AR visualisation is shown to the surgeon, AR may reduce attention shifts, leading to more streamlined and focused procedures.

Cognitive processing of visual images in migraine populations in between headache attacks.

PubMed

Mickleborough, Marla J S; Chapman, Christine M; Toma, Andreea S; Handy, Todd C

2014-09-25

People with migraine headache have altered interictal visual sensory-level processing in between headache attacks. Here we examined the extent to which these migraine abnormalities may extend into higher visual processing such as implicit evaluative analysis of visual images in between migraine events. Specifically, we asked two groups of participants--migraineurs (N=29) and non-migraine controls (N=29)--to view a set of unfamiliar commercial logos in the context of a target identification task as the brain electrical responses to these objects were recorded via event-related potentials (ERPs). Following this task, participants individually identified those logos that they most liked or disliked. We applied a between-groups comparison of how ERP responses to logos varied as a function of hedonic evaluation. Our results suggest migraineurs have abnormal implicit evaluative processing of visual stimuli. Specifically, migraineurs lacked a bias for disliked logos found in control subjects, as measured via a late positive potential (LPP) ERP component. These results suggest post-sensory consequences of migraine in between headache events, specifically abnormal cognitive evaluative processing with a lack of normal categorical hedonic evaluation. Copyright © 2014 Elsevier B.V. All rights reserved.

The recognition of web pages' hyperlinks by people with intellectual disabilities: an evaluation study.

PubMed

Rocha, Tânia; Bessa, Maximino; Gonçalves, Martinho; Cabral, Luciana; Godinho, Francisco; Peres, Emanuel; Reis, Manuel C; Magalhães, Luís; Chalmers, Alan

2012-11-01

One of the most mentioned problems of web accessibility, as recognized in several different studies, is related to the difficulty regarding the perception of what is or is not clickable in a web page. In particular, a key problem is the recognition of hyperlinks by a specific group of people, namely those with intellectual disabilities. This experiment investigated a methodology based on the direct observation, video recording, interview and data obtained by an eye tracker device. Ten participants took part in this study. They were divided into two groups and asked to perform two tasks: 'Sing a song' and 'Listen to a story' in two websites. These websites were developed to include specific details. The first website presented an image navigation menu (INM), whereas the other one showed a text navigation menu (TNM). There was a general improvement regarding the participants' performance when using INMs. The referred analysis indeed shows that not only did these specific participants gain a better understanding of the demanding task, but also they showed an improved perception concerning the content of the navigation menu that included hyperlinks with images. © 2012 Blackwell Publishing Ltd.

Elementary Teachers' Selection and Use of Visual Models

NASA Astrophysics Data System (ADS)

Lee, Tammy D.; Gail Jones, M.

2018-02-01

As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.

The relationship between bilingualism and selective attention in young adults: Evidence from an ambiguous figures task.

PubMed

Chung-Fat-Yim, Ashley; Sorge, Geoff B; Bialystok, Ellen

2017-03-01

Previous research has shown that bilinguals outperform monolinguals on a variety of tasks that have been described as involving executive functioning, but the precise mechanism for those effects or a clear definition for "executive function" is unknown. This uncertainty has led to a number of studies for which no performance difference between monolingual and bilingual adults has been detected. One approach to clarifying these issues comes from research with children showing that bilinguals were more able than their monolingual peers to perceive both interpretations of an ambiguous figure, an ability that is more tied to a conception of selective attention than to specific components of executive function. The present study extends this notion to adults by assessing their ability to see the alternative image in an ambiguous figure. Bilinguals performed this task more efficiently than monolinguals by requiring fewer cues to identify the second image. This finding has implications for the role of selective attention in performance differences between monolinguals and bilinguals.

Skin self-examinations and visual identification of atypical nevi: comparing individual and crowdsourcing approaches.

PubMed

King, Andy J; Gehl, Robert W; Grossman, Douglas; Jensen, Jakob D

2013-12-01

Skin self-examination (SSE) is one method for identifying atypical nevi among members of the general public. Unfortunately, past research has shown that SSE has low sensitivity in detecting atypical nevi. The current study investigates whether crowdsourcing (collective effort) can improve SSE identification accuracy. Collective effort is potentially useful for improving people's visual identification of atypical nevi during SSE because, even when a single person has low reliability at a task, the pattern of the group can overcome the limitations of each individual. Adults (N=500) were recruited from a shopping mall in the Midwest. Participants viewed educational pamphlets about SSE and then completed a mole identification task. For the task, participants were asked to circle mole images that appeared atypical. Forty nevi images were provided; nine of the images were of nevi that were later diagnosed as melanoma. Consistent with past research, individual effort exhibited modest sensitivity (.58) for identifying atypical nevi in the mole identification task. As predicted, collective effort overcame the limitations of individual effort. Specifically, a 19% collective effort identification threshold exhibited superior sensitivity (.90). The results of the current study suggest that limitations of SSE can be countered by collective effort, a finding that supports the pursuit of interventions promoting early melanoma detection that contain crowdsourced visual identification components. Copyright © 2013 Elsevier Ltd. All rights reserved.

Error Processing and Gender-Shared and -Specific Neural Predictors of Relapse in Cocaine Dependence

ERIC Educational Resources Information Center

Luo, Xi; Zhang, Sheng; Hu, Sien; Bednarski, Sarah R.; Erdman, Emily; Farr, Olivia M.; Hong, Kwang-Ik; Sinha, Rajita; Mazure, Carolyn M.; Li, Chiang-shan R.

2013-01-01

Deficits in cognitive control are implicated in cocaine dependence. Previously, combining functional magnetic resonance imaging and a stop signal task, we demonstrated altered cognitive control in cocaine-dependent individuals. However, the clinical implications of these cross-sectional findings and, in particular, whether the changes were…

Bidirectional Modulation of Recognition Memory

PubMed Central

Ho, Jonathan W.; Poeta, Devon L.; Jacobson, Tara K.; Zolnik, Timothy A.; Neske, Garrett T.; Connors, Barry W.

2015-01-01

Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30–40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30–40 Hz was not effective in increasing exploration of novel images. Stimulation at 10–15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. SIGNIFICANCE STATEMENT Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of the PER could increase or decrease exploration of novel and familiar images depending on the frequency of stimulation. Our findings suggest that optical stimulation of PER at specific frequencies can predictably alter recognition memory. PMID:26424881

Divided versus selective attention: evidence for common processing mechanisms

PubMed Central

Hahn, Britta; Wolkenberg, Frank A.; Ross, Thomas J.; Myers, Carol S.; Heishman, Stephen J.; Stein, Dan J.; Kurup, Pradeep K.; Stein, Elliot A.

2008-01-01

The current study revisited the question of whether there are brain mechanisms specific to divided attention that differ from those used in selective attention. Increased neuronal activity required to simultaneously process two stimulus dimensions as compared with each separate dimension has often been observed, but rarely has activity induced by a divided attention condition exceeded the sum of activity induced by the component tasks. Healthy participants performed a selective-divided attention paradigm while undergoing functional Magnetic Resonance Imaging (fMRI). The task required participants to make a same-different judgment about either one of two simultaneously presented stimulus dimensions, or about both dimensions. Performance accuracy was equated between tasks by dynamically adjusting the stimulus display time. Blood Oxygenation Level Dependent (BOLD) signal differences between tasks were identified by whole-brain voxel-wise comparisons and by region-specific analyses of all areas modulated by the divided attention task (DIV). No region displayed greater activation or deactivation by DIV than the sum of signal change by the two selective attention tasks. Instead, regional activity followed the tasks’ processing demands as reflected by reaction time. Only a left cerebellar region displayed a correlation between participants’ BOLD signal intensity and reaction time that was selective for DIV. The correlation was positive, reflecting slower responding with greater activation. Overall, the findings do not support the existence of functional brain activity specific to DIV. Increased activity appears to reflect additional processing demands by introducing a secondary task, but those demands do not appear to qualitatively differ from processes of selective attention. PMID:18479670

Task difficulty modulates brain activation in the emotional oddball task.

PubMed

Siciliano, Rachel E; Madden, David J; Tallman, Catherine W; Boylan, Maria A; Kirste, Imke; Monge, Zachary A; Packard, Lauren E; Potter, Guy G; Wang, Lihong

2017-06-01

Previous functional magnetic resonance imaging (fMRI) studies have reported that task-irrelevant, emotionally salient events can disrupt target discrimination, particularly when attentional demands are low, while others demonstrate alterations in the distracting effects of emotion in behavior and neural activation in the context of attention-demanding tasks. We used fMRI, in conjunction with an emotional oddball task, at different levels of target discrimination difficulty, to investigate the effects of emotional distractors on the detection of subsequent targets. In addition, we distinguished different behavioral components of target detection representing decisional, nondecisional, and response criterion processes. Results indicated that increasing target discrimination difficulty led to increased time required for both the decisional and nondecisional components of the detection response, as well as to increased target-related neural activation in frontoparietal regions. The emotional distractors were associated with activation in ventral occipital and frontal regions and dorsal frontal regions, but this activation was attenuated with increased difficulty. Emotional distraction did not alter the behavioral measures of target detection, but did lead to increased target-related frontoparietal activation for targets following emotional images as compared to those following neutral images. This latter effect varied with target discrimination difficulty, with an increased influence of the emotional distractors on subsequent target-related frontoparietal activation in the more difficult discrimination condition. This influence of emotional distraction was in addition associated specifically with the decisional component of target detection. These findings indicate that emotion-cognition interactions, in the emotional oddball task, vary depending on the difficulty of the target discrimination and the associated limitations on processing resources. Copyright © 2017 Elsevier B.V. All rights reserved.

Image Display and Manipulation System (IDAMS) program documentation, Appendixes A-D. [including routines, convolution filtering, image expansion, and fast Fourier transformation

NASA Technical Reports Server (NTRS)

Cecil, R. W.; White, R. A.; Szczur, M. R.

1972-01-01

The IDAMS Processor is a package of task routines and support software that performs convolution filtering, image expansion, fast Fourier transformation, and other operations on a digital image tape. A unique task control card for that program, together with any necessary parameter cards, selects each processing technique to be applied to the input image. A variable number of tasks can be selected for execution by including the proper task and parameter cards in the input deck. An executive maintains control of the run; it initiates execution of each task in turn and handles any necessary error processing.

Left inferior-parietal lobe activity in perspective tasks: identity statements

PubMed Central

Arora, Aditi; Weiss, Benjamin; Schurz, Matthias; Aichhorn, Markus; Wieshofer, Rebecca C.; Perner, Josef

2015-01-01

We investigate the theory that the left inferior parietal lobe (IPL) is closely associated with tracking potential differences of perspective. Developmental studies find that perspective tasks are mastered at around 4 years of age. Our first study, meta-analyses of brain imaging studies shows that perspective tasks specifically activate a region in the left IPL and precuneus. These tasks include processing of false belief, visual perspective, and episodic memory. We test the location specificity theory in our second study with an unusual and novel kind of perspective task: identity statements. According to Frege's classical logical analysis, identity statements require appreciation of modes of presentation (perspectives). We show that identity statements, e.g., “the tour guide is also the driver” activate the left IPL in contrast to a control statements, “the tour guide has an apprentice.” This activation overlaps with the activations found in the meta-analysis. This finding is confirmed in a third study with different types of statements and different comparisons. All studies support the theory that the left IPL has as one of its overarching functions the tracking of perspective differences. We discuss how this function relates to the bottom-up attention function proposed for the bilateral IPL. PMID:26175677

Environmental heat stress enhances mental fatigue during sustained attention task performing: evidence from an ASL perfusion study.

PubMed

Qian, Shaowen; Li, Min; Li, Guoying; Liu, Kai; Li, Bo; Jiang, Qingjun; Li, Li; Yang, Zhen; Sun, Gang

2015-03-01

This study was to investigate the potential enhancing effect of heat stress on mental fatigue progression during sustained attention task using arterial spin labeling (ASL) imaging. Twenty participants underwent two thermal exposures in an environmental chamber: normothermic (NT) condition (25°C, 1h) and hyperthermic (HT) condition (50°C, 1h). After thermal exposure, they performed a twenty-minute psychomotor vigilance test (PVT) in the scanner. Behavioral analysis revealed progressively increasing subjective fatigue ratings and reaction time as PVT progressed. Moreover, heat stress caused worse performance. Perfusion imaging analyses showed significant resting-state cerebral blood flow (CBF) alterations after heat exposure. Specifically, increased CBF mainly gathered in thalamic-brainstem area while decreased CBF predominantly located in fronto-parietal areas, anterior cingulate cortex, posterior cingulate cortex, and medial frontal cortex. More importantly, diverse CBF distributions and trend of changes between both conditions were observed as the fatigue level progressed during subsequent PVT task. Specifically, higher CBF and enhanced rising trend were presented in superior parietal lobe, precuneus, posterior cingulate cortex and anterior cingulate cortex, while lower CBF or inhibited rising trend was found in dorsolateral frontal cortex, medial frontal cortex, inferior parietal lobe and thalamic-brainstem areas. Furthermore, the decrease of post-heat resting-state CBF in fronto-parietal cortex was correlated with subsequent slower reaction time, suggesting prior disturbed resting-state CBF might be indicator of performance potential and fatigue level in following task. These findings may provide proof for such a view: heat stress has a potential fatigue-enhancing effect when individual is performing highly cognition-demanding attention task. Copyright © 2014 Elsevier B.V. All rights reserved.

Robot acting on moving bodies (RAMBO): Preliminary results

NASA Technical Reports Server (NTRS)

Davis, Larry S.; Dementhon, Daniel; Bestul, Thor; Ziavras, Sotirios; Srinivasan, H. V.; Siddalingaiah, Madju; Harwood, David

1989-01-01

A robot system called RAMBO is being developed. It is equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a moving object. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations nearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enchancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows the use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using parametric cubic splines between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

Quantification of signal detection performance degradation induced by phase-retrieval in propagation-based x-ray phase-contrast imaging

NASA Astrophysics Data System (ADS)

Chou, Cheng-Ying; Anastasio, Mark A.

2016-04-01

In propagation-based X-ray phase-contrast (PB XPC) imaging, the measured image contains a mixture of absorption- and phase-contrast. To obtain separate images of the projected absorption and phase (i.e., refractive) properties of a sample, phase retrieval methods can be employed. It has been suggested that phase-retrieval can always improve image quality in PB XPC imaging. However, when objective (task-based) measures of image quality are employed, this is not necessarily true and phase retrieval can be detrimental. In this work, signal detection theory is utilized to quantify the performance of a Hotelling observer (HO) for detecting a known signal in a known background. Two cases are considered. In the first case, the HO acts directly on the measured intensity data. In the second case, the HO acts on either the retrieved phase or absorption image. We demonstrate that the performance of the HO is superior when acting on the measured intensity data. The loss of task-specific information induced by phase-retrieval is quantified by computing the efficiency of the HO as the ratio of the test statistic signal-to-noise ratio (SNR) for the two cases. The effect of the system geometry on this efficiency is systematically investigated. Our findings confirm that phase-retrieval can impair signal detection performance in XPC imaging.

Resting-State Functional Magnetic Resonance Imaging for Language Preoperative Planning

PubMed Central

Branco, Paulo; Seixas, Daniela; Deprez, Sabine; Kovacs, Silvia; Peeters, Ronald; Castro, São L.; Sunaert, Stefan

2016-01-01

Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in resting-state protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using resting-state fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-state networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting-state language maps, particularly within the language regions of interest. Furthermore, resting-state language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that resting-state protocols may be suitable to map language networks in a quick and clinically efficient way. PMID:26869899

Task-irrelevant emotion facilitates face discrimination learning.

PubMed

Lorenzino, Martina; Caudek, Corrado

2015-03-01

We understand poorly how the ability to discriminate faces from one another is shaped by visual experience. The purpose of the present study is to determine whether face discrimination learning can be facilitated by facial emotions. To answer this question, we used a task-irrelevant perceptual learning paradigm because it closely mimics the learning processes that, in daily life, occur without a conscious intention to learn and without an attentional focus on specific facial features. We measured face discrimination thresholds before and after training. During the training phase (4 days), participants performed a contrast discrimination task on face images. They were not informed that we introduced (task-irrelevant) subtle variations in the face images from trial to trial. For the Identity group, the task-irrelevant features were variations along a morphing continuum of facial identity. For the Emotion group, the task-irrelevant features were variations along an emotional expression morphing continuum. The Control group did not undergo contrast discrimination learning and only performed the pre-training and post-training tests, with the same temporal gap between them as the other two groups. Results indicate that face discrimination improved, but only for the Emotion group. Participants in the Emotion group, moreover, showed face discrimination improvements also for stimulus variations along the facial identity dimension, even if these (task-irrelevant) stimulus features had not been presented during training. The present results highlight the importance of emotions for face discrimination learning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Explicit attention interferes with selective emotion processing in human extrastriate cortex.

PubMed

Schupp, Harald T; Stockburger, Jessica; Bublatzky, Florian; Junghöfer, Markus; Weike, Almut I; Hamm, Alfons O

2007-02-22

Brain imaging and event-related potential studies provide strong evidence that emotional stimuli guide selective attention in visual processing. A reflection of the emotional attention capture is the increased Early Posterior Negativity (EPN) for pleasant and unpleasant compared to neutral images (approximately 150-300 ms poststimulus). The present study explored whether this early emotion discrimination reflects an automatic phenomenon or is subject to interference by competing processing demands. Thus, emotional processing was assessed while participants performed a concurrent feature-based attention task varying in processing demands. Participants successfully performed the primary visual attention task as revealed by behavioral performance and selected event-related potential components (Selection Negativity and P3b). Replicating previous results, emotional modulation of the EPN was observed in a task condition with low processing demands. In contrast, pleasant and unpleasant pictures failed to elicit increased EPN amplitudes compared to neutral images in more difficult explicit attention task conditions. Further analyses determined that even the processing of pleasant and unpleasant pictures high in emotional arousal is subject to interference in experimental conditions with high task demand. Taken together, performing demanding feature-based counting tasks interfered with differential emotion processing indexed by the EPN. The present findings demonstrate that taxing processing resources by a competing primary visual attention task markedly attenuated the early discrimination of emotional from neutral picture contents. Thus, these results provide further empirical support for an interference account of the emotion-attention interaction under conditions of competition. Previous studies revealed the interference of selective emotion processing when attentional resources were directed to locations of explicitly task-relevant stimuli. The present data suggest that interference of emotion processing by competing task demands is a more general phenomenon extending to the domain of feature-based attention. Furthermore, the results are inconsistent with the notion of effortlessness, i.e., early emotion discrimination despite concurrent task demands. These findings implicate to assess the presumed automatic nature of emotion processing at the level of specific aspects rather than considering automaticity as an all-or-none phenomenon.

Explicit attention interferes with selective emotion processing in human extrastriate cortex

PubMed Central

Schupp, Harald T; Stockburger, Jessica; Bublatzky, Florian; Junghöfer, Markus; Weike, Almut I; Hamm, Alfons O

2007-01-01

Background Brain imaging and event-related potential studies provide strong evidence that emotional stimuli guide selective attention in visual processing. A reflection of the emotional attention capture is the increased Early Posterior Negativity (EPN) for pleasant and unpleasant compared to neutral images (~150–300 ms poststimulus). The present study explored whether this early emotion discrimination reflects an automatic phenomenon or is subject to interference by competing processing demands. Thus, emotional processing was assessed while participants performed a concurrent feature-based attention task varying in processing demands. Results Participants successfully performed the primary visual attention task as revealed by behavioral performance and selected event-related potential components (Selection Negativity and P3b). Replicating previous results, emotional modulation of the EPN was observed in a task condition with low processing demands. In contrast, pleasant and unpleasant pictures failed to elicit increased EPN amplitudes compared to neutral images in more difficult explicit attention task conditions. Further analyses determined that even the processing of pleasant and unpleasant pictures high in emotional arousal is subject to interference in experimental conditions with high task demand. Taken together, performing demanding feature-based counting tasks interfered with differential emotion processing indexed by the EPN. Conclusion The present findings demonstrate that taxing processing resources by a competing primary visual attention task markedly attenuated the early discrimination of emotional from neutral picture contents. Thus, these results provide further empirical support for an interference account of the emotion-attention interaction under conditions of competition. Previous studies revealed the interference of selective emotion processing when attentional resources were directed to locations of explicitly task-relevant stimuli. The present data suggest that interference of emotion processing by competing task demands is a more general phenomenon extending to the domain of feature-based attention. Furthermore, the results are inconsistent with the notion of effortlessness, i.e., early emotion discrimination despite concurrent task demands. These findings implicate to assess the presumed automatic nature of emotion processing at the level of specific aspects rather than considering automaticity as an all-or-none phenomenon. PMID:17316444

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

PubMed

Gomez-Cardona, Daniel; Hayes, John W; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong

2018-05-01

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs. The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work. A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object. © 2018 American Association of Physicists in Medicine.

Characteristic and intermingled neocortical circuits encode different visual object discriminations.

PubMed

Zhang, Guo-Rong; Zhao, Hua; Cook, Nathan; Svestka, Michael; Choi, Eui M; Jan, Mary; Cook, Robert G; Geller, Alfred I

2017-07-28

Synaptic plasticity and neural network theories hypothesize that the essential information for advanced cognitive tasks is encoded in specific circuits and neurons within distributed neocortical networks. However, these circuits are incompletely characterized, and we do not know if a specific discrimination is encoded in characteristic circuits among multiple animals. Here, we determined the spatial distribution of active neurons for a circuit that encodes some of the essential information for a cognitive task. We genetically activated protein kinase C pathways in several hundred spatially-grouped glutamatergic and GABAergic neurons in rat postrhinal cortex, a multimodal associative area that is part of a distributed circuit that encodes visual object discriminations. We previously established that this intervention enhances accuracy for specific discriminations. Moreover, the genetically-modified, local circuit in POR cortex encodes some of the essential information, and this local circuit is preferentially activated during performance, as shown by activity-dependent gene imaging. Here, we mapped the positions of the active neurons, which revealed that two image sets are encoded in characteristic and different circuits. While characteristic circuits are known to process sensory information, in sensory areas, this is the first demonstration that characteristic circuits encode specific discriminations, in a multimodal associative area. Further, the circuits encoding the two image sets are intermingled, and likely overlapping, enabling efficient encoding. Consistent with reconsolidation theories, intermingled and overlapping encoding could facilitate formation of associations between related discriminations, including visually similar discriminations or discriminations learned at the same time or place. Copyright © 2017 Elsevier B.V. All rights reserved.

3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.

PubMed

Jiang, Jun; Wu, Yao; Huang, Meiyan; Yang, Wei; Chen, Wufan; Feng, Qianjin

2013-01-01

Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Abnormal Image Detection in Endoscopy Videos Using a Filter Bank and Local Binary Patterns

PubMed Central

Nawarathna, Ruwan; Oh, JungHwan; Muthukudage, Jayantha; Tavanapong, Wallapak; Wong, Johnny; de Groen, Piet C.; Tang, Shou Jiang

2014-01-01

Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save physician’s time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a “texton histogram” of an image block as features. The histogram captures the distribution of different “textons” representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images. PMID:25132723

Automated Real-Time Behavioral and Physiological Data Acquisition and Display Integrated with Stimulus Presentation for fMRI

PubMed Central

Voyvodic, James T.; Glover, Gary H.; Greve, Douglas; Gadde, Syam

2011-01-01

Functional magnetic resonance imaging (fMRI) is based on correlating blood oxygen-level dependent (BOLD) signal fluctuations in the brain with other time-varying signals. Although the most common reference for correlation is the timing of a behavioral task performed during the scan, many other behavioral and physiological variables can also influence fMRI signals. Variations in cardiac and respiratory functions in particular are known to contribute significant BOLD signal fluctuations. Variables such as skin conduction, eye movements, and other measures that may be relevant to task performance can also be correlated with BOLD signals and can therefore be used in image analysis to differentiate multiple components in complex brain activity signals. Combining real-time recording and data management of multiple behavioral and physiological signals in a way that can be routinely used with any task stimulus paradigm is a non-trivial software design problem. Here we discuss software methods that allow users control of paradigm-specific audio–visual or other task stimuli combined with automated simultaneous recording of multi-channel behavioral and physiological response variables, all synchronized with sub-millisecond temporal accuracy. We also discuss the implementation and importance of real-time display feedback to ensure data quality of all recorded variables. Finally, we discuss standards and formats for storage of temporal covariate data and its integration into fMRI image analysis. These neuroinformatics methods have been adopted for behavioral task control at all sites in the Functional Biomedical Informatics Research Network (FBIRN) multi-center fMRI study. PMID:22232596

Evidence that primary visual cortex is required for image, orientation, and motion discrimination by rats.

PubMed

Petruno, Sarah K; Clark, Robert E; Reinagel, Pamela

2013-01-01

The pigmented Long-Evans rat has proven to be an excellent subject for studying visually guided behavior including quantitative visual psychophysics. This observation, together with its experimental accessibility and its close homology to the mouse, has made it an attractive model system in which to dissect the thalamic and cortical circuits underlying visual perception. Given that visually guided behavior in the absence of primary visual cortex has been described in the literature, however, it is an empirical question whether specific visual behaviors will depend on primary visual cortex in the rat. Here we tested the effects of cortical lesions on performance of two-alternative forced-choice visual discriminations by Long-Evans rats. We present data from one highly informative subject that learned several visual tasks and then received a bilateral lesion ablating >90% of primary visual cortex. After the lesion, this subject had a profound and persistent deficit in complex image discrimination, orientation discrimination, and full-field optic flow motion discrimination, compared with both pre-lesion performance and sham-lesion controls. Performance was intact, however, on another visual two-alternative forced-choice task that required approaching a salient visual target. A second highly informative subject learned several visual tasks prior to receiving a lesion ablating >90% of medial extrastriate cortex. This subject showed no impairment on any of the four task categories. Taken together, our data provide evidence that these image, orientation, and motion discrimination tasks require primary visual cortex in the Long-Evans rat, whereas approaching a salient visual target does not.

A sharp image or a sharp knife: norms for the modality-exclusivity of 774 concept-property items.

PubMed

van Dantzig, Saskia; Cowell, Rosemary A; Zeelenberg, René; Pecher, Diane

2011-03-01

According to recent embodied cognition theories, mental concepts are represented by modality-specific sensory-motor systems. Much of the evidence for modality-specificity in conceptual processing comes from the property-verification task. When applying this and other tasks, it is important to select items based on their modality-exclusivity. We collected modality ratings for a set of 387 properties, each of which was paired with two different concepts, yielding a total of 774 concept-property items. For each item, participants rated the degree to which the property could be experienced through five perceptual modalities (vision, audition, touch, smell, and taste). Based on these ratings, we computed a measure of modality exclusivity, the degree to which a property is perceived exclusively through one sensory modality. In this paper, we briefly sketch the theoretical background of conceptual knowledge, discuss the use of the property-verification task in cognitive research, provide our norms and statistics, and validate the norms in a memory experiment. We conclude that our norms are important for researchers studying modality-specific effects in conceptual processing.

Cognitive-motor dual-task interference: A systematic review of neural correlates.

PubMed

Leone, Carmela; Feys, Peter; Moumdjian, Lousin; D'Amico, Emanuele; Zappia, Mario; Patti, Francesco

2017-04-01

Cognitive-motor interference refers to dual-tasking (DT) interference (DTi) occurring when the simultaneous performance of a cognitive and a motor task leads to a percentage change in one or both tasks. Several theories exist to explain DTi in humans: the capacity-sharing, the bottleneck and the cross-talk theories. Numerous studies investigating whether a specific brain locus is associated with cognitive-motor DTi have been conducted, but not systematically reviewed. We aimed to review the evidences on brain activity associated with the cognitive-motor DT, in order to better understand the neurological basis of the CMi. Results were reported according to the technique used to assess brain activity. Twenty-three articles met the inclusion criteria. Out of them, nine studies used functional magnetic resonance imaging to show an additive, under-additive, over- additive, or a mixed activation pattern of the brain. Seven studies used near-infrared spectroscopy, and seven neurophysiological instruments. Yet a specific DT locus in the brain cannot be concluded from the overall current literature. Future studies are warranted to overcome the shortcomings identified. Copyright © 2017 Elsevier Ltd. All rights reserved.

Grid Computing Application for Brain Magnetic Resonance Image Processing

NASA Astrophysics Data System (ADS)

Valdivia, F.; Crépeault, B.; Duchesne, S.

2012-02-01

This work emphasizes the use of grid computing and web technology for automatic post-processing of brain magnetic resonance images (MRI) in the context of neuropsychiatric (Alzheimer's disease) research. Post-acquisition image processing is achieved through the interconnection of several individual processes into pipelines. Each process has input and output data ports, options and execution parameters, and performs single tasks such as: a) extracting individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear registration), c) performing image statistical analyses, and d) producing the necessary quality control images and/or files for user review. The pipelines are built to perform specific sequences of tasks on the alphanumeric data and MRIs contained in our database. The web application is coded in PHP and allows the creation of scripts to create, store and execute pipelines and their instances either on our local cluster or on high-performance computing platforms. To run an instance on an external cluster, the web application opens a communication tunnel through which it copies the necessary files, submits the execution commands and collects the results. We present result on system tests for the processing of a set of 821 brain MRIs from the Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed of 10 processes. Our results show successful execution on both local and external clusters, and a 4-fold increase in performance if using the external cluster. However, the latter's performance does not scale linearly as queue waiting times and execution overhead increase with the number of tasks to be executed.

Brain Connectivity and Visual Attention

PubMed Central

Parks, Emily L.

2013-01-01

Abstract Emerging hypotheses suggest that efficient cognitive functioning requires the integration of separate, but interconnected cortical networks in the brain. Although task-related measures of brain activity suggest that a frontoparietal network is associated with the control of attention, little is known regarding how components within this distributed network act together or with other networks to achieve various attentional functions. This review considers both functional and structural studies of brain connectivity, as complemented by behavioral and task-related neuroimaging data. These studies show converging results: The frontal and parietal cortical regions are active together, over time, and identifiable frontoparietal networks are active in relation to specific task demands. However, the spontaneous, low-frequency fluctuations of brain activity that occur in the resting state, without specific task demands, also exhibit patterns of connectivity that closely resemble the task-related, frontoparietal attention networks. Both task-related and resting-state networks exhibit consistent relations to behavioral measures of attention. Further, anatomical structure, particularly white matter pathways as defined by diffusion tensor imaging, places constraints on intrinsic functional connectivity. Lastly, connectivity analyses applied to investigate cognitive differences across individuals in both healthy and diseased states suggest that disconnection of attentional networks is linked to deficits in cognitive functioning, and in extreme cases, to disorders of attention. Thus, comprehensive theories of visual attention and their clinical translation depend on the continued integration of behavioral, task-related neuroimaging, and brain connectivity measures. PMID:23597177

The effects of 'ecstasy' (MDMA) on visuospatial memory performance: findings from a systematic review with meta-analyses.

PubMed

Murphy, Philip N; Bruno, Raimondo; Ryland, Ida; Wareing, Michele; Fisk, John E; Montgomery, Catharine; Hilton, Joanne

2012-03-01

To review, with meta-analyses where appropriate, performance differences between ecstasy (3,4-methylenedioxymethamphetamine) users and non-users on a wider range of visuospatial tasks than previously reviewed. Such tasks have been shown to draw upon working memory executive resources. Abstract databases were searched using the United Kingdom National Health Service Evidence Health Information Resource. Inclusion criteria were publication in English language peer-reviewed journals and the reporting of new findings regarding human ecstasy-users' performance on visuospatial tasks. Data extracted included specific task requirements to provide a basis for meta-analyses for categories of tasks with similar requirements. Fifty-two studies were identified for review, although not all were suitable for meta-analysis. Significant weighted mean effect sizes indicating poorer performance by ecstasy users compared with matched controls were found for tasks requiring recall of spatial stimulus elements, recognition of figures and production/reproduction of figures. There was no evidence of a linear relationship between estimated ecstasy consumption and effect sizes. Given the networked nature of processing for spatial and non-spatial visual information, future scanning and imaging studies should focus on brain activation differences between ecstasy users and non-users in the context of specific tasks to facilitate identification of loci of potentially compromised activity in users. Copyright © 2012 John Wiley & Sons, Ltd.

A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images.

PubMed

Chang, Xiao; Mazur, Thomas; Li, H Harold; Yang, Deshan

2017-12-01

A method was developed to recognize anatomical site and image acquisition view automatically in 2D X-ray images that are used in image-guided radiation therapy. The purpose is to enable site and view dependent automation and optimization in the image processing tasks including 2D-2D image registration, 2D image contrast enhancement, and independent treatment site confirmation. The X-ray images for 180 patients of six disease sites (the brain, head-neck, breast, lung, abdomen, and pelvis) were included in this study with 30 patients each site and two images of orthogonal views each patient. A hierarchical multiclass recognition model was developed to recognize general site first and then specific site. Each node of the hierarchical model recognized the images using a feature extraction step based on principal component analysis followed by a binary classification step based on support vector machine. Given two images in known orthogonal views, the site recognition model achieved a 99% average F1 score across the six sites. If the views were unknown in the images, the average F1 score was 97%. If only one image was taken either with or without view information, the average F1 score was 94%. The accuracy of the site-specific view recognition models was 100%.

Novel Algorithm for Classification of Medical Images

NASA Astrophysics Data System (ADS)

Bhushan, Bharat; Juneja, Monika

2010-11-01

Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

Human activity discrimination for maritime application

NASA Astrophysics Data System (ADS)

Boettcher, Evelyn; Deaver, Dawne M.; Krapels, Keith

2008-04-01

The US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) is investigating how motion affects the target acquisition model (NVThermIP) sensor performance estimates. This paper looks specifically at estimating sensor performance for the task of discriminating human activities on watercraft, and was sponsored by the Office of Naval Research (ONR). Traditionally, sensor models were calibrated using still images. While that approach is sufficient for static targets, video allows one to use motion cues to aid in discerning the type of human activity more quickly and accurately. This, in turn, will affect estimated sensor performance and these effects are measured in order to calibrate current target acquisition models for this task. The study employed an eleven alternative forced choice (11AFC) human perception experiment to measure the task difficulty of discriminating unique human activities on watercrafts. A mid-wave infrared camera was used to collect video at night. A description of the construction of this experiment is given, including: the data collection, image processing, perception testing and how contrast was defined for video. These results are applicable to evaluate sensor field performance for Anti-Terrorism and Force Protection (AT/FP) tasks for the U.S. Navy.

Effects of spatial frequency bands on perceptual decision: it is not the stimuli but the comparison.

PubMed

Rotshtein, Pia; Schofield, Andrew; Funes, María J; Humphreys, Glyn W

2010-08-24

Observers performed three between- and two within-category perceptual decisions with hybrid stimuli comprising low and high spatial frequency (SF) images. We manipulated (a) attention to, and (b) congruency of information in the two SF bands. Processing difficulty of the different SF bands varied across different categorization tasks: house-flower, face-house, and valence decisions were easier when based on high SF bands, while flower-face and gender categorizations were easier when based on low SF bands. Larger interference also arose from response relevant distracters that were presented in the "preferred" SF range of the task. Low SF effects were facilitated by short exposure durations. The results demonstrate that decisions are affected by an interaction of task and SF range and that the information from the non-attended SF range interfered at the decision level. A further analysis revealed that overall differences in the statistics of image features, in particular differences of orientation information between two categories, were associated with decision difficulty. We concluded that the advantage of using information from one SF range over another depends on the specific task requirements that built on the differences of the statistical properties between the compared categories.

Body Image in Anorexia Nervosa: Body Size Estimation Utilising a Biological Motion Task and Eyetracking.

PubMed

Phillipou, Andrea; Rossell, Susan Lee; Gurvich, Caroline; Castle, David Jonathan; Troje, Nikolaus Friedrich; Abel, Larry Allen

2016-03-01

Anorexia nervosa (AN) is a psychiatric condition characterised by a distortion of body image. However, whether individuals with AN can accurately perceive the size of other individuals' bodies is unclear. In the current study, 24 women with AN and 24 healthy control participants undertook two biological motion tasks while eyetracking was performed: to identify the gender and to indicate the walkers' body size. Anorexia nervosa participants tended to 'hyperscan' stimuli but did not demonstrate differences in how visual attention was directed to different body areas, relative to controls. Groups also did not differ in their estimation of body size. The hyperscanning behaviours suggest increased anxiety to disorder-relevant stimuli in AN. The lack of group difference in the estimation of body size suggests that the AN group was able to judge the body size of others accurately. The findings are discussed in terms of body image distortion specific to oneself in AN. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Standardization of left atrial, right ventricular, and right atrial deformation imaging using two-dimensional speckle tracking echocardiography: a consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging.

PubMed

Badano, Luigi P; Kolias, Theodore J; Muraru, Denisa; Abraham, Theodore P; Aurigemma, Gerard; Edvardsen, Thor; D'Hooge, Jan; Donal, Erwan; Fraser, Alan G; Marwick, Thomas; Mertens, Luc; Popescu, Bogdan A; Sengupta, Partho P; Lancellotti, Patrizio; Thomas, James D; Voigt, Jens-Uwe

2018-03-27

The EACVI/ASE/Industry Task Force to standardize deformation imaging prepared this consensus document to standardize definitions and techniques for using two-dimensional (2D) speckle tracking echocardiography (STE) to assess left atrial, right ventricular, and right atrial myocardial deformation. This document is intended for both the technical engineering community and the clinical community at large to provide guidance on selecting the functional parameters to measure and how to measure them using 2D STE.This document aims to represent a significant step forward in the collaboration between the scientific societies and the industry since technical specifications of the software packages designed to post-process echocardiographic datasets have been agreed and shared before their actual development. Hopefully, this will lead to more clinically oriented software packages which will be better tailored to clinical needs and will allow industry to save time and resources in their development.

Disconnected aging: cerebral white matter integrity and age-related differences in cognition.

PubMed

Bennett, I J; Madden, D J

2014-09-12

Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Cost effective raspberry pi-based radio frequency identification tagging of mice suitable for automated in vivo imaging.

PubMed

Bolaños, Federico; LeDue, Jeff M; Murphy, Timothy H

2017-01-30

Automation of animal experimentation improves consistency, reduces potential for error while decreasing animal stress and increasing well-being. Radio frequency identification (RFID) tagging can identify individual mice in group housing environments enabling animal-specific tracking of physiological parameters. We describe a simple protocol to radio frequency identification (RFID) tag and detect mice. RFID tags were injected sub-cutaneously after brief isoflurane anesthesia and do not require surgical steps such as suturing or incisions. We employ glass-encapsulated 125kHz tags that can be read within 30.2±2.4mm of the antenna. A raspberry pi single board computer and tag reader enable automated logging and cross platform support is possible through Python. We provide sample software written in Python to provide a flexible and cost effective system for logging the weights of multiple mice in relation to pre-defined targets. The sample software can serve as the basis of any behavioral or physiological task where users will need to identify and track specific animals. Recently, we have applied this system of tagging to automated mouse brain imaging within home-cages. We provide a cost effective solution employing open source software to facilitate adoption in applications such as automated imaging or tracking individual animal weights during tasks where food or water restriction is employed as motivation for a specific behavior. Copyright © 2016 Elsevier B.V. All rights reserved.

Disconnected Aging: Cerebral White Matter Integrity and Age-Related Differences in Cognition

PubMed Central

Bennett, Ilana J.; Madden, David J.

2013-01-01

Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging. PMID:24280637

Reducing involuntary memory by interfering consolidation of stressful auditory information: A pilot study.

PubMed

Tabrizi, Fara; Jansson, Billy

2016-03-01

Intrusive emotional memories were induced by aversive auditory stimuli and modulated with cognitive tasks performed post-encoding (i.e., during consolidation). A between-subjects design was used with four conditions; three consolidation-interference tasks (a visuospatial and two verbal interference tasks) and a no-task control condition. Forty-one participants listened to a soundtrack depicting traumatic scenes (e.g., police brutality, torture and rape). Immediately after listening to the soundtrack, the subjects completed a randomly assigned task for 10 min. Intrusions from the soundtrack were reported in a diary during the following seven-day period. In line with a modality-specific approach to intrusion modulation, auditory intrusions were reduced by verbal tasks compared to both a no-task and a visuospatial interference task.. The study did not control for individual differences in imagery ability which may be a feature in intrusion development. The results provide an increased understanding of how intrusive mental images can be modulated which may have implications for preventive treatment.. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combining population and patient-specific characteristics for prostate segmentation on 3D CT images

NASA Astrophysics Data System (ADS)

Ma, Ling; Guo, Rongrong; Tian, Zhiqiang; Venkataraman, Rajesh; Sarkar, Saradwata; Liu, Xiabi; Tade, Funmilayo; Schuster, David M.; Fei, Baowei

2016-03-01

Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.

Viewing sexual images is associated with reduced physiological arousal response to gambling loss.

PubMed

Lui, Ming; Hsu, Ming

2018-01-01

Erotic imagery is one highly salient emotional signal that exists everywhere in daily life. The impact of sexual stimuli on human decision-making, however, has rarely been investigated. This study examines the impact of sexual stimuli on financial decision-making under risk. In each trial, either a sexual or neutral image was presented in a picture categorization task before a gambling task. Thirty-four men made gambling decisions while their physiological arousal, measured by skin conductance responses (SCRs), was recorded. Behaviorally, the proportion of gambling decisions did not differ between the sexual and neutral image trials. Physiologically, participants had smaller arousal differences, measured in micro-siemen per dollar, between losses and gains in the sexual rather than in the neutral image trials. Moreover, participants' SCRs to losses relative to gains predicted the proportion of gambling decisions in the neutral image trials but not in the sexual image trials. The results were consistent with the hypothesis that the presence of emotionally salient sexual images reduces attentional and arousal-related responses to gambling losses. Our results are consistent with the theory of loss attention involving increased cognitive investment in losses compared to gains. The findings also have potential practical implications for our understanding of the specific roles of sexual images in human financial decision making in everyday life, such as gambling behaviors in the casino.

Viewing sexual images is associated with reduced physiological arousal response to gambling loss

PubMed Central

Hsu, Ming

2018-01-01

Erotic imagery is one highly salient emotional signal that exists everywhere in daily life. The impact of sexual stimuli on human decision-making, however, has rarely been investigated. This study examines the impact of sexual stimuli on financial decision-making under risk. In each trial, either a sexual or neutral image was presented in a picture categorization task before a gambling task. Thirty-four men made gambling decisions while their physiological arousal, measured by skin conductance responses (SCRs), was recorded. Behaviorally, the proportion of gambling decisions did not differ between the sexual and neutral image trials. Physiologically, participants had smaller arousal differences, measured in micro-siemen per dollar, between losses and gains in the sexual rather than in the neutral image trials. Moreover, participants’ SCRs to losses relative to gains predicted the proportion of gambling decisions in the neutral image trials but not in the sexual image trials. The results were consistent with the hypothesis that the presence of emotionally salient sexual images reduces attentional and arousal-related responses to gambling losses. Our results are consistent with the theory of loss attention involving increased cognitive investment in losses compared to gains. The findings also have potential practical implications for our understanding of the specific roles of sexual images in human financial decision making in everyday life, such as gambling behaviors in the casino. PMID:29649287

Interactive Video in Training. Computers in Personnel--Making Management Profitable.

ERIC Educational Resources Information Center

Copeland, Peter

Interactive video is achieved by merging the two powerful technologies of microcomputing and video. Using television as the vehicle for display, text and diagrams, filmic images, and sound can be used separately or in combination to achieve a specific training task. An interactive program can check understanding, determine progress, and challenge…

The use of digital spaceborne SAR data for the delineation of surface features indicative of malaria vector breeding habitats

NASA Technical Reports Server (NTRS)

Imhoff, M. L.; Vermillion, C. H.; Khan, F. A.

1984-01-01

An investigation to examine the utility of spaceborne radar image data to malaria vector control programs is described. Specific tasks involve an analysis of radar illumination geometry vs information content, the synergy of radar and multispectral data mergers, and automated information extraction techniques.

Implicit and Explicit Attention to Pictures and Words: An fMRI-Study of Concurrent Emotional Stimulus Processing.

PubMed

Flaisch, Tobias; Imhof, Martin; Schmälzle, Ralf; Wentz, Klaus-Ulrich; Ibach, Bernd; Schupp, Harald T

2015-01-01

The present study utilized functional magnetic resonance imaging (fMRI) to examine the neural processing of concurrently presented emotional stimuli under varying explicit and implicit attention demands. Specifically, in separate trials, participants indicated the category of either pictures or words. The words were placed over the center of the pictures and the picture-word compound-stimuli were presented for 1500 ms in a rapid event-related design. The results reveal pronounced main effects of task and emotion: the picture categorization task prompted strong activations in visual, parietal, temporal, frontal, and subcortical regions; the word categorization task evoked increased activation only in left extrastriate cortex. Furthermore, beyond replicating key findings regarding emotional picture and word processing, the results point to a dissociation of semantic-affective and sensory-perceptual processes for words: while emotional words engaged semantic-affective networks of the left hemisphere regardless of task, the increased activity in left extrastriate cortex associated with explicitly attending to words was diminished when the word was overlaid over an erotic image. Finally, we observed a significant interaction between Picture Category and Task within dorsal visual-associative regions, inferior parietal, and dorsolateral, and medial prefrontal cortices: during the word categorization task, activation was increased in these regions when the words were overlaid over erotic as compared to romantic pictures. During the picture categorization task, activity in these areas was relatively decreased when categorizing erotic as compared to romantic pictures. Thus, the emotional intensity of the pictures strongly affected brain regions devoted to the control of task-related word or picture processing. These findings are discussed with respect to the interplay of obligatory stimulus processing with task-related attentional control mechanisms.

Implicit and Explicit Attention to Pictures and Words: An fMRI-Study of Concurrent Emotional Stimulus Processing

PubMed Central

Flaisch, Tobias; Imhof, Martin; Schmälzle, Ralf; Wentz, Klaus-Ulrich; Ibach, Bernd; Schupp, Harald T.

2015-01-01

The present study utilized functional magnetic resonance imaging (fMRI) to examine the neural processing of concurrently presented emotional stimuli under varying explicit and implicit attention demands. Specifically, in separate trials, participants indicated the category of either pictures or words. The words were placed over the center of the pictures and the picture-word compound-stimuli were presented for 1500 ms in a rapid event-related design. The results reveal pronounced main effects of task and emotion: the picture categorization task prompted strong activations in visual, parietal, temporal, frontal, and subcortical regions; the word categorization task evoked increased activation only in left extrastriate cortex. Furthermore, beyond replicating key findings regarding emotional picture and word processing, the results point to a dissociation of semantic-affective and sensory-perceptual processes for words: while emotional words engaged semantic-affective networks of the left hemisphere regardless of task, the increased activity in left extrastriate cortex associated with explicitly attending to words was diminished when the word was overlaid over an erotic image. Finally, we observed a significant interaction between Picture Category and Task within dorsal visual-associative regions, inferior parietal, and dorsolateral, and medial prefrontal cortices: during the word categorization task, activation was increased in these regions when the words were overlaid over erotic as compared to romantic pictures. During the picture categorization task, activity in these areas was relatively decreased when categorizing erotic as compared to romantic pictures. Thus, the emotional intensity of the pictures strongly affected brain regions devoted to the control of task-related word or picture processing. These findings are discussed with respect to the interplay of obligatory stimulus processing with task-related attentional control mechanisms. PMID:26733895

Image quality enhancement for skin cancer optical diagnostics

NASA Astrophysics Data System (ADS)

Bliznuks, Dmitrijs; Kuzmina, Ilona; Bolocko, Katrina; Lihachev, Alexey

2017-12-01

The research presents image quality analysis and enhancement proposals in biophotonic area. The sources of image problems are reviewed and analyzed. The problems with most impact in biophotonic area are analyzed in terms of specific biophotonic task - skin cancer diagnostics. The results point out that main problem for skin cancer analysis is the skin illumination problems. Since it is often not possible to prevent illumination problems, the paper proposes image post processing algorithm - low frequency filtering. Practical results show diagnostic results improvement after using proposed filter. Along that, filter do not reduces diagnostic results' quality for images without illumination defects. Current filtering algorithm requires empirical tuning of filter parameters. Further work needed to test the algorithm in other biophotonic applications and propose automatic filter parameter selection.

Image based performance analysis of thermal imagers

NASA Astrophysics Data System (ADS)

Wegner, D.; Repasi, E.

2016-05-01

Due to advances in technology, modern thermal imagers resemble sophisticated image processing systems in functionality. Advanced signal and image processing tools enclosed into the camera body extend the basic image capturing capability of thermal cameras. This happens in order to enhance the display presentation of the captured scene or specific scene details. Usually, the implemented methods are proprietary company expertise, distributed without extensive documentation. This makes the comparison of thermal imagers especially from different companies a difficult task (or at least a very time consuming/expensive task - e.g. requiring the execution of a field trial and/or an observer trial). For example, a thermal camera equipped with turbulence mitigation capability stands for such a closed system. The Fraunhofer IOSB has started to build up a system for testing thermal imagers by image based methods in the lab environment. This will extend our capability of measuring the classical IR-system parameters (e.g. MTF, MTDP, etc.) in the lab. The system is set up around the IR- scene projector, which is necessary for the thermal display (projection) of an image sequence for the IR-camera under test. The same set of thermal test sequences might be presented to every unit under test. For turbulence mitigation tests, this could be e.g. the same turbulence sequence. During system tests, gradual variation of input parameters (e. g. thermal contrast) can be applied. First ideas of test scenes selection and how to assembly an imaging suite (a set of image sequences) for the analysis of imaging thermal systems containing such black boxes in the image forming path is discussed.

The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images

PubMed Central

Mitry, Danny; Zutis, Kris; Dhillon, Baljean; Peto, Tunde; Hayat, Shabina; Khaw, Kay-Tee; Morgan, James E.; Moncur, Wendy; Trucco, Emanuele; Foster, Paul J.

2016-01-01

Purpose Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. Methods We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. Results In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%–74%) and 87% (95% CI, 86%–88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91–0.96). For image annotation, a maximal Dice coefficient (∼0.6) was achieved with a consensus threshold of 0.25. Conclusions This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. Translational Relevance The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver timely, accurate, and cost-effective image analysis. PMID:27668130

The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images.

PubMed

Mitry, Danny; Zutis, Kris; Dhillon, Baljean; Peto, Tunde; Hayat, Shabina; Khaw, Kay-Tee; Morgan, James E; Moncur, Wendy; Trucco, Emanuele; Foster, Paul J

2016-09-01

Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%-74%) and 87% (95% CI, 86%-88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91-0.96). For image annotation, a maximal Dice coefficient (∼0.6) was achieved with a consensus threshold of 0.25. This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver timely, accurate, and cost-effective image analysis.

Imaginal, semantic, and surface-level processing of concrete and abstract words: an electrophysiological investigation.

PubMed

West, W C; Holcomb, P J

2000-11-01

Words representing concrete concepts are processed more quickly and efficiently than words representing abstract concepts. Concreteness effects have also been observed in studies using event-related brain potentials (ERPs). The aim of this study was to examine concrete and abstract words using both reaction time (RT) and ERP measurements to determine (1) at what point in the stream of cognitive processing concreteness effects emerge and (2) how different types of cognitive operations influence these concreteness effects. Three groups of subjects performed a sentence verification task in which the final word of each sentence was concrete or abstract. For each group the truthfulness judgment required either (1) image generation, (2) semantic decision, or (3) evaluation of surface characteristics. Concrete and abstract words produced similar RTs and ERPs in the surface task, suggesting that postlexical semantic processing is necessary to elicit concreteness effects. In both the semantic and imagery tasks, RTs were shorter for concrete than for abstract words. This difference was greatest in the imagery task. Also, in both of these tasks concrete words elicited more negative ERPs than abstract words between 300 and 550 msec (N400). This effect was widespread across the scalp and may reflect activation in a linguistic semantic system common to both concrete and abstract words. ERPs were also more negative for concrete than abstract words between 550 and 800 msec. This effect was more frontally distributed and was most evident in the imagery task. We propose that this later anterior effect represents a distinct ERP component (N700) that is sensitive to the use of mental imagery. The N700 may reflect the a access of specific characteristics of the imaged item or activation in a working memory system specific to mental imagery. These results also support the extended dual-coding hypothesis that superior associative connections and the use of mental imagery both contribute to processing advantages for concrete words over abstract words.

Real-time imaging through strongly scattering media: seeing through turbid media, instantly

PubMed Central

Sudarsanam, Sriram; Mathew, James; Panigrahi, Swapnesh; Fade, Julien; Alouini, Mehdi; Ramachandran, Hema

2016-01-01

Numerous everyday situations like navigation, medical imaging and rescue operations require viewing through optically inhomogeneous media. This is a challenging task as photons propagate predominantly diffusively (rather than ballistically) due to random multiple scattering off the inhomogenieties. Real-time imaging with ballistic light under continuous-wave illumination is even more challenging due to the extremely weak signal, necessitating voluminous data-processing. Here we report imaging through strongly scattering media in real-time and at rates several times the critical flicker frequency of the eye, so that motion is perceived as continuous. Two factors contributed to the speedup of more than three orders of magnitude over conventional techniques - the use of a simplified algorithm enabling processing of data on the fly, and the utilisation of task and data parallelization capabilities of typical desktop computers. The extreme simplicity of the technique, and its implementation with present day low-cost technology promises its utility in a variety of devices in maritime, aerospace, rail and road transport, in medical imaging and defence. It is of equal interest to the common man and adventure sportsperson like hikers, divers, mountaineers, who frequently encounter situations requiring realtime imaging through obscuring media. As a specific example, navigation under poor visibility is examined. PMID:27114106

Component-based target recognition inspired by human vision

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Agyepong, Kwabena

2009-05-01

In contrast with machine vision, human can recognize an object from complex background with great flexibility. For example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based target recognition method by simulating the human recognition process. The component templates (equivalent to the virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal response in visual process. A phase correlation matching algorithm is then applied to match the templates with the testing edge image. If all key component templates are matched with the examining object, then this object is recognized as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars). In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary results show that the component-based recognition method is very promising.

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

PubMed

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2015-08-01

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Task-based measures of image quality and their relation to radiation dose and patient risk

PubMed Central

Barrett, Harrison H.; Myers, Kyle J.; Hoeschen, Christoph; Kupinski, Matthew A.; Little, Mark P.

2015-01-01

The theory of task-based assessment of image quality is reviewed in the context of imaging with ionizing radiation, and objective figures of merit (FOMs) for image quality are summarized. The variation of the FOMs with the task, the observer and especially with the mean number of photons recorded in the image is discussed. Then various standard methods for specifying radiation dose are reviewed and related to the mean number of photons in the image and hence to image quality. Current knowledge of the relation between local radiation dose and the risk of various adverse effects is summarized, and some graphical depictions of the tradeoffs between image quality and risk are introduced. Then various dose-reduction strategies are discussed in terms of their effect on task-based measures of image quality. PMID:25564960

Advanced spinning disk-TIRF microscopy for faster imaging of the cell interior and the plasma membrane.

PubMed

Zobiak, Bernd; Failla, Antonio Virgilio

2018-03-01

Understanding the cellular processes that occur between the cytosol and the plasma membrane is an important task for biological research. Till now, however, it was not possible to combine fast and high-resolution imaging of both the isolated plasma membrane and the surrounding intracellular volume. Here, we demonstrate the combination of fast high-resolution spinning disk (SD) and total internal reflection fluorescence (TIRF) microscopy for specific imaging of the plasma membrane. A customised SD-TIRF microscope was used with specific design of the light paths that allowed, for the first time, live SD-TIRF experiments at high acquisition rates. A series of experiments is shown to demonstrate the feasibility and performance of our setup. © 2017 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography.

PubMed

Gang, Grace J; Siewerdsen, Jeffrey H; Stayman, J Webster

2017-12-01

This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

Shared and disorder-specific task-positive and default mode network dysfunctions during sustained attention in paediatric Attention-Deficit/Hyperactivity Disorder and obsessive/compulsive disorder.

PubMed

Norman, Luke J; Carlisi, Christina O; Christakou, Anastasia; Cubillo, Ana; Murphy, Clodagh M; Chantiluke, Kaylita; Simmons, Andrew; Giampietro, Vincent; Brammer, Michael; Mataix-Cols, David; Rubia, Katya

2017-01-01

Patients with Attention-Deficit/Hyperactivity Disorder (ADHD) and obsessive/compulsive disorder (OCD) share problems with sustained attention, and are proposed to share deficits in switching between default mode and task positive networks. The aim of this study was to investigate shared and disorder-specific brain activation abnormalities during sustained attention in the two disorders. Twenty boys with ADHD, 20 boys with OCD and 20 age-matched healthy controls aged between 12 and 18 years completed a functional magnetic resonance imaging (fMRI) version of a parametrically modulated sustained attention task with a progressively increasing sustained attention load. Performance and brain activation were compared between groups. Only ADHD patients were impaired in performance. Group by sustained attention load interaction effects showed that OCD patients had disorder-specific middle anterior cingulate underactivation relative to controls and ADHD patients, while ADHD patients showed disorder-specific underactivation in left dorsolateral prefrontal cortex/dorsal inferior frontal gyrus (IFG). ADHD and OCD patients shared left insula/ventral IFG underactivation and increased activation in posterior default mode network relative to controls, but had disorder-specific overactivation in anterior default mode regions, in dorsal anterior cingulate for ADHD and in anterior ventromedial prefrontal cortex for OCD. In sum, ADHD and OCD patients showed mostly disorder-specific patterns of brain abnormalities in both task positive salience/ventral attention networks with lateral frontal deficits in ADHD and middle ACC deficits in OCD, as well as in their deactivation patterns in medial frontal DMN regions. The findings suggest that attention performance in the two disorders is underpinned by disorder-specific activation patterns.

Magnetic resonance brain tissue segmentation based on sparse representations

NASA Astrophysics Data System (ADS)

Rueda, Andrea

2015-12-01

Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

Developmental dyscalculia: compensatory mechanisms in left intraparietal regions in response to nonsymbolic magnitudes.

PubMed

Kaufmann, Liane; Vogel, Stephan E; Starke, Marc; Kremser, Christian; Schocke, Michael; Wood, Guilherme

2009-08-05

Functional magnetic resonance imaging (fMRI) studies investigating the neural mechanisms underlying developmental dyscalculia are scarce and results are thus far inconclusive. Main aim of the present study is to investigate the neural correlates of nonsymbolic number magnitude processing in children with and without dyscalculia. 18 children (9 with dyscalculia) were asked to solve a non-symbolic number magnitude comparison task (finger patterns) during brain scanning. For the spatial control task identical stimuli were employed, instructions varying only (judgment of palm rotation). This design enabled us to present identical stimuli with identical visual processing requirements in the experimental and the control task. Moreover, because numerical and spatial processing relies on parietal brain regions, task-specific contrasts are expected to reveal true number-specific activations. Behavioral results during scanning reveal that despite comparable (almost at ceiling) performance levels, task-specific activations were stronger in dyscalculic children in inferior parietal cortices bilaterally (intraparietal sulcus, supramarginal gyrus, extending to left angular gyrus). Interestingly, fMRI signal strengths reflected a group x task interaction: relative to baseline, controls produced significant deactivations in (intra)parietal regions bilaterally in response to number but not spatial processing, while the opposite pattern emerged in dyscalculics. Moreover, beta weights in response to number processing differed significantly between groups in left - but not right - (intra)parietal regions (becoming even positive in dyscalculic children). Overall, findings are suggestive of (a) less consistent neural activity in right (intra)parietal regions upon processing nonsymbolic number magnitudes; and (b) compensatory neural activity in left (intra)parietal regions in developmental dyscalculia.

Developmental dyscalculia: compensatory mechanisms in left intraparietal regions in response to nonsymbolic magnitudes

PubMed Central

Kaufmann, Liane; Vogel, Stephan E; Starke, Marc; Kremser, Christian; Schocke, Michael; Wood, Guilherme

2009-01-01

Background Functional magnetic resonance imaging (fMRI) studies investigating the neural mechanisms underlying developmental dyscalculia are scarce and results are thus far inconclusive. Main aim of the present study is to investigate the neural correlates of nonsymbolic number magnitude processing in children with and without dyscalculia. Methods 18 children (9 with dyscalculia) were asked to solve a non-symbolic number magnitude comparison task (finger patterns) during brain scanning. For the spatial control task identical stimuli were employed, instructions varying only (judgment of palm rotation). This design enabled us to present identical stimuli with identical visual processing requirements in the experimental and the control task. Moreover, because numerical and spatial processing relies on parietal brain regions, task-specific contrasts are expected to reveal true number-specific activations. Results Behavioral results during scanning reveal that despite comparable (almost at ceiling) performance levels, task-specific activations were stronger in dyscalculic children in inferior parietal cortices bilaterally (intraparietal sulcus, supramarginal gyrus, extending to left angular gyrus). Interestingly, fMRI signal strengths reflected a group × task interaction: relative to baseline, controls produced significant deactivations in (intra)parietal regions bilaterally in response to number but not spatial processing, while the opposite pattern emerged in dyscalculics. Moreover, beta weights in response to number processing differed significantly between groups in left – but not right – (intra)parietal regions (becoming even positive in dyscalculic children). Conclusion Overall, findings are suggestive of (a) less consistent neural activity in right (intra)parietal regions upon processing nonsymbolic number magnitudes; and (b) compensatory neural activity in left (intra)parietal regions in developmental dyscalculia. PMID:19653919

Hallucination- and speech-specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task-based fMRI data: An fBIRN study.

PubMed

Lavigne, Katie M; Woodward, Todd S

2018-04-01

Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation. © 2017 Wiley Periodicals, Inc.

Reverse alignment "mirror image" visualization as a laparoscopic training tool improves task performance.

PubMed

Dunnican, Ward J; Singh, T Paul; Ata, Ashar; Bendana, Emma E; Conlee, Thomas D; Dolce, Charles J; Ramakrishnan, Rakesh

2010-06-01

Reverse alignment (mirror image) visualization is a disconcerting situation occasionally faced during laparoscopic operations. This occurs when the camera faces back at the surgeon in the opposite direction from which the surgeon's body and instruments are facing. Most surgeons will attempt to optimize trocar and camera placement to avoid this situation. The authors' objective was to determine whether the intentional use of reverse alignment visualization during laparoscopic training would improve performance. A standard box trainer was configured for reverse alignment, and 34 medical students and junior surgical residents were randomized to train with either forward alignment (DIRECT) or reverse alignment (MIRROR) visualization. Enrollees were tested on both modalities before and after a 4-week structured training program specific to their modality. Student's t test was used to determine differences in task performance between the 2 groups. Twenty-one participants completed the study (10 DIRECT, 11 MIRROR). There were no significant differences in performance time between DIRECT or MIRROR participants during forward or reverse alignment initial testing. At final testing, DIRECT participants had improved times only in forward alignment performance; they demonstrated no significant improvement in reverse alignment performance. MIRROR participants had significant time improvement in both forward and reverse alignment performance at final testing. Reverse alignment imaging for laparoscopic training improves task performance for both reverse alignment and forward alignment tasks. This may be translated into improved performance in the operating room when faced with reverse alignment situations. Minimal lab training can account for drastic adaptation to this environment.

Automated identification of the lung contours in positron emission tomography

NASA Astrophysics Data System (ADS)

Nery, F.; Silvestre Silva, J.; Ferreira, N. C.; Caramelo, F. J.; Faustino, R.

2013-03-01

Positron Emission Tomography (PET) is a nuclear medicine imaging technique that permits to analyze, in three dimensions, the physiological processes in vivo. One of the areas where PET has demonstrated its advantages is in the staging of lung cancer, where it offers better sensitivity and specificity than other techniques such as CT. On the other hand, accurate segmentation, an important procedure for Computer Aided Diagnostics (CAD) and automated image analysis, is a challenging task given the low spatial resolution and the high noise that are intrinsic characteristics of PET images. This work presents an algorithm for the segmentation of lungs in PET images, to be used in CAD and group analysis in a large patient database. The lung boundaries are automatically extracted from a PET volume through the application of a marker-driven watershed segmentation procedure which is robust to the noise. In order to test the effectiveness of the proposed method, we compared the segmentation results in several slices using our approach with the results obtained from manual delineation. The manual delineation was performed by nuclear medicine physicians that used a software routine that we developed specifically for this task. To quantify the similarity between the contours obtained from the two methods, we used figures of merit based on region and also on contour definitions. Results show that the performance of the algorithm was similar to the performance of human physicians. Additionally, we found that the algorithm-physician agreement is similar (statistically significant) to the inter-physician agreement.

Face matching impairment in developmental prosopagnosia.

PubMed

White, David; Rivolta, Davide; Burton, A Mike; Al-Janabi, Shahd; Palermo, Romina

2017-02-01

Developmental prosopagnosia (DP) is commonly referred to as 'face blindness', a term that implies a perceptual basis to the condition. However, DP presents as a deficit in face recognition and is diagnosed using memory-based tasks. Here, we test face identification ability in six people with DP, who are severely impaired on face memory tasks, using tasks that do not rely on memory. First, we compared DP to control participants on a standardized test of unfamiliar face matching using facial images taken on the same day and under standardized studio conditions (Glasgow Face Matching Test; GFMT). Scores for DP participants did not differ from normative accuracy scores on the GFMT. Second, we tested face matching performance on a test created using images that were sourced from the Internet and so varied substantially due to changes in viewing conditions and in a person's appearance (Local Heroes Test; LHT). DP participants showed significantly poorer matching accuracy on the LHT than control participants, for both unfamiliar and familiar face matching. Interestingly, this deficit is specific to 'match' trials, suggesting that people with DP may have particular difficulty in matching images of the same person that contain natural day-to-day variations in appearance. We discuss these results in the broader context of individual differences in face matching ability.

Expertise with unfamiliar objects is flexible to changes in task but not changes in class

PubMed Central

Tangen, Jason M.

2017-01-01

Perceptual expertise is notoriously specific and bound by familiarity; generalizing to novel or unfamiliar images, objects, identities, and categories often comes at some cost to performance. In forensic and security settings, however, examiners are faced with the task of discriminating unfamiliar images of unfamiliar objects within their general domain of expertise (e.g., fingerprints, faces, or firearms). The job of a fingerprint expert, for instance, is to decide whether two unfamiliar fingerprint images were left by the same unfamiliar finger (e.g., Smith’s left thumb), or two different unfamiliar fingers (e.g., Smith and Jones’s left thumb). Little is known about the limits of this kind of perceptual expertise. Here, we examine fingerprint experts’ and novices’ ability to distinguish fingerprints compared to inverted faces in two different tasks. Inverted face images serve as an ideal comparison because they vary naturally between and within identities, as do fingerprints, and people tend to be less accurate or more novice-like at distinguishing faces when they are presented in an inverted or unfamiliar orientation. In Experiment 1, fingerprint experts outperformed novices in locating categorical fingerprint outliers (i.e., a loop pattern in an array of whorls), but not inverted face outliers (i.e., an inverted male face in an array of inverted female faces). In Experiment 2, fingerprint experts were more accurate than novices at discriminating matching and mismatching fingerprints that were presented very briefly, but not so for inverted faces. Our data show that perceptual expertise with fingerprints can be flexible to changing task demands, but there can also be abrupt limits: fingerprint expertise did not generalize to an unfamiliar class of stimuli. We interpret these findings as evidence that perceptual expertise with unfamiliar objects is highly constrained by one’s experience. PMID:28574998

Model-based position correlation between breast images

NASA Astrophysics Data System (ADS)

Georgii, J.; Zöhrer, F.; Hahn, H. K.

2013-02-01

Nowadays, breast diagnosis is based on images of different projections and modalities, such that sensitivity and specificity of the diagnosis can be improved. However, this emburdens radiologists to find corresponding locations in these data sets, which is a time consuming task, especially since the resolution of the images increases and thus more and more data have to be considered in the diagnosis. Therefore, we aim at support radiologist by automatically synchronizing cursor positions between different views of the breast. Specifically, we present an automatic approach to compute the spatial correlation between MLO and CC mammogram or tomosynthesis projections of the breast. It is based on pre-computed finite element simulations of generic breast models, which are adapted to the patient-specific breast using a contour mapping approach. Our approach is designed to be fully automatic and efficient, such that it can be implemented directly into existing multimodal breast workstations. Additionally, it is extendable to support other breast modalities in future, too.

Differences in emotion modulation using cognitive reappraisal in individuals with and without suicidal ideation: An ERP study.

PubMed

Kudinova, Anastacia Y; Owens, Max; Burkhouse, Katie L; Barretto, Kenneth M; Bonanno, George A; Gibb, Brandon E

2016-08-01

Difficulties in emotion regulation have been associated with increased suicidal thoughts and behaviours. The majority of studies have examined self-reported use of emotion regulation strategies. In contrast, the current study focused on a direct measure of individuals' ability to use a specific emotion regulation strategy, cognitive reappraisal, using the late positive potential (LPP), an event-related potential component that reflects attention to emotional stimuli. Specifically, the cognitive reappraisal ability of 33 undergraduate students was assessed via an image-viewing task during which the participants had to passively view, increase or reduce their emotions in response to looking at neutral, positive or dysphoric images. We found that participants with a history of suicidal ideation (SI) had significantly higher LPP when asked to reduce negative emotion in response to dysphoric images, compared to individuals with no history of SI. These findings suggest that difficulties with using cognitive reappraisal, specifically to decrease negative affect, might be linked to suicide risk.

Neural organization of linguistic short-term memory is sensory modality-dependent: evidence from signed and spoken language.

PubMed

Pa, Judy; Wilson, Stephen M; Pickell, Herbert; Bellugi, Ursula; Hickok, Gregory

2008-12-01

Despite decades of research, there is still disagreement regarding the nature of the information that is maintained in linguistic short-term memory (STM). Some authors argue for abstract phonological codes, whereas others argue for more general sensory traces. We assess these possibilities by investigating linguistic STM in two distinct sensory-motor modalities, spoken and signed language. Hearing bilingual participants (native in English and American Sign Language) performed equivalent STM tasks in both languages during functional magnetic resonance imaging. Distinct, sensory-specific activations were seen during the maintenance phase of the task for spoken versus signed language. These regions have been previously shown to respond to nonlinguistic sensory stimulation, suggesting that linguistic STM tasks recruit sensory-specific networks. However, maintenance-phase activations common to the two languages were also observed, implying some form of common process. We conclude that linguistic STM involves sensory-dependent neural networks, but suggest that sensory-independent neural networks may also exist.

Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex

PubMed Central

Allen, William E.; Kauvar, Isaac V.; Chen, Michael Z.; Richman, Ethan B.; Yang, Samuel J.; Chan, Ken; Gradinaru, Viviana; Deverman, Benjamin E.; Luo, Liqun; Deisseroth, Karl

2017-01-01

SUMMARY The successful planning and execution of adaptive behaviors in mammals may require long-range coordination of neural networks throughout cerebral cortex. The neuronal implementation of signals that could orchestrate cortex-wide activity remains unclear. Here, we develop and apply methods for cortex-wide Ca2+ imaging in mice performing decision-making behavior and identify a global cortical representation of task engagement encoded in the activity dynamics of both single cells and superficial neuropil distributed across the majority of dorsal cortex. The activity of multiple molecularly defined cell types was found to reflect this representation with type-specific dynamics. Focal optogenetic inhibition tiled across cortex revealed a crucial role for frontal cortex in triggering this cortex-wide phenomenon; local inhibition of this region blocked both the cortex-wide response to task-initiating cues and the voluntary behavior. These findings reveal cell-type-specific processes in cortex for globally representing goal-directed behavior and identify a major cortical node that gates the global broadcast of task-related information. PMID:28521139

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Plasma level-dependent effects of methylphenidate on task-related functional magnetic resonance imaging signal changes.

PubMed

Müller, Ulrich; Suckling, J; Zelaya, F; Honey, G; Faessel, H; Williams, S C R; Routledge, C; Brown, J; Robbins, T W; Bullmore, E T

2005-08-01

Methylphenidate (MPH) is a dopamine and noradrenaline enhancing drug used to treat attentional deficits. Understanding of its cognition-enhancing effects and the neurobiological mechanisms involved, especially in elderly people, is currently incomplete. The aim of this study was to investigate the relationship between MPH plasma levels and brain activation during visuospatial attention and movement preparation. Twelve healthy elderly volunteers were scanned twice using functional magnetic resonance imaging (fMRI) after oral administration of MPH 20 mg or placebo in a within-subject design. The cognitive paradigm was a four-choice reaction time task presented at two levels of difficulty (with and without spatial cue). Plasma MPH levels were measured at six time points between 30 and 205 min after dosing. FMRI data were analysed using a linear model to estimate physiological response to the task and nonparametric permutation tests for inference. Lateral premotor and medial posterior parietal cortical activation was increased by MPH, on average, over both levels of task difficulty. There was considerable intersubject variability in the pharmacokinetics of MPH. Greater area under the plasma concentration-time curve was positively correlated with strength of activation in motor and premotor cortex, temporoparietal cortex and caudate nucleus during the difficult version of the task. This is the first pharmacokinetic/pharmacodynamic study to find an association between plasma levels of MPH and its modulatory effects on brain activation measured using fMRI. The results suggest that catecholaminergic mechanisms may be important in brain adaptivity to task difficulty and in task-specific recruitment of spatial attention systems.

Increase in MST activity correlates with visual motion learning: A functional MRI study of perceptual learning

PubMed Central

Larcombe, Stephanie J.; Kennard, Chris

2017-01-01

Abstract Repeated practice of a specific task can improve visual performance, but the neural mechanisms underlying this improvement in performance are not yet well understood. Here we trained healthy participants on a visual motion task daily for 5 days in one visual hemifield. Before and after training, we used functional magnetic resonance imaging (fMRI) to measure the change in neural activity. We also imaged a control group of participants on two occasions who did not receive any task training. While in the MRI scanner, all participants completed the motion task in the trained and untrained visual hemifields separately. Following training, participants improved their ability to discriminate motion direction in the trained hemifield and, to a lesser extent, in the untrained hemifield. The amount of task learning correlated positively with the change in activity in the medial superior temporal (MST) area. MST is the anterior portion of the human motion complex (hMT+). MST changes were localized to the hemisphere contralateral to the region of the visual field, where perceptual training was delivered. Visual areas V2 and V3a showed an increase in activity between the first and second scan in the training group, but this was not correlated with performance. The contralateral anterior hippocampus and bilateral dorsolateral prefrontal cortex (DLPFC) and frontal pole showed changes in neural activity that also correlated with the amount of task learning. These findings emphasize the importance of MST in perceptual learning of a visual motion task. Hum Brain Mapp 39:145–156, 2018. © 2017 Wiley Periodicals, Inc. PMID:28963815

Automatic mission planning algorithms for aerial collection of imaging-specific tasks

NASA Astrophysics Data System (ADS)

Sponagle, Paul; Salvaggio, Carl

2017-05-01

The rapid advancement and availability of small unmanned aircraft systems (sUAS) has led to many novel exploitation tasks utilizing that utilize this unique aerial imagery data. Collection of this unique data requires novel flight planning to accomplish the task at hand. This work describes novel flight planning to better support structure-from-motion missions to minimize occlusions, autonomous and periodic overflight of reflectance calibration panels to permit more efficient and accurate data collection under varying illumination conditions, and the collection of imagery data to study optical properties such as the bidirectional reflectance distribution function without disturbing the target in sensitive or remote areas of interest. These novel mission planning algorithms will provide scientists with additional tools to meet their future data collection needs.

An fMRI study of musicians with focal dystonia during tapping tasks.

PubMed

Kadota, Hiroshi; Nakajima, Yasoichi; Miyazaki, Makoto; Sekiguchi, Hirofumi; Kohno, Yutaka; Amako, Masatoshi; Arino, Hiroshi; Nemoto, Koichi; Sakai, Naotaka

2010-07-01

Musician's dystonia is a type of task specific dystonia for which the pathophysiology is not clear. In this study, we performed functional magnetic resonance imaging to investigate the motor-related brain activity associated with musician's dystonia. We compared brain activities measured from subjects with focal hand dystonia and normal (control) musicians during right-hand, left-hand, and both-hands tapping tasks. We found activations in the thalamus and the basal ganglia during the tapping tasks in the control group but not in the dystonia group. For both groups, we detected significant activations in the contralateral sensorimotor areas, including the premotor area and cerebellum, during each tapping task. Moreover, direct comparison between the dystonia and control groups showed that the dystonia group had greater activity in the ipsilateral premotor area during the right-hand tapping task and less activity in the left cerebellum during the both-hands tapping task. Thus, the dystonic musicians showed irregular activation patterns in the motor-association system. We suggest that irregular neural activity patterns in dystonic subjects reflect dystonic neural malfunction and consequent compensatory activity to maintain appropriate voluntary movements.

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

PubMed Central

Kent, Brendon W.; Yang, Fang-Chi; Burwell, Rebecca D.

2014-01-01

Neuropsychological tasks used in primates to investigate mechanisms of learning and memory are typically visually guided cognitive tasks. We have developed visual cognitive tasks for rats using the Floor Projection Maze1,2 that are optimized for visual abilities of rats permitting stronger comparisons of experimental findings with other species. In order to investigate neural correlates of learning and memory, we have integrated electrophysiological recordings into fully automated cognitive tasks on the Floor Projection Maze1,2. Behavioral software interfaced with an animal tracking system allows monitoring of the animal's behavior with precise control of image presentation and reward contingencies for better trained animals. Integration with an in vivo electrophysiological recording system enables examination of behavioral correlates of neural activity at selected epochs of a given cognitive task. We describe protocols for a model system that combines automated visual presentation of information to rodents and intracranial reward with electrophysiological approaches. Our model system offers a sophisticated set of tools as a framework for other cognitive tasks to better isolate and identify specific mechanisms contributing to particular cognitive processes. PMID:24638057

Bilateral transfer phenomenon: A functional magnetic resonance imaging pilot study of healthy subjects.

PubMed

Uggetti, Carla; Ausenda, Carlo D; Squarza, Silvia; Cadioli, Marcello; Grimoldi, Ludovico; Cerri, Cesare; Cariati, Maurizio

2016-08-01

The bilateral transfer of a motor skill is a physiological phenomenon: the development of a motor skill with one hand can trigger the development of the same ability of the other hand. The purpose of this study was to verify whether bilateral transfer is associated with a specific brain activation pattern using functional magnetic resonance imaging (fMRI). The motor task was implemented as the execution of the Nine Hole Peg Test. Fifteen healthy subjects (10 right-handers and five left-handers) underwent two identical fMRI runs performing the motor task with the non-dominant hand. Between the first and the second run, each subject was intensively trained for five minutes to perform the same motor task with the dominant hand. Comparing the two functional scans across the pool of subjects, a change of the motor activation pattern was observed. In particular, we observed, in the second run, a change in the activation pattern both in the cerebellum and in the cerebral cortex. We found activations in cortical areas involved in somatosensory integration, areas involved in procedural memory. Our study shows, in a small group of healthy subjects, the modification of the fMRI activation pathway of a motor task performed by the non-dominant hand after intensive exercise performing the same task with the dominant hand. © The Author(s) 2016.

Monkeys rely on recency of stimulus repetition when solving short-term memory tasks.

PubMed

Wittig, John H; Richmond, Barry J

2014-05-16

Seven monkeys performed variants of two short-term memory tasks that others have used to differentiate between selective and nonselective memory mechanisms. The first task was to view a list of sequentially presented images and identify whether a test matched any image from the list, but not a distractor from a preceding list. Performance was best when the test matched the most recently presented image. Response rates depended linearly on recency of repetition whether the test matched a sample from the current list or a distractor from a preceding list, suggesting nonselective memorization of all images viewed instead of just the sample images. The second task was to remember just the first image in a list selectively and ignore subsequent distractors. False alarms occurred frequently when the test matched a distractor presented near the beginning of the sequence. In a pilot experiment, response rates depended linearly on recency of repetition irrespective of whether the test matched the first image or a distractor, again suggesting nonselective memorization of all images instead of just the first image. Modification of the second task improved recognition of the first image, but did not abolish use of recency. Monkeys appear to perform nonspatial visual short-term memory tasks often (or exclusively) using a single, nonselective, memory mechanism that conveys the recency of stimulus repetition. Published by Cold Spring Harbor Laboratory Press.

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

NASA Technical Reports Server (NTRS)

Knasel, T. Michael

1996-01-01

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Task Dependence, Tissue Specificity, and Spatial Distribution of Widespread Activations in Large Single-Subject Functional MRI Datasets at 7T

PubMed Central

Gonzalez-Castillo, Javier; Hoy, Colin W.; Handwerker, Daniel A.; Roopchansingh, Vinai; Inati, Souheil J.; Saad, Ziad S.; Cox, Robert W.; Bandettini, Peter A.

2015-01-01

It was recently shown that when large amounts of task-based blood oxygen level–dependent (BOLD) data are combined to increase contrast- and temporal signal-to-noise ratios, the majority of the brain shows significant hemodynamic responses time-locked with the experimental paradigm. Here, we investigate the biological significance of such widespread activations. First, the relationship between activation extent and task demands was investigated by varying cognitive load across participants. Second, the tissue specificity of responses was probed using the better BOLD signal localization capabilities of a 7T scanner. Finally, the spatial distribution of 3 primary response types—namely positively sustained (pSUS), negatively sustained (nSUS), and transient—was evaluated using a newly defined voxel-wise waveshape index that permits separation of responses based on their temporal signature. About 86% of gray matter (GM) became significantly active when all data entered the analysis for the most complex task. Activation extent scaled with task load and largely followed the GM contour. The most common response type was nSUS BOLD, irrespective of the task. Our results suggest that widespread activations associated with extremely large single-subject functional magnetic resonance imaging datasets can provide valuable information about the functional organization of the brain that goes undetected in smaller sample sizes. PMID:25405938

Adaptive distance metric learning for diffusion tensor image segmentation.

PubMed

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

PubMed Central

Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

2014-01-01

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

Usage of fMRI for pre-surgical planning in brain tumor and vascular lesion patients: Task and statistical threshold effects on language lateralization☆☆☆

PubMed Central

Nadkarni, Tanvi N.; Andreoli, Matthew J.; Nair, Veena A.; Yin, Peng; Young, Brittany M.; Kundu, Bornali; Pankratz, Joshua; Radtke, Andrew; Holdsworth, Ryan; Kuo, John S.; Field, Aaron S.; Baskaya, Mustafa K.; Moritz, Chad H.; Meyerand, M. Elizabeth; Prabhakaran, Vivek

2014-01-01

Background and purpose Functional magnetic resonance imaging (fMRI) is a non-invasive pre-surgical tool used to assess localization and lateralization of language function in brain tumor and vascular lesion patients in order to guide neurosurgeons as they devise a surgical approach to treat these lesions. We investigated the effect of varying the statistical thresholds as well as the type of language tasks on functional activation patterns and language lateralization. We hypothesized that language lateralization indices (LIs) would be threshold- and task-dependent. Materials and methods Imaging data were collected from brain tumor patients (n = 67, average age 48 years) and vascular lesion patients (n = 25, average age 43 years) who received pre-operative fMRI scanning. Both patient groups performed expressive (antonym and/or letter-word generation) and receptive (tumor patients performed text-reading; vascular lesion patients performed text-listening) language tasks. A control group (n = 25, average age 45 years) performed the letter-word generation task. Results Brain tumor patients showed left-lateralization during the antonym-word generation and text-reading tasks at high threshold values and bilateral activation during the letter-word generation task, irrespective of the threshold values. Vascular lesion patients showed left-lateralization during the antonym and letter-word generation, and text-listening tasks at high threshold values. Conclusion Our results suggest that the type of task and the applied statistical threshold influence LI and that the threshold effects on LI may be task-specific. Thus identifying critical functional regions and computing LIs should be conducted on an individual subject basis, using a continuum of threshold values with different tasks to provide the most accurate information for surgical planning to minimize post-operative language deficits. PMID:25685705

Into the Looking Glass: Literacy Acquisition and Mirror Invariance in Preschool and First-Grade Children

ERIC Educational Resources Information Center

Fernandes, Tânia; Leite, Isabel; Kolinsky, Régine

2016-01-01

At what point in reading development does literacy impact object recognition and orientation processing? Is it specific to mirror images? To answer these questions, forty-six 5- to 7-year-old preschoolers and first graders performed two same-different tasks differing in the matching criterion-orientation-based versus shape-based (orientation…

Development of magnetic resonance technology for noninvasive boron quantification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradshaw, K.M.

1990-11-01

Boron magnetic resonance imaging (MRI) and spectroscopy (MRS) were developed in support of the noninvasive boron quantification task of the Idaho National Engineering Laboratory (INEL) Power Burst Facility/Boron Neutron Capture Therapy (PBF/BNCT) program. The hardware and software described in this report are modifications specific to a GE Signa{trademark} MRI system, release 3.X and are necessary for boron magnetic resonance operation. The technology developed in this task has been applied to obtaining animal pharmacokinetic data of boron compounds (drug time response) and the in-vivo localization of boron in animal tissue noninvasively. 9 refs., 21 figs.

Visual skills in airport-security screening.

PubMed

McCarley, Jason S; Kramer, Arthur F; Wickens, Christopher D; Vidoni, Eric D; Boot, Walter R

2004-05-01

An experiment examined visual performance in a simulated luggage-screening task. Observers participated in five sessions of a task requiring them to search for knives hidden in x-ray images of cluttered bags. Sensitivity and response times improved reliably as a result of practice. Eye movement data revealed that sensitivity increases were produced entirely by changes in observers' ability to recognize target objects, and not by changes in the effectiveness of visual scanning. Moreover, recognition skills were in part stimulus-specific, such that performance was degraded by the introduction of unfamiliar target objects. Implications for screener training are discussed.

SU-D-12A-06: A Comprehensive Parameter Analysis for Low Dose Cone-Beam CT Reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, W; Southern Medical University, Guangzhou; Yan, H

Purpose: There is always a parameter in compressive sensing based iterative reconstruction (IR) methods low dose cone-beam CT (CBCT), which controls the weight of regularization relative to data fidelity. A clear understanding of the relationship between image quality and parameter values is important. The purpose of this study is to investigate this subject based on experimental data and a representative advanced IR algorithm using Tight-frame (TF) regularization. Methods: Three data sets of a Catphan phantom acquired at low, regular and high dose levels are used. For each tests, 90 projections covering a 200-degree scan range are used for reconstruction. Threemore » different regions-of-interest (ROIs) of different contrasts are used to calculate contrast-to-noise ratios (CNR) for contrast evaluation. A single point structure is used to measure modulation transfer function (MTF) for spatial-resolution evaluation. Finally, we analyze CNRs and MTFs to study the relationship between image quality and parameter selections. Results: It was found that: 1) there is no universal optimal parameter. The optimal parameter value depends on specific task and dose level. 2) There is a clear trade-off between CNR and resolution. The parameter for the best CNR is always smaller than that for the best resolution. 3) Optimal parameters are also dose-specific. Data acquired under a high dose protocol require less regularization, yielding smaller optimal parameter values. 4) Comparing with conventional FDK images, TF-based CBCT images are better under a certain optimally selected parameters. The advantages are more obvious for low dose data. Conclusion: We have investigated the relationship between image quality and parameter values in the TF-based IR algorithm. Preliminary results indicate optimal parameters are specific to both the task types and dose levels, providing guidance for selecting parameters in advanced IR algorithms. This work is supported in part by NIH (1R01CA154747-01)« less

A functional MRI study of working memory task in euthymic bipolar disorder: evidence for task-specific dysfunction.

PubMed

Monks, Paul J; Thompson, Jill M; Bullmore, Edward T; Suckling, John; Brammer, Michael J; Williams, Steve C R; Simmons, Andrew; Giles, Nicola; Lloyd, Adrian J; Harrison, C Louise; Seal, Marc; Murray, Robin M; Ferrier, I Nicol; Young, Allan H; Curtis, Vivienne A

2004-12-01

Even when euthymic bipolar disorder patients can have persistent deficits in working memory, but the neural basis of this deficit remains unclear. We undertook an functional magnetic resonance imaging investigation of euthymic bipolar disorder patients performing two working memory paradigms; the two-back and Sternberg tasks, selected to examine the central executive and the phonological loop respectively. We hypothesized that neuronal dysfunction would be specific to the network underlying the executive rather than the phonological loop component of working memory. Twelve right-handed euthymic bipolar I males receiving lithium carbonate monotherapy were matched with 12 controls. The two-back task comprised a single working memory load contrasted with baseline vigilance condition. The Sternberg paradigm used a parametric design incorporating variable working memory load with fixed delay between presentation of an array of items to be remembered and a target item. Functional activation data were acquired during performance of the tasks and were analysed to produce brain activation maps representing significant group differences in activation (ANOVA). Load-response curves were derived from the Sternberg task data set. There were no significant between-group differences (t-test) in performance of the two-back task, or in 2 x 5 group by memory load ANOVA for the performance data from Sternberg task. In the two-back task, compared with controls bipolar disorder patients showed reductions in bilateral frontal, temporal and parietal activation, and increased activations with the left precentral, right medial frontal and left supramarginal gyri. No between-group differences were observed in the Sternberg task at any working memory load. Our findings support the notion that, in euthymic bipolar disorder, failure to engage fronto-executive function underpins the core neuropsychological deficits. Blackwell Munksgaard, 2004

Is a neutral expression also a neutral stimulus? A study with functional magnetic resonance.

PubMed

Carvajal, Fernando; Rubio, Sandra; Serrano, Juan M; Ríos-Lago, Marcos; Alvarez-Linera, Juan; Pacheco, Lara; Martín, Pilar

2013-08-01

Although neutral faces do not initially convey an explicit emotional message, it has been found that individuals tend to assign them an affective content. Moreover, previous research has shown that affective judgments are mediated by the task they have to perform. Using functional magnetic resonance imaging in 21 healthy participants, we focus this study on the cerebral activity patterns triggered by neutral and emotional faces in two different tasks (social or gender judgments). Results obtained, using conjunction analyses, indicated that viewing both emotional and neutral faces evokes activity in several similar brain areas indicating a common neural substrate. Moreover, neutral faces specifically elicit activation of cerebellum, frontal and temporal areas, while emotional faces involve the cuneus, anterior cingulated gyrus, medial orbitofrontal cortex, posterior superior temporal gyrus, precentral/postcentral gyrus and insula. The task selected was also found to influence brain activity, in that the social task recruited frontal areas while the gender task involved the posterior cingulated, inferior parietal lobule and middle temporal gyrus to a greater extent. Specifically, in the social task viewing neutral faces was associated with longer reaction times and increased activity of left dorsolateral frontal cortex compared with viewing facial expressions of emotions. In contrast, in the same task emotional expressions distinctively activated the left amygdale. The results are discussed taking into consideration the fact that, like other facial expressions, neutral expressions are usually assigned some emotional significance. However, neutral faces evoke a greater activation of circuits probably involved in more elaborate cognitive processing.

The emotional counting Stroop: a task for assessing emotional interference during brain imaging.

PubMed

Whalen, Paul J; Bush, George; Shin, Lisa M; Rauch, Scott L

2006-01-01

The emotional counting Stroop (ecStroop) is an emotional variant of the counting Stroop. Both of these tasks require a motor response instead of a spoken response for the purpose of minimizing head movement during functional MRI (fMRI). During this task, subjects report, by button press, the number of words (1-4) that appear on a screen, regardless of word meaning. Neutral word-control trials contain common words (e.g., 'cabinet' written three times), while interference trials contain emotional words (e.g., 'murder' written three times). The degree to which this task represents a true 'Stroop' interference task, in the sense that emotional words will increase motor-response times compared with neutral words, depends upon the subjects of the study and the words that are presented. Much research on the emotional Stroop task demonstrates that interference effects are observed in psychopathological groups in response to words that are specific to their disorder, and in normal subjects when the words are related to current concerns endorsed by them. The ecStroop task described here will produce reaction time-interference effects that are comparable to the traditional color-naming emotional Stroop. This protocol can be completed in approximately 20 min per subject. The protocol described here employs neutral words and emotional words that include general-negative words, as well as words specific to combat-related trauma. However, this protocol is amenable to any emotional word lists.

Does a 3D Image Improve Laparoscopic Motor Skills?

PubMed

Folaranmi, Semiu Eniola; Partridge, Roland W; Brennan, Paul M; Hennessey, Iain A M

2016-08-01

To quantitatively determine whether a three-dimensional (3D) image improves laparoscopic performance compared with a two-dimensional (2D) image. This is a prospective study with two groups of participants: novices (5) and experts (5). Individuals within each group undertook a validated laparoscopic task on a box simulator, alternating between 2D and a 3D laparoscopic image until they had repeated the task five times with each imaging modality. A dedicated motion capture camera was used to determine the time taken to complete the task (seconds) and instrument distance traveled (meters). Among the experts, the mean time taken to perform the task on the 3D image was significantly quicker than on the 2D image, 40.2 seconds versus 51.2 seconds, P < .0001. Among the novices, the mean task time again was significantly quicker on the 3D image, 56.4 seconds versus 82.7 seconds, P < .0001. There was no significant difference in the mean time it took a novice to perform the task using a 3D camera compared with an expert on a 2D camera, 56.4 seconds versus 51.3 seconds, P = .3341. The use of a 3D image confers a significant performance advantage over a 2D camera in quantitatively measured laparoscopic skills for both experts and novices. The use of a 3D image appears to improve a novice's performance to the extent that it is not statistically different from an expert using a 2D image.

A framework for optimizing micro-CT in dual-modality micro-CT/XFCT small-animal imaging system

NASA Astrophysics Data System (ADS)

Vedantham, Srinivasan; Shrestha, Suman; Karellas, Andrew; Cho, Sang Hyun

2017-09-01

Dual-modality Computed Tomography (CT)/X-ray Fluorescence Computed Tomography (XFCT) can be a valuable tool for imaging and quantifying the organ and tissue distribution of small concentrations of high atomic number materials in small-animal system. In this work, the framework for optimizing the micro-CT imaging system component of the dual-modality system is described, either when the micro-CT images are concurrently acquired with XFCT and using the x-ray spectral conditions for XFCT, or when the micro-CT images are acquired sequentially and independently of XFCT. This framework utilizes the cascaded systems analysis for task-specific determination of the detectability index using numerical observer models at a given radiation dose, where the radiation dose is determined using Monte Carlo simulations.

Active appearance model and deep learning for more accurate prostate segmentation on MRI

NASA Astrophysics Data System (ADS)

Cheng, Ruida; Roth, Holger R.; Lu, Le; Wang, Shijun; Turkbey, Baris; Gandler, William; McCreedy, Evan S.; Agarwal, Harsh K.; Choyke, Peter; Summers, Ronald M.; McAuliffe, Matthew J.

2016-03-01

Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to segment the prostate on MR images. The performance of the segmentation method is evaluated on 20 unseen MR image datasets. The proposed method combining AAM and Deep Learning achieves a mean Dice Similarity Coefficient (DSC) of 0.925 for whole 3D MR images of the prostate using axial cross-sections. The proposed model utilizes the adaptive atlas-based AAM model and Deep Learning to achieve significant segmentation accuracy.

Two-Dimensional Hermite Filters Simplify the Description of High-Order Statistics of Natural Images.

PubMed

Hu, Qin; Victor, Jonathan D

2016-09-01

Natural image statistics play a crucial role in shaping biological visual systems, understanding their function and design principles, and designing effective computer-vision algorithms. High-order statistics are critical for conveying local features, but they are challenging to study - largely because their number and variety is large. Here, via the use of two-dimensional Hermite (TDH) functions, we identify a covert symmetry in high-order statistics of natural images that simplifies this task. This emerges from the structure of TDH functions, which are an orthogonal set of functions that are organized into a hierarchy of ranks. Specifically, we find that the shape (skewness and kurtosis) of the distribution of filter coefficients depends only on the projection of the function onto a 1-dimensional subspace specific to each rank. The characterization of natural image statistics provided by TDH filter coefficients reflects both their phase and amplitude structure, and we suggest an intuitive interpretation for the special subspace within each rank.

Automated seeding-based nuclei segmentation in nonlinear optical microscopy.

PubMed

Medyukhina, Anna; Meyer, Tobias; Heuke, Sandro; Vogler, Nadine; Dietzek, Benjamin; Popp, Jürgen

2013-10-01

Nonlinear optical (NLO) microscopy based, e.g., on coherent anti-Stokes Raman scattering (CARS) or two-photon-excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy; there is a lack of robust and durable nuclei segmentation methods capable of accurate image processing in cases of variable image contrast, nuclear density, and type of investigated tissue. Nonetheless, such algorithms specifically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e.g., in pathology or intraoperatively for surgical guidance. In this paper, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian filter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods followed by the verification of the detected nuclei performs high average sensitivity and specificity when applied to various types of NLO microscopy images.

Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks.

PubMed

Ebina, Teppei; Masamizu, Yoshito; Tanaka, Yasuhiro R; Watakabe, Akiya; Hirakawa, Reiko; Hirayama, Yuka; Hira, Riichiro; Terada, Shin-Ichiro; Koketsu, Daisuke; Hikosaka, Kazuo; Mizukami, Hiroaki; Nambu, Atsushi; Sasaki, Erika; Yamamori, Tetsuo; Matsuzaki, Masanori

2018-05-14

Two-photon imaging in behaving animals has revealed neuronal activities related to behavioral and cognitive function at single-cell resolution. However, marmosets have posed a challenge due to limited success in training on motor tasks. Here we report the development of protocols to train head-fixed common marmosets to perform upper-limb movement tasks and simultaneously perform two-photon imaging. After 2-5 months of training sessions, head-fixed marmosets can control a manipulandum to move a cursor to a target on a screen. We conduct two-photon calcium imaging of layer 2/3 neurons in the motor cortex during this motor task performance, and detect task-relevant activity from multiple neurons at cellular and subcellular resolutions. In a two-target reaching task, some neurons show direction-selective activity over the training days. In a short-term force-field adaptation task, some neurons change their activity when the force field is on. Two-photon calcium imaging in behaving marmosets may become a fundamental technique for determining the spatial organization of the cortical dynamics underlying action and cognition.

Distorted images of one's own body activates the prefrontal cortex and limbic/paralimbic system in young women: a functional magnetic resonance imaging study.

PubMed

Kurosaki, Mitsuhaya; Shirao, Naoko; Yamashita, Hidehisa; Okamoto, Yasumasa; Yamawaki, Shigeto

2006-02-15

Our aim was to study the gender differences in brain activation upon viewing visual stimuli of distorted images of one's own body. We performed functional magnetic resonance imaging on 11 healthy young men and 11 healthy young women using the "body image tasks" which consisted of fat, real, and thin shapes of the subject's own body. Comparison of the brain activation upon performing the fat-image task versus real-image task showed significant activation of the bilateral prefrontal cortex and left parahippocampal area including the amygdala in the women, and significant activation of the right occipital lobe including the primary and secondary visual cortices in the men. Comparison of brain activation upon performing the thin-image task versus real-image task showed significant activation of the left prefrontal cortex, left limbic area including the cingulate gyrus and paralimbic area including the insula in women, and significant activation of the occipital lobe including the left primary and secondary visual cortices in men. These results suggest that women tend to perceive distorted images of their own bodies by complex cognitive processing of emotion, whereas men tend to perceive distorted images of their own bodies by object visual processing and spatial visual processing.

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.

PubMed

Wei, Xiu-Shen; Luo, Jian-Hao; Wu, Jianxin; Zhou, Zhi-Hua

2017-06-01

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

Radiologists' confidence in detecting abnormalities on chest images and their subjective judgments of image quality

NASA Astrophysics Data System (ADS)

King, Jill L.; Gur, David; Rockette, Howard E.; Curtin, Hugh D.; Obuchowski, Nancy A.; Thaete, F. Leland; Britton, Cynthia A.; Metz, Charles E.

1991-07-01

The relationship between subjective judgments of image quality for the performance of specific detection tasks and radiologists' confidence level in arriving at correct diagnoses was investigated in two studies in which 12 readers, using a total of three different display environments, interpreted a series of 300 PA chest images. The modalities used were conventional films, laser-printed films, and high-resolution CRT display of digitized images. For the detection of interstitial disease, nodules, and pneumothoraces, there was no statistically significant correlation (Spearman rho) between subjective ratings of quality and radiologists' confidence in detecting these abnormalities. However, in each study, for all modalities and all readers but one, a small but statistically significant correlation was found between the radiologists' ability to correctly and confidently rule out interstitial disease and their subjective ratings of image quality.

DiversePathsJ: diverse shortest paths for bioimage analysis.

PubMed

Uhlmann, Virginie; Haubold, Carsten; Hamprecht, Fred A; Unser, Michael

2018-02-01

We introduce a formulation for the general task of finding diverse shortest paths between two end-points. Our approach is not linked to a specific biological problem and can be applied to a large variety of images thanks to its generic implementation as a user-friendly ImageJ/Fiji plugin. It relies on the introduction of additional layers in a Viterbi path graph, which requires slight modifications to the standard Viterbi algorithm rules. This layered graph construction allows for the specification of various constraints imposing diversity between solutions. The software allows obtaining a collection of diverse shortest paths under some user-defined constraints through a convenient and user-friendly interface. It can be used alone or be integrated into larger image analysis pipelines. http://bigwww.epfl.ch/algorithms/diversepathsj. michael.unser@epfl.ch or fred.hamprecht@iwr.uni-heidelberg.de. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades.

PubMed

Orchard, Garrick; Jayawant, Ajinkya; Cohen, Gregory K; Thakor, Nitish

2015-01-01

Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

A ganglion-cell-based primary image representation method and its contribution to object recognition

NASA Astrophysics Data System (ADS)

Wei, Hui; Dai, Zhi-Long; Zuo, Qing-Song

2016-10-01

A visual stimulus is represented by the biological visual system at several levels: in the order from low to high levels, they are: photoreceptor cells, ganglion cells (GCs), lateral geniculate nucleus cells and visual cortical neurons. Retinal GCs at the early level need to represent raw data only once, but meet a wide number of diverse requests from different vision-based tasks. This means the information representation at this level is general and not task-specific. Neurobiological findings have attributed this universal adaptation to GCs' receptive field (RF) mechanisms. For the purposes of developing a highly efficient image representation method that can facilitate information processing and interpretation at later stages, here we design a computational model to simulate the GC's non-classical RF. This new image presentation method can extract major structural features from raw data, and is consistent with other statistical measures of the image. Based on the new representation, the performances of other state-of-the-art algorithms in contour detection and segmentation can be upgraded remarkably. This work concludes that applying sophisticated representation schema at early state is an efficient and promising strategy in visual information processing.

Recurrent neural networks for breast lesion classification based on DCE-MRIs

NASA Astrophysics Data System (ADS)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2018-02-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03).

A compact eyetracked optical see-through head-mounted display

NASA Astrophysics Data System (ADS)

Hua, Hong; Gao, Chunyu

2012-03-01

An eye-tracked head-mounted display (ET-HMD) system is able to display virtual images as a classical HMD does, while additionally tracking the gaze direction of the user. There is ample evidence that a fully-integrated ETHMD system offers multi-fold benefits, not only to fundamental scientific research but also to emerging applications of such technology. For instance eyetracking capability in HMDs adds a very valuable tool and objective metric for scientists to quantitatively assess user interaction with 3D environments and investigate the effectiveness of various 3D visualization technologies for various specific tasks including training, education, and augmented cognition tasks. In this paper, we present an innovative optical approach to the design of an optical see-through ET-HMD system based on freeform optical technology and an innovative optical scheme that uniquely combines the display optics with the eye imaging optics. A preliminary design of the described ET-HMD system will be presented.

Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex

PubMed Central

Poort, Jasper; Khan, Adil G.; Pachitariu, Marius; Nemri, Abdellatif; Orsolic, Ivana; Krupic, Julija; Bauza, Marius; Sahani, Maneesh; Keller, Georg B.; Mrsic-Flogel, Thomas D.; Hofer, Sonja B.

2015-01-01

Summary We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli. PMID:26051421

Measurement of food-related approach-avoidance biases: Larger biases when food stimuli are task relevant.

PubMed

Lender, Anja; Meule, Adrian; Rinck, Mike; Brockmeyer, Timo; Blechert, Jens

2018-06-01

Strong implicit responses to food have evolved to avoid energy depletion but contribute to overeating in today's affluent environments. The Approach-Avoidance Task (AAT) supposedly assesses implicit biases in response to food stimuli: Participants push pictures on a monitor "away" or pull them "near" with a joystick that controls a corresponding image zoom. One version of the task couples movement direction with image content-independent features, for example, pulling blue-framed images and pushing green-framed images regardless of content ('irrelevant feature version'). However, participants might selectively attend to this feature and ignore image content and, thus, such a task setup might underestimate existing biases. The present study tested this attention account by comparing two irrelevant feature versions of the task with either a more peripheral (image frame color: green vs. blue) or central (small circle vs. cross overlaid over the image content) image feature as response instruction to a 'relevant feature version', in which participants responded to the image content, thus making it impossible to ignore that content. Images of chocolate-containing foods and of objects were used, and several trait and state measures were acquired to validate the obtained biases. Results revealed a robust approach bias towards food only in the relevant feature condition. Interestingly, a positive correlation with state chocolate craving during the task was found when all three conditions were combined, indicative of criterion validity of all three versions. However, no correlations were found with trait chocolate craving. Results provide a strong case for the relevant feature version of the AAT for bias measurement. They also point to several methodological avenues for future research around selective attention in the irrelevant versions and task validity regarding trait vs. state variables. Copyright © 2018 Elsevier Ltd. All rights reserved.

Delegation of medical tasks in French radiation oncology departments: current situation and impact on residents' training.

PubMed

Thureau, S; Challand, T; Bibault, J-E; Biau, J; Cervellera, M; Diaz, O; Faivre, J-C; Fumagalli, I; Leroy, T; Lescut, N; Martin, V; Pichon, B; Riou, O; Dubray, B; Giraud, P; Hennequin, C

2013-10-01

A national survey was conducted among the radiation oncology residents about their clinical activities and responsibilities. The aim was to evaluate the clinical workload and to assess how medical tasks are delegated and supervised. A first questionnaire was administered to radiation oncology residents during a national course. A second questionnaire was mailed to 59 heads of departments. The response rate was 62% for radiation oncology residents (99 questionnaires) and 51% for heads of department (30). Eighteen heads of department (64%) declared having written specifications describing the residents' clinical tasks and roles, while only 31 radiation oncology residents (34%) knew about such a document (P=0.009). A majority of residents were satisfied with the amount of medical tasks that were delegated to them. Older residents complained about insufficient exposure to new patient's consultation, treatment planning and portal images validation. The variations observed between departments may induce heterogeneous trainings and should be addressed specifically. National specifications are necessary to reduce heterogeneities in training, and to insure that the residents' training covers all the professional skills required to practice radiation oncology. A frame endorsed by academic and professional societies would also clarify the responsibilities of both residents and seniors. Copyright © 2013 Société française de radiothérapie oncologique (SFRO). Published by Elsevier SAS. All rights reserved.

Functional anatomy of temporal organisation and domain-specificity of episodic memory retrieval.

PubMed

Kwok, Sze Chai; Shallice, Tim; Macaluso, Emiliano

2012-10-01

Episodic memory provides information about the "when" of events as well as "what" and "where" they happened. Using functional imaging, we investigated the domain specificity of retrieval-related processes following encoding of complex, naturalistic events. Subjects watched a 42-min TV episode, and 24h later, made discriminative choices of scenes from the clip during fMRI. Subjects were presented with two scenes and required to either choose the scene that happened earlier in the film (Temporal), or the scene with a correct spatial arrangement (Spatial), or the scene that had been shown (Object). We identified a retrieval network comprising the precuneus, lateral and dorsal parietal cortex, middle frontal and medial temporal areas. The precuneus and angular gyrus are associated with temporal retrieval, with precuneal activity correlating negatively with temporal distance between two happenings at encoding. A dorsal fronto-parietal network engages during spatial retrieval, while antero-medial temporal regions activate during object-related retrieval. We propose that access to episodic memory traces involves different processes depending on task requirements. These include memory-searching within an organised knowledge structure in the precuneus (Temporal task), online maintenance of spatial information in dorsal fronto-parietal cortices (Spatial task) and combining scene-related spatial and non-spatial information in the hippocampus (Object task). Our findings support the proposal of process-specific dissociations of retrieval. Copyright © 2012 Elsevier Ltd. All rights reserved.

Functional anatomy of temporal organisation and domain-specificity of episodic memory retrieval

PubMed Central

Kwok, Sze Chai; Shallice, Tim; Macaluso, Emiliano

2013-01-01

Episodic memory provides information about the “when” of events as well as “what” and “where” they happened. Using functional imaging, we investigated the domain specificity of retrieval-related processes following encoding of complex, naturalistic events. Subjects watched a 42-min TV episode, and 24 h later, made discriminative choices of scenes from the clip during fMRI. Subjects were presented with two scenes and required to either choose the scene that happened earlier in the film (Temporal), or the scene with a correct spatial arrangement (Spatial), or the scene that had been shown (Object). We identified a retrieval network comprising the precuneus, lateral and dorsal parietal cortex, middle frontal and medial temporal areas. The precuneus and angular gyrus are associated with temporal retrieval, with precuneal activity correlating negatively with temporal distance between two happenings at encoding. A dorsal fronto-parietal network engages during spatial retrieval, while antero-medial temporal regions activate during object-related retrieval. We propose that access to episodic memory traces involves different processes depending on task requirements. These include memory-searching within an organised knowledge structure in the precuneus (Temporal task), online maintenance of spatial information in dorsal fronto-parietal cortices (Spatial task) and combining scene-related spatial and non-spatial information in the hippocampus (Object task). Our findings support the proposal of process-specific dissociations of retrieval. PMID:22877840

Multispectral image fusion for target detection

NASA Astrophysics Data System (ADS)

Leviner, Marom; Maltz, Masha

2009-09-01

Various different methods to perform multi-spectral image fusion have been suggested, mostly on the pixel level. However, the jury is still out on the benefits of a fused image compared to its source images. We present here a new multi-spectral image fusion method, multi-spectral segmentation fusion (MSSF), which uses a feature level processing paradigm. To test our method, we compared human observer performance in an experiment using MSSF against two established methods: Averaging and Principle Components Analysis (PCA), and against its two source bands, visible and infrared. The task that we studied was: target detection in the cluttered environment. MSSF proved superior to the other fusion methods. Based on these findings, current speculation about the circumstances in which multi-spectral image fusion in general and specific fusion methods in particular would be superior to using the original image sources can be further addressed.

Teaching People and Machines to Enhance Images

NASA Astrophysics Data System (ADS)

Berthouzoz, Floraine Sara Martianne

Procedural tasks such as following a recipe or editing an image are very common. They require a person to execute a sequence of operations (e.g. chop onions, or sharpen the image) in order to achieve the goal of the task. People commonly use step-by-step tutorials to learn these tasks. We focus on software tutorials, more specifically photo manipulation tutorials, and present a set of tools and techniques to help people learn, compare and automate photo manipulation procedures. We describe three different systems that are each designed to help with a different stage in acquiring procedural knowledge. Today, people primarily rely on hand-crafted tutorials in books and on websites to learn photo manipulation procedures. However, putting together a high quality step-by-step tutorial is a time-consuming process. As a consequence, many online tutorials are poorly designed which can lead to confusion and slow down the learning process. We present a demonstration-based system for automatically generating succinct step-by-step visual tutorials of photo manipulations. An author first demonstrates the manipulation using an instrumented version of GIMP (GNU Image Manipulation Program) that records all changes in interface and application state. From the example recording, our system automatically generates tutorials that illustrate the manipulation using images, text, and annotations. It leverages automated image labeling (recognition of facial features and outdoor scene structures in our implementation) to generate more precise text descriptions of many of the steps in the tutorials. A user study finds that our tutorials are effective for learning the steps of a procedure; users are 20-44% faster and make 60-95% fewer errors when using our tutorials than when using screencapture video tutorials or hand-designed tutorials. We also demonstrate a new interface that allows learners to navigate, explore and compare large collections (i.e. thousands) of photo manipulation tutorials based on their command-level structure. Sites such as tutorialized.com or good-tutorials.com collect tens of thousands of photo manipulation tutorials. These collections typically contain many different tutorials for the same task. For example, there are many different tutorials that describe how to recolor the hair of a person in an image. Learners often want to compare these tutorials to understand the different ways a task can be done. They may also want to identify common strategies that are used across tutorials for a variety of tasks. However, the large number of tutorials in these collections and their inconsistent formats can make it difficult for users to systematically explore and compare them. Current tutorial collections do not exploit the underlying command-level structure of tutorials, and to explore the collection users have to either page through long lists of tutorial titles or perform keyword searches on the natural language tutorial text. We present a new browsing interface to help learners navigate, explore and compare collections of photo manipulation tutorials based on their command-level structure. Our browser indexes tutorials by their commands, identifies common strategies within the tutorial collection, and highlights the similarities and differences between sets of tutorials that execute the same task. User feedback suggests that our interface is easy to understand and use, and that users find command-level browsing to be useful for exploring large tutorial collections. They strongly preferred to explore tutorial collections with our browser over keyword search. Finally, we present a framework for generating content-adaptive macros (programs) that can transfer complex photo manipulation procedures to new target images. After learners master a photo manipulation procedure, they often repeatedly apply it to multiple images. For example, they might routinely apply the same vignetting effect to all their photographs. This process can be very tedious especially for procedures that involve many steps. While image manipulation programs provide basic macro authoring tools that allow users to record and then replay a sequence of operations, these macros are very brittle and cannot adapt to new images. We present a more comprehensive approach for generating content-adaptive macros that can automatically transfer operations to new target images. To create these macro, we make use of multiple training demonstrations. Specifically, we use automated image labeling and machine learning techniques to to adapt the parameters of each operation to the new image content. We show that our framework is able to learn a large class of the most commonly-used manipulations using as few as 20 training demonstrations. Our content-adaptive macros allow users to transfer photo manipulation procedures with a single button click and thereby significantly simplify repetitive procedures.

Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion.

PubMed

Li, Gang; Liu, Tianming; Nie, Jingxin; Guo, Lei; Malicki, Jarema; Mara, Andrew; Holley, Scott A; Xia, Weiming; Wong, Stephen T C

2007-10-01

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.

Using In-vivo Fluorescence Imaging in Personalized Cancer Diagnostics and Therapy, an Image and Treat Paradigm

PubMed Central

Ardeshirpour, Yasaman; Chernomordik, Victor; Capala, Jacek; Hassan, Moinuddin; Zielinsky, Rafal; Griffiths, Gary; Achilefu, Samuel; Smith, Paul; Gandjbakhckhe, Amir

2013-01-01

The major goal in developing drugs targeting specific tumor receptors, such as Monoclonal AntiBodies (MAB), is to make a drug compound that targets selectively the cancer-causing biomarkers, inhibits their functionality, and/or delivers the toxin specifically to the malignant cells. Recent advances in MABs show that their efficacy depends strongly on characterization of tumor biomarkers. Therefore, one of the main tasks in cancer diagnostics and treatment is to develop non-invasive in-vivo imaging techniques for detection of cancer biomarkers and monitoring their down regulation during the treatment. Such methods can potentially result in a new imaging and treatment paradigm for cancer therapy. In this article we have reviewed fluorescence imaging approaches, including those developed in our group, to detect and monitor Human Epidermal Growth Factor 2 (HER2) receptors before and during therapy. Transition of these techniques from the bench to bedside is the ultimate goal of our project. Similar approaches can be used potentially for characterization of other cancer related cell biomarkers. PMID:22066595

Creating a classification of image types in the medical literature for visual categorization

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

2012-02-01

Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management.

PubMed

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).

Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management

PubMed Central

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches). PMID:23483997

Determination of hemispheric language dominance using functional magnetic resonance imaging and the Shiritori (Japanese word chain) task in patients with epilepsy: Comparison with the Wada test.

PubMed

Kashida, Yumi; Otsubo, Toshiaki; Hanaya, Ryosuke; Kodabashi, Atsushi; Tsumagari, Noriko; Sugata, Sei; Hosoyama, Hiroshi; Iida, Koji; Nakamura, Katsumi; Tokimura, Hiroshi; Fujimoto, Toshiro; Arita, Kazunori

2016-08-01

The Wada test has been the gold standard for determining hemispheric language dominance (HLD) in the presurgical evaluation of patients scheduled for neurosurgical procedures. As it poses inherent risks associated with intra-arterial catheter techniques and as it occasionally fails to indicate language dominance, an alternative reliable test is needed. We quantitatively assessed the results of functional magnetic resonance imaging (fMRI) using the Shiritori task, a Japanese word chain, to identify the threshold for correctly predicting HLD. The subjects were 28 patients with intractable epilepsy scheduled to undergo the Wada test and focus resection. We set the region of interest (ROI) on the bilateral Brodmann areas 44 and 45 (BA 44 and 45). To compare the functional activity at both ROIs we calculated the language laterality index (LI) using the formula: [VL-VR]/[VL+VR]×100, where VL and VR indicated the number of activated voxels in the left and right ROIs, respectively. As 2 patients were excluded due to the lack of activation in either ROI, the final study population consisted of 26 patients. By the Wada test, HLD was left in 20, right in 3, and equivocal in 3. At a cut-off of LI+50, the predictive sensitivity and specificity for left HLD were 85% (17/20) and 100%; right HLD was predicted in a single patient (sensitivity 33.3%, specificity 100%). The fMRI using the Shiritori task showed good activation in ROI of BA 44 and 45. At a cut-off of LI+50, LI of BA 44 and 45 predicted HLD identified by the Wada test with high specificity. Copyright © 2016 Elsevier B.V. All rights reserved.

Transferring cognitive tasks between brain imaging modalities: implications for task design and results interpretation in FMRI studies.

PubMed

Warbrick, Tracy; Reske, Martina; Shah, N Jon

2014-09-22

As cognitive neuroscience methods develop, established experimental tasks are used with emerging brain imaging modalities. Here transferring a paradigm (the visual oddball task) with a long history of behavioral and electroencephalography (EEG) experiments to a functional magnetic resonance imaging (fMRI) experiment is considered. The aims of this paper are to briefly describe fMRI and when its use is appropriate in cognitive neuroscience; illustrate how task design can influence the results of an fMRI experiment, particularly when that task is borrowed from another imaging modality; explain the practical aspects of performing an fMRI experiment. It is demonstrated that manipulating the task demands in the visual oddball task results in different patterns of blood oxygen level dependent (BOLD) activation. The nature of the fMRI BOLD measure means that many brain regions are found to be active in a particular task. Determining the functions of these areas of activation is very much dependent on task design and analysis. The complex nature of many fMRI tasks means that the details of the task and its requirements need careful consideration when interpreting data. The data show that this is particularly important in those tasks relying on a motor response as well as cognitive elements and that covert and overt responses should be considered where possible. Furthermore, the data show that transferring an EEG paradigm to an fMRI experiment needs careful consideration and it cannot be assumed that the same paradigm will work equally well across imaging modalities. It is therefore recommended that the design of an fMRI study is pilot tested behaviorally to establish the effects of interest and then pilot tested in the fMRI environment to ensure appropriate design, implementation and analysis for the effects of interest.

Task positive and default mode networks during a working memory in children with primary monosymptomatic nocturnal enuresis and healthy controls.

PubMed

Zhang, Kaihua; Ma, Jun; Lei, Du; Wang, Mengxing; Zhang, Jilei; Du, Xiaoxia

2015-10-01

Nocturnal enuresis is a common developmental disorder in children, and primary monosymptomatic nocturnal enuresis (PMNE) is the dominant subtype. This study investigated brain functional abnormalities that are specifically related to working memory in children with PMNE using function magnetic resonance imaging (fMRI) in combination with an n-back task. Twenty children with PMNE and 20 healthy children, group-matched for age and sex, participated in this experiment. Several brain regions exhibited reduced activation during the n-back task in children with PMNE, including the right precentral gyrus and the right inferior parietal lobule extending to the postcentral gyrus. Children with PMNE exhibited decreased cerebral activation in the task-positive network, increased task-related cerebral deactivation during a working memory task, and longer response times. Patients exhibited different brain response patterns to different levels of working memory and tended to compensate by greater default mode network deactivation to sustain normal working memory function. Our results suggest that children with PMNE have potential working memory dysfunction.

Anatomical background and generalized detectability in tomosynthesis and cone-beam CT.

PubMed

Gang, G J; Tward, D J; Lee, J; Siewerdsen, J H

2010-05-01

Anatomical background presents a major impediment to detectability in 2D radiography as well as 3D tomosynthesis and cone-beam CT (CBCT). This article incorporates theoretical and experimental analysis of anatomical background "noise" in cascaded systems analysis of 2D and 3D imaging performance to yield "generalized" metrics of noise-equivalent quanta (NEQ) and detectability index as a function of the orbital extent of the (circular arc) source-detector orbit. A physical phantom was designed based on principles of fractal self-similarity to exhibit power-law spectral density (kappa/Fbeta) comparable to various anatomical sites (e.g., breast and lung). Background power spectra [S(B)(F)] were computed as a function of source-detector orbital extent, including tomosynthesis (approximately 10 degrees -180 degrees) and CBCT (180 degrees + fan to 360 degrees) under two acquisition schemes: (1) Constant angular separation between projections (variable dose) and (2) constant total number of projections (constant dose). The resulting S(B) was incorporated in the generalized NEQ, and detectability index was computed from 3D cascaded systems analysis for a variety of imaging tasks. The phantom yielded power-law spectra within the expected spatial frequency range, quantifying the dependence of clutter magnitude (kappa) and correlation (beta) with increasing tomosynthesis angle. Incorporation of S(B) in the 3D NEQ provided a useful framework for analyzing the tradeoffs among anatomical, quantum, and electronic noise with dose and orbital extent. Distinct implications are posed for breast and chest tomosynthesis imaging system design-applications varying significantly in kappa and beta, and imaging task and, therefore, in optimal selection of orbital extent, number of projections, and dose. For example, low-frequency tasks (e.g., soft-tissue masses or nodules) tend to benefit from larger orbital extent and more fully 3D tomographic imaging, whereas high-frequency tasks (e.g., microcalcifications) require careful, application-specific selection of orbital extent and number of projections to minimize negative effects of quantum and electronic noise. The complex tradeoffs among anatomical background, quantum noise, and electronic noise in projection imaging, tomosynthesis, and CBCT can be described by generalized cascaded systems analysis, providing a useful framework for system design and optimization.

Noticing spiders on the left: Evidence on attentional bias and spider fear in the inattentional blindness paradigm.

PubMed

Brailsford, Richard; Catherwood, Di; Tyson, Philip J; Edgar, Graham

2014-01-01

Attentional biases in anxiety disorders have been assessed primarily using three types of experiment: the emotional Stroop task, the probe-detection task, and variations of the visual search task. It is proposed that the inattentional blindness procedure has the ability to overcome limitations of these paradigms in regard to identifying the components of attentional bias. Three experiments examined attentional responding to spider images in individuals with low and moderate to high spider fear. The results demonstrate that spider fear causes a bias in the engage component of visual attention and this is specific to stimuli presented in the left visual field (i.e., to the right hemisphere). The implications of the results are discussed and recommendations for future research are made.

WE-H-207B-03: MRI Guidance in the Radiation Therapy Clinic: Site-Specific Discussions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shang, C.

2016-06-15

In recent years, steady progress has been made towards the implementation of MRI in external beam radiation therapy for processes ranging from treatment simulation to in-room guidance. Novel procedures relying mostly on MR data are currently implemented in the clinic. This session will cover topics such as (a) commissioning and quality control of the MR in-room imagers and simulators specific to RT, (b) treatment planning requirements, constraints and challenges when dealing with various MR data, (c) quantification of organ motion with an emphasis on treatment delivery guidance, and (d) MR-driven strategies for adaptive RT workflows. The content of the sessionmore » was chosen to address both educational and practical key aspects of MR guidance. Learning Objectives: Good understanding of MR testing recommended for in-room MR imaging as well as image data validation for RT chain (e.g. image transfer, filtering for consistency, spatial accuracy, manipulation for task specific); Familiarity with MR-based planning procedures: motivation, core workflow requirements, current status, challenges; Overview of the current methods for the quantification of organ motion; Discussion on approaches for adaptive treatment planning and delivery. T. Stanescu - License agreement with Modus Medical Devices to develop a phantom for the quantification of MR image system-related distortions.; T. Stanescu, N/A.« less

Optimal retinal cyst segmentation from OCT images

NASA Astrophysics Data System (ADS)

Oguz, Ipek; Zhang, Li; Abramoff, Michael D.; Sonka, Milan

2016-03-01

Accurate and reproducible segmentation of cysts and fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size and location, as well as the intensity profile similarity between such regions and certain healthy tissue types. While machine learning techniques can be beneficial for this task, they require large training datasets and are often over-fitted to the appearance models of specific scanner vendors. We propose a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. We illustrate the results of this approach on two publicly available datasets with a variety of scanner vendors and retinal disease status. Compared to a previous machine-learning based approach, the volume similarity error was dramatically reduced from 81:3+/-56:4% to 22:2+/-21:3% (paired t-test, p << 0:001).

Parietal and frontal object areas underlie perception of object orientation in depth.

PubMed

Niimi, Ryosuke; Saneyoshi, Ayako; Abe, Reiko; Kaminaga, Tatsuro; Yokosawa, Kazuhiko

2011-05-27

Recent studies have shown that the human parietal and frontal cortices are involved in object image perception. We hypothesized that the parietal/frontal object areas play a role in differentiating the orientations (i.e., views) of an object. By using functional magnetic resonance imaging, we compared brain activations while human observers differentiated between two object images in depth-orientation (orientation task) and activations while they differentiated the images in object identity (identity task). The left intraparietal area, right angular gyrus, and right inferior frontal areas were activated more for the orientation task than for the identity task. The occipitotemporal object areas, however, were activated equally for the two tasks. No region showed greater activation for the identity task. These results suggested that the parietal/frontal object areas encode view-dependent visual features and underlie object orientation perception. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Thread concept for automatic task parallelization in image analysis

NASA Astrophysics Data System (ADS)

Lueckenhaus, Maximilian; Eckstein, Wolfgang

1998-09-01

Parallel processing of image analysis tasks is an essential method to speed up image processing and helps to exploit the full capacity of distributed systems. However, writing parallel code is a difficult and time-consuming process and often leads to an architecture-dependent program that has to be re-implemented when changing the hardware. Therefore it is highly desirable to do the parallelization automatically. For this we have developed a special kind of thread concept for image analysis tasks. Threads derivated from one subtask may share objects and run in the same context but may process different threads of execution and work on different data in parallel. In this paper we describe the basics of our thread concept and show how it can be used as basis of an automatic task parallelization to speed up image processing. We further illustrate the design and implementation of an agent-based system that uses image analysis threads for generating and processing parallel programs by taking into account the available hardware. The tests made with our system prototype show that the thread concept combined with the agent paradigm is suitable to speed up image processing by an automatic parallelization of image analysis tasks.

Architecture of a high-performance surgical guidance system based on C-arm cone-beam CT: software platform for technical integration and clinical translation

NASA Astrophysics Data System (ADS)

Uneri, Ali; Schafer, Sebastian; Mirota, Daniel; Nithiananthan, Sajendra; Otake, Yoshito; Reaungamornrat, Sureerat; Yoo, Jongheun; Stayman, J. Webster; Reh, Douglas; Gallia, Gary L.; Khanna, A. Jay; Hager, Gregory; Taylor, Russell H.; Kleinszig, Gerhard; Siewerdsen, Jeffrey H.

2011-03-01

Intraoperative imaging modalities are becoming more prevalent in recent years, and the need for integration of these modalities with surgical guidance is rising, creating new possibilities as well as challenges. In the context of such emerging technologies and new clinical applications, a software architecture for cone-beam CT (CBCT) guided surgery has been developed with emphasis on binding open-source surgical navigation libraries and integrating intraoperative CBCT with novel, application-specific registration and guidance technologies. The architecture design is focused on accelerating translation of task-specific technical development in a wide range of applications, including orthopaedic, head-and-neck, and thoracic surgeries. The surgical guidance system is interfaced with a prototype mobile C-arm for high-quality CBCT and through a modular software architecture, integration of different tools and devices consistent with surgical workflow in each of these applications is realized. Specific modules are developed according to the surgical task, such as: 3D-3D rigid or deformable registration of preoperative images, surgical planning data, and up-to-date CBCT images; 3D-2D registration of planning and image data in real-time fluoroscopy and/or digitally reconstructed radiographs (DRRs); compatibility with infrared, electromagnetic, and video-based trackers used individually or in hybrid arrangements; augmented overlay of image and planning data in endoscopic or in-room video; real-time "virtual fluoroscopy" computed from GPU-accelerated DRRs; and multi-modality image display. The platform aims to minimize offline data processing by exposing quantitative tools that analyze and communicate factors of geometric precision. The system was translated to preclinical phantom and cadaver studies for assessment of fiducial (FRE) and target registration error (TRE) showing sub-mm accuracy in targeting and video overlay within intraoperative CBCT. The work culminates in the development of a CBCT guidance system (reported here for the first time) that leverages the technical developments in Carm CBCT and associated technologies for realizing a high-performance system for translation to clinical studies.

Speed-Accuracy Tradeoffs in Speech Production

DTIC Science & Technology

2017-06-01

imaging data of speech production. A theoretical framework for considering Fitts’ law in the domain of speech production is elucidated. Methodological ...articulatory kinematics conform to Fitts’ law. A second, associated goal is to address the methodological challenges inherent in performing Fitts-style...analysis on rtMRI data of speech production. Methodological challenges include segmenting continuous speech into specific motor tasks, defining key

Agrammatic Comprehension Caused by a Glioma in the Left Frontal Cortex

ERIC Educational Resources Information Center

Kinno, Ryuta; Muragaki, Yoshihiro; Hori, Tomokatsu; Maruyama, Takashi; Kawamura, Mitsuru; Sakai, Kuniyoshi L.

2009-01-01

It has been known that lesions in the left inferior frontal gyrus (L. IFG) do not always cause Broca's aphasia, casting doubt upon the specificity of this region. We have previously devised a picture-sentence matching task for a functional magnetic resonance imaging (fMRI) study, and observed that both pars triangularis (L. F3t) of L. IFG…

Embodied mental rotation: a special link between egocentric transformation and the bodily self

PubMed Central

Kaltner, Sandra; Riecke, Bernhard E.; Jansen, Petra

2014-01-01

This experiment investigated the influence of motor expertise on object-based versus egocentric transformations in a chronometric mental rotation task using images of either the own or another person’s body as stimulus material. According to the embodied cognition viewpoint, we hypothesized motor-experts to outperform non-motor experts specifically in the egocentric condition because of higher kinesthetic representation and motor simulations compared to object-based transformations. In line with this, we expected that images of the own body are solved faster than another person’s body stimuli. Results showed a benefit of motor expertise and representations of another person’s body, but only for the object-based transformation task. That is, this other-advantage diminishes in egocentric transformations. Since motor experts did not show any specific expertise in rotational movements, we concluded that using human bodies as stimulus material elicits embodied spatial transformations, which facilitates performance exclusively for egocentric transformations. Regarding stimulus material, the other-advantage ascribed to increased self-awareness-consciousness distracting attention-demanding resources, disappeared in the egocentric condition. This result may be due to the stronger link between the bodily self and motor representations compared to that emerging in object-based transformations. PMID:24917832

A reaction time advantage for calculating beliefs over public representations signals domain specificity for 'theory of mind'.

PubMed

Cohen, Adam S; German, Tamsin C

2010-06-01

In a task where participants' overt task was to track the location of an object across a sequence of events, reaction times to unpredictable probes requiring an inference about a social agent's beliefs about the location of that object were obtained. Reaction times to false belief situations were faster than responses about the (false) contents of a map showing the location of the object (Experiment 1) and about the (false) direction of an arrow signaling the location of the object (Experiment 2). These results are consistent with developmental, neuro-imaging and neuropsychological evidence that there exist domain specific mechanisms within human cognition for encoding and reasoning about mental states. Specialization of these mechanisms may arise from either core cognitive architecture or via the accumulation of expertise in the social domain.

Increase in MST activity correlates with visual motion learning: A functional MRI study of perceptual learning.

PubMed

Larcombe, Stephanie J; Kennard, Chris; Bridge, Holly

2018-01-01

Repeated practice of a specific task can improve visual performance, but the neural mechanisms underlying this improvement in performance are not yet well understood. Here we trained healthy participants on a visual motion task daily for 5 days in one visual hemifield. Before and after training, we used functional magnetic resonance imaging (fMRI) to measure the change in neural activity. We also imaged a control group of participants on two occasions who did not receive any task training. While in the MRI scanner, all participants completed the motion task in the trained and untrained visual hemifields separately. Following training, participants improved their ability to discriminate motion direction in the trained hemifield and, to a lesser extent, in the untrained hemifield. The amount of task learning correlated positively with the change in activity in the medial superior temporal (MST) area. MST is the anterior portion of the human motion complex (hMT+). MST changes were localized to the hemisphere contralateral to the region of the visual field, where perceptual training was delivered. Visual areas V2 and V3a showed an increase in activity between the first and second scan in the training group, but this was not correlated with performance. The contralateral anterior hippocampus and bilateral dorsolateral prefrontal cortex (DLPFC) and frontal pole showed changes in neural activity that also correlated with the amount of task learning. These findings emphasize the importance of MST in perceptual learning of a visual motion task. Hum Brain Mapp 39:145-156, 2018. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Evaluation of image quality

NASA Technical Reports Server (NTRS)

Pavel, M.

1993-01-01

This presentation outlines in viewgraph format a general approach to the evaluation of display system quality for aviation applications. This approach is based on the assumption that it is possible to develop a model of the display which captures most of the significant properties of the display. The display characteristics should include spatial and temporal resolution, intensity quantizing effects, spatial sampling, delays, etc. The model must be sufficiently well specified to permit generation of stimuli that simulate the output of the display system. The first step in the evaluation of display quality is an analysis of the tasks to be performed using the display. Thus, for example, if a display is used by a pilot during a final approach, the aesthetic aspects of the display may be less relevant than its dynamic characteristics. The opposite task requirements may apply to imaging systems used for displaying navigation charts. Thus, display quality is defined with regard to one or more tasks. Given a set of relevant tasks, there are many ways to approach display evaluation. The range of evaluation approaches includes visual inspection, rapid evaluation, part-task simulation, and full mission simulation. The work described is focused on two complementary approaches to rapid evaluation. The first approach is based on a model of the human visual system. A model of the human visual system is used to predict the performance of the selected tasks. The model-based evaluation approach permits very rapid and inexpensive evaluation of various design decisions. The second rapid evaluation approach employs specifically designed critical tests that embody many important characteristics of actual tasks. These are used in situations where a validated model is not available. These rapid evaluation tests are being implemented in a workstation environment.

Functional magnetic resonance imaging exploration of combined hand and speech movements in Parkinson's disease.

PubMed

Pinto, Serge; Mancini, Laura; Jahanshahi, Marjan; Thornton, John S; Tripoliti, Elina; Yousry, Tarek A; Limousin, Patricia

2011-10-01

Among the repertoire of motor functions, although hand movement and speech production tasks have been investigated widely by functional neuroimaging, paradigms combining both movements have been studied less so. Such paradigms are of particular interest in Parkinson's disease, in which patients have specific difficulties performing two movements simultaneously. In 9 unmedicated patients with Parkinson's disease and 15 healthy control subjects, externally cued tasks (i.e., hand movement, speech production, and combined hand movement and speech production) were performed twice in a random order and functional magnetic resonance imaging detected cerebral activations, compared to the rest. F-statistics tested within-group (significant activations at P values < 0.05, familywise error corrected), between-group, and between-task comparisons (regional activations significant at P values < 0.001, uncorrected, with cluster size > 10 voxels). For control subjects, the combined task activations comprised the sum of those obtained during hand movement and speech production performed separately, reflecting the neural correlates of performing movements sharing similar programming modalities. In patients with Parkinson's disease, only activations underlying hand movement were observed during the combined task. We interpreted this phenomenon as patients' potential inability to recruit facilitatory activations while performing two movements simultaneously. This lost capacity could be related to a functional prioritization of one movement (i.e., hand movement), in comparison with the other (i.e., speech production). Our observation could also reflect the inability of patients with Parkinson's disease to intrinsically engage the motor coordination necessary to perform a combined task. Copyright © 2011 Movement Disorder Society.

Relation of visual creative imagery manipulation to resting-state brain oscillations.

PubMed

Cai, Yuxuan; Zhang, Delong; Liang, Bishan; Wang, Zengjian; Li, Junchao; Gao, Zhenni; Gao, Mengxia; Chang, Song; Jiao, Bingqing; Huang, Ruiwang; Liu, Ming

2018-02-01

Visual creative imagery (VCI) manipulation is the key component of visual creativity; however, it remains largely unclear how it occurs in the brain. The present study investigated the brain neural response to VCI manipulation and its relation to intrinsic brain activity. We collected functional magnetic resonance imaging (fMRI) datasets related to a VCI task and a control task as well as pre- and post-task resting states in sequential sessions. A general linear model (GLM) was subsequently used to assess the specific activation of the VCI task compared with the control task. The changes in brain oscillation amplitudes across the pre-, on-, and post-task states were measured to investigate the modulation of the VCI task. Furthermore, we applied a Granger causal analysis (GCA) to demonstrate the dynamic neural interactions that underlie the modulation effect. We determined that the VCI task specifically activated the left inferior frontal gyrus pars triangularis (IFGtriang) and the right superior frontal gyrus (SFG), as well as the temporoparietal areas, including the left inferior temporal gyrus, right precuneus, and bilateral superior parietal gyrus. Furthermore, the VCI task modulated the intrinsic brain activity of the right IFGtriang (0.01-0.08 Hz) and the left caudate nucleus (0.2-0.25 Hz). Importantly, an inhibitory effect (negative) may exist from the left SFG to the right IFGtriang in the on-VCI task state, in the frequency of 0.01-0.08 Hz, whereas this effect shifted to an excitatory effect (positive) in the subsequent post-task resting state. Taken together, the present findings provide experimental evidence for the existence of a common mechanism that governs the brain activity of many regions at resting state and whose neural activity may engage during the VCI manipulation task, which may facilitate an understanding of the neural substrate of visual creativity.

Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects

NASA Technical Reports Server (NTRS)

Davis, Larry S.; Dementhon, Daniel; Bestul, Thor; Ziavras, Sotirios; Srinivasan, H. V.; Siddalingaiah, Madhu; Harwood, David

1989-01-01

Interaction with tumbling objects will become more common as human activities in space expand. Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions. A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using dynamic interpolations between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors.

Low Striatal Dopamine D2-type Receptor Availability is Linked to Simulated Drug Choice in Methamphetamine Users.

PubMed

Moeller, Scott J; Okita, Kyoji; Robertson, Chelsea L; Ballard, Michael E; Konova, Anna B; Goldstein, Rita Z; Mandelkern, Mark A; London, Edythe D

2018-03-01

Individuals with drug use disorders seek drugs over other rewarding activities, and exhibit neurochemical deficits related to dopamine, which is involved in value-based learning and decision-making. Thus, a dopaminergic disturbance may underpin drug-biased choice in addiction. Classical drug-choice assessments, which offer drug-consumption opportunities, are inappropriate for addicted individuals seeking treatment or abstaining. Fifteen recently abstinent methamphetamine users and 15 healthy controls completed two laboratory paradigms of 'simulated' drug choice (choice for drug-related vs affectively pleasant, unpleasant, and neutral images), and underwent positron emission tomography measurements of dopamine D2-type receptor availability, indicated by binding potential (BP ND ) for [ 18 F]fallypride. Thirteen of the methamphetamine users and 10 controls also underwent [ 11 C]NNC112 PET scans to measure dopamine D1-type receptor availability. Group analyses showed that, compared with controls, methamphetamine users chose to view more methamphetamine-related images on one task, with a similar trend on the second task. Regression analyses showed that, on both tasks, the more methamphetamine users chose to view methamphetamine images, specifically vs pleasant images (the most frequently chosen images across all participants), the lower was their D2-type BP ND in the lateral orbitofrontal cortex, an important region in value-based choice. No associations were observed with D2-type BP ND in striatal regions, or with D1-type BP ND in any region. These results identify a neurochemical correlate for a laboratory drug-seeking paradigm that can be administered to treatment-seeking and abstaining drug-addicted individuals. More broadly, these results refine the central hypothesis that dopamine-system deficits contribute to drug-biased decision-making in addiction, here showing a role for the orbitofrontal cortex.

Dissecting hemisphere-specific contributions to visual spatial imagery using parametric brain mapping.

PubMed

Bien, Nina; Sack, Alexander T

2014-07-01

In the current study we aimed to empirically test previously proposed accounts of a division of labour between the left and right posterior parietal cortices during visuospatial mental imagery. The representation of mental images in the brain has been a topic of debate for several decades. Although the posterior parietal cortex is involved bilaterally, previous studies have postulated that hemispheric specialisation might result in a division of labour between the left and right parietal cortices. In the current fMRI study, we used an elaborated version of a behaviourally-controlled spatial imagery paradigm, the mental clock task, which involves mental image generation and a subsequent spatial comparison between two angles. By systematically varying the difference between the two angles that are mentally compared, we induced a symbolic distance effect: smaller differences between the two angles result in higher task difficulty. We employed parametrically weighed brain imaging to reveal brain areas showing a graded activation pattern in accordance with the induced distance effect. The parametric difficulty manipulation influenced behavioural data and brain activation patterns in a similar matter. Moreover, since this difficulty manipulation only starts to play a role from the angle comparison phase onwards, it allows for a top-down dissociation between the initial mental image formation, and the subsequent angle comparison phase of the spatial imagery task. Employing parametrically weighed fMRI analysis enabled us to top-down disentangle brain activation related to mental image formation, and activation reflecting spatial angle comparison. The results provide first empirical evidence for the repeatedly proposed division of labour between the left and right posterior parietal cortices during spatial imagery. Copyright © 2014 Elsevier Inc. All rights reserved.

Image Quality Assessment Based on Local Linear Information and Distortion-Specific Compensation.

PubMed

Wang, Hanli; Fu, Jie; Lin, Weisi; Hu, Sudeng; Kuo, C-C Jay; Zuo, Lingxuan

2016-12-14

Image Quality Assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper, we make two main contributions to the IQA field. First, a novel IQA method is developed based on a local linear model that examines the distortion between the reference and the distorted images for better alignment with human visual experience. Second, a distortion-specific compensation strategy is proposed to offset the negative effect on IQA modeling caused by different image distortion types. These score offsets are learned from several known distortion types. Furthermore, for an image with an unknown distortion type, a Convolutional Neural Network (CNN) based method is proposed to compute the score offset automatically. Finally, an integrated IQA metric is proposed by combining the aforementioned two ideas. Extensive experiments are performed to verify the proposed IQA metric, which demonstrate that the local linear model is useful in human perception modeling, especially for individual image distortion, and the overall IQA method outperforms several state-of-the-art IQA approaches.

Gesture-Controlled Interface for Contactless Control of Various Computer Programs with a Hooking-Based Keyboard and Mouse-Mapping Technique in the Operating Room

PubMed Central

Park, Ben Joonyeon; Jang, Taekjin; Choi, Jong Woo; Kim, Namkug

2016-01-01

We developed a contactless interface that exploits hand gestures to effectively control medical images in the operating room. We developed an in-house program called GestureHook that exploits message hooking techniques to convert gestures into specific functions. For quantitative evaluation of this program, we used gestures to control images of a dynamic biliary CT study and compared the results with those of a mouse (8.54 ± 1.77 s to 5.29 ± 1.00 s; p < 0.001) and measured the recognition rates of specific gestures and the success rates of tasks based on clinical scenarios. For clinical applications, this program was set up in the operating room to browse images for plastic surgery. A surgeon browsed images from three different programs: CT images from a PACS program, volume-rendered images from a 3D PACS program, and surgical planning photographs from a basic image viewing program. All programs could be seamlessly controlled by gestures and motions. This approach can control all operating room programs without source code modification and provide surgeons with a new way to safely browse through images and easily switch applications during surgical procedures. PMID:26981146

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

PubMed Central

Vu, Tiep Huu; Mousavi, Hojjat Seyed; Monga, Vishal; Rao, Ganesh; Rao, UK Arvind

2016-01-01

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. PMID:26513781

Gesture-Controlled Interface for Contactless Control of Various Computer Programs with a Hooking-Based Keyboard and Mouse-Mapping Technique in the Operating Room.

PubMed

Park, Ben Joonyeon; Jang, Taekjin; Choi, Jong Woo; Kim, Namkug

2016-01-01

We developed a contactless interface that exploits hand gestures to effectively control medical images in the operating room. We developed an in-house program called GestureHook that exploits message hooking techniques to convert gestures into specific functions. For quantitative evaluation of this program, we used gestures to control images of a dynamic biliary CT study and compared the results with those of a mouse (8.54 ± 1.77 s to 5.29 ± 1.00 s; p < 0.001) and measured the recognition rates of specific gestures and the success rates of tasks based on clinical scenarios. For clinical applications, this program was set up in the operating room to browse images for plastic surgery. A surgeon browsed images from three different programs: CT images from a PACS program, volume-rendered images from a 3D PACS program, and surgical planning photographs from a basic image viewing program. All programs could be seamlessly controlled by gestures and motions. This approach can control all operating room programs without source code modification and provide surgeons with a new way to safely browse through images and easily switch applications during surgical procedures.

Automatic Selection of Multiple Images in the Frontier Field Clusters

NASA Astrophysics Data System (ADS)

Mahler, Guillaume; Richard, Johan; Patricio, Vera; Clément, Benjamin; Lagattuta, David

2015-08-01

Probing the central mass distribution of massive galaxy clusters is an important step towards mapping the overall distribution of their dark matter content. Thanks to gravitational lensing and the appearance of multiple images, we can constrain the inner region of galaxy clusters with a high precision. The Frontier Fields (FF) provide us with the deepest HST data ever in such clusters. Currently, most multiple-image systems are found by eye, yet in the FF, we expect hundreds to exist.Thus, In order to deal with such huge amounts of data, we need to method develop an automated detection method.I present a new tool to perform this task, MISE (Multiple Images SEarcher), a program which identifies multiple images by combining their specific properties. In particular, multiple images must: a) have similar colors, b) have similar surface brightnesses, and c) appear in locations predicted by a specific lensing configuration.I will describe the tuning and performances of MISE on both the FF clusters and the simulated clusters HERA and ARES. MISE allows us to not confirm multiple images identified visually, but also detect new multiple-image candidates in MACS0416 and A2744, giving us additional constraints on the mass distribution in these clusters. A spectroscopic follow-up of these candidates is currently underway with MUSE.

Task-Driven Orbit Design and Implementation on a Robotic C-Arm System for Cone-Beam CT.

PubMed

Ouadah, S; Jacobson, M; Stayman, J W; Ehtiati, T; Weiss, C; Siewerdsen, J H

2017-03-01

This work applies task-driven optimization to the design of non-circular orbits that maximize imaging performance for a particular imaging task. First implementation of task-driven imaging on a clinical robotic C-arm system is demonstrated, and a framework for orbit calculation is described and evaluated. We implemented a task-driven imaging framework to optimize orbit parameters that maximize detectability index d '. This framework utilizes a specified Fourier domain task function and an analytical model for system spatial resolution and noise. Two experiments were conducted to test the framework. First, a simple task was considered consisting of frequencies lying entirely on the f z -axis (e.g., discrimination of structures oriented parallel to the central axial plane), and a "circle + arc" orbit was incorporated into the framework as a means to improve sampling of these frequencies, and thereby increase task-based detectability. The orbit was implemented on a robotic C-arm (Artis Zeego, Siemens Healthcare). A second task considered visualization of a cochlear implant simulated within a head phantom, with spatial frequency response emphasizing high-frequency content in the ( f y , f z ) plane of the cochlea. An optimal orbit was computed using the task-driven framework, and the resulting image was compared to that for a circular orbit. For the f z -axis task, the circle + arc orbit was shown to increase d ' by a factor of 1.20, with an improvement of 0.71 mm in a 3D edge-spread measurement for edges located far from the central plane and a decrease in streak artifacts compared to a circular orbit. For the cochlear implant task, the resulting orbit favored complementary views of high tilt angles in a 360° orbit, and d ' was increased by a factor of 1.83. This work shows that a prospective definition of imaging task can be used to optimize source-detector orbit and improve imaging performance. The method was implemented for execution of non-circular, task-driven orbits on a clinical robotic C-arm system. The framework is sufficiently general to include both acquisition parameters (e.g., orbit, kV, and mA selection) and reconstruction parameters (e.g., a spatially varying regularizer).

Task-driven orbit design and implementation on a robotic C-arm system for cone-beam CT

NASA Astrophysics Data System (ADS)

Ouadah, S.; Jacobson, M.; Stayman, J. W.; Ehtiati, T.; Weiss, C.; Siewerdsen, J. H.

2017-03-01

Purpose: This work applies task-driven optimization to the design of non-circular orbits that maximize imaging performance for a particular imaging task. First implementation of task-driven imaging on a clinical robotic C-arm system is demonstrated, and a framework for orbit calculation is described and evaluated. Methods: We implemented a task-driven imaging framework to optimize orbit parameters that maximize detectability index d'. This framework utilizes a specified Fourier domain task function and an analytical model for system spatial resolution and noise. Two experiments were conducted to test the framework. First, a simple task was considered consisting of frequencies lying entirely on the fz-axis (e.g., discrimination of structures oriented parallel to the central axial plane), and a "circle + arc" orbit was incorporated into the framework as a means to improve sampling of these frequencies, and thereby increase task-based detectability. The orbit was implemented on a robotic C-arm (Artis Zeego, Siemens Healthcare). A second task considered visualization of a cochlear implant simulated within a head phantom, with spatial frequency response emphasizing high-frequency content in the (fy, fz) plane of the cochlea. An optimal orbit was computed using the task-driven framework, and the resulting image was compared to that for a circular orbit. Results: For the fz-axis task, the circle + arc orbit was shown to increase d' by a factor of 1.20, with an improvement of 0.71 mm in a 3D edge-spread measurement for edges located far from the central plane and a decrease in streak artifacts compared to a circular orbit. For the cochlear implant task, the resulting orbit favored complementary views of high tilt angles in a 360° orbit, and d' was increased by a factor of 1.83. Conclusions: This work shows that a prospective definition of imaging task can be used to optimize source-detector orbit and improve imaging performance. The method was implemented for execution of non-circular, task-driven orbits on a clinical robotic C-arm system. The framework is sufficiently general to include both acquisition parameters (e.g., orbit, kV, and mA selection) and reconstruction parameters (e.g., a spatially varying regularizer).

Report of AAPM Task Group 162: Software for planar image quality metrology.

PubMed

Samei, Ehsan; Ikejimba, Lynda C; Harrawood, Brian P; Rong, John; Cunningham, Ian A; Flynn, Michael J

2018-02-01

The AAPM Task Group 162 aimed to provide a standardized approach for the assessment of image quality in planar imaging systems. This report offers a description of the approach as well as the details of the resultant software bundle to measure detective quantum efficiency (DQE) as well as its basis components and derivatives. The methodology and the associated software include the characterization of the noise power spectrum (NPS) from planar images acquired under specific acquisition conditions, modulation transfer function (MTF) using an edge test object, the DQE, and effective DQE (eDQE). First, a methodological framework is provided to highlight the theoretical basis of the work. Then, a step-by-step guide is included to assist in proper execution of each component of the code. Lastly, an evaluation of the method is included to validate its accuracy against model-based and experimental data. The code was built using a Macintosh OSX operating system. The software package contains all the source codes to permit an experienced user to build the suite on a Linux or other *nix type system. The package further includes manuals and sample images and scripts to demonstrate use of the software for new users. The results of the code are in close alignment with theoretical expectations and published results of experimental data. The methodology and the software package offered in AAPM TG162 can be used as baseline for characterization of inherent image quality attributes of planar imaging systems. © 2017 American Association of Physicists in Medicine.

Image patch-based method for automated classification and detection of focal liver lesions on CT

NASA Astrophysics Data System (ADS)

Safdari, Mustafa; Pasari, Raghav; Rubin, Daniel; Greenspan, Hayit

2013-03-01

We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

Altered segregation between task-positive and task-negative regions in mild traumatic brain injury.

PubMed

Sours, Chandler; Kinnison, Joshua; Padmala, Srikanth; Gullapalli, Rao P; Pessoa, Luiz

2018-06-01

Changes in large-scale brain networks that accompany mild traumatic brain injury (mTBI) were investigated using functional magnetic resonance imaging (fMRI) during the N-back working memory task at two cognitive loads (1-back and 2-back). Thirty mTBI patients were examined during the chronic stage of injury and compared to 28 control participants. Demographics and behavioral performance were matched across groups. Due to the diffuse nature of injury, we hypothesized that there would be an imbalance in the communication between task-positive and Default Mode Network (DMN) regions in the context of effortful task execution. Specifically, a graph-theoretic measure of modularity was used to quantify the extent to which groups of brain regions tended to segregate into task-positive and DMN sub-networks. Relative to controls, mTBI patients showed reduced segregation between the DMN and task-positive networks, but increased functional connectivity within the DMN regions during the more cognitively demanding 2-back task. Together, our findings reveal that patients exhibit alterations in the communication between and within neural networks during a cognitively demanding task. These findings reveal altered processes that persist through the chronic stage of injury, highlighting the need for longitudinal research to map the neural recovery of mTBI patients.

Predicting dual-task performance with the Multiple Resources Questionnaire (MRQ).

PubMed

Boles, David B; Bursk, Jonathan H; Phillips, Jeffrey B; Perdelwitz, Jason R

2007-02-01

The objective was to assess the validity of the Multiple Resources Questionnaire (MRQ) in predicting dual-task interference. Subjective workload measures such as the Subjective Workload Assessment Technique (SWAT) and NASA Task Load Index are sensitive to single-task parameters and dual-task loads but have not attempted to measure workload in particular mental processes. An alternative is the MRQ. In Experiment 1, participants completed simple laboratory tasks and the MRQ after each. Interference between tasks was then correlated to three different task similarity metrics: profile similarity, based on r(2) between ratings; overlap similarity, based on summed minima; and overall demand, based on summed ratings. Experiment 2 used similar methods but more complex computer-based games. In Experiment 1 the MRQ moderately predicted interference (r = +.37), with no significant difference between metrics. In Experiment 2 the metric effect was significant, with overlap similarity excelling in predicting interference (r = +.83). Mean ratings showed high diagnosticity in identifying specific mental processing bottlenecks. The MRQ shows considerable promise as a cognitive-process-sensitive workload measure. Potential applications of the MRQ include the identification of dual-processing bottlenecks as well as process overloads in single tasks, preparatory to redesign in areas such as air traffic management, advanced flight displays, and medical imaging.

Orientation Transfer in Vernier and Stereoacuity Training.

PubMed

Snell, Nathaniel; Kattner, Florian; Rokers, Bas; Green, C Shawn

2015-01-01

Human performance on various visual tasks can be improved substantially via training. However, the enhancements are frequently specific to relatively low-level stimulus dimensions. While such specificity has often been thought to be indicative of a low-level neural locus of learning, recent research suggests that these same effects can be accounted for by changes in higher-level areas--in particular in the way higher-level areas read out information from lower-level areas in the service of highly practiced decisions. Here we contrast the degree of orientation transfer seen after training on two different tasks--vernier acuity and stereoacuity. Importantly, while the decision rule that could improve vernier acuity (i.e. a discriminant in the image plane) would not be transferable across orientations, the simplest rule that could be learned to solve the stereoacuity task (i.e. a discriminant in the depth plane) would be insensitive to changes in orientation. Thus, given a read-out hypothesis, more substantial transfer would be expected as a result of stereoacuity than vernier acuity training. To test this prediction, participants were trained (7500 total trials) on either a stereoacuity (N = 9) or vernier acuity (N = 7) task with the stimuli in either a vertical or horizontal configuration (balanced across participants). Following training, transfer to the untrained orientation was assessed. As predicted, evidence for relatively orientation specific learning was observed in vernier trained participants, while no evidence of specificity was observed in stereo trained participants. These results build upon the emerging view that perceptual learning (even very specific learning effects) may reflect changes in inferences made by high-level areas, rather than necessarily fully reflecting changes in the receptive field properties of low-level areas.

Modelling neural correlates of working memory: A coordinate-based meta-analysis

PubMed Central

Rottschy, C.; Langner, R.; Dogan, I.; Reetz, K.; Laird, A.R.; Schulz, J.B.; Fox, P.T.; Eickhoff, S.B.

2011-01-01

Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca’s region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task-set and load effects. Nevertheless, a consistent but more restricted “core” network emerged from conjunctions across analyses of specific task designs and contrasts. This “core” network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focusing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations. PMID:22178808

Object recognition based on Google's reverse image search and image similarity

NASA Astrophysics Data System (ADS)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

An integrated content and metadata based retrieval system for art.

PubMed

Lewis, Paul H; Martinez, Kirk; Abas, Fazly Salleh; Fauzi, Mohammad Faizal Ahmad; Chan, Stephen C Y; Addis, Matthew J; Boniface, Mike J; Grimwood, Paul; Stevenson, Alison; Lahanier, Christian; Stevenson, James

2004-03-01

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.

Stereo Image Ranging For An Autonomous Robot Vision System

NASA Astrophysics Data System (ADS)

Holten, James R.; Rogers, Steven K.; Kabrisky, Matthew; Cross, Steven

1985-12-01

The principles of stereo vision for three-dimensional data acquisition are well-known and can be applied to the problem of an autonomous robot vehicle. Coincidental points in the two images are located and then the location of that point in a three-dimensional space can be calculated using the offset of the points and knowledge of the camera positions and geometry. This research investigates the application of artificial intelligence knowledge representation techniques as a means to apply heuristics to relieve the computational intensity of the low level image processing tasks. Specifically a new technique for image feature extraction is presented. This technique, the Queen Victoria Algorithm, uses formal language productions to process the image and characterize its features. These characterized features are then used for stereo image feature registration to obtain the required ranging information. The results can be used by an autonomous robot vision system for environmental modeling and path finding.

A general system for automatic biomedical image segmentation using intensity neighborhoods.

PubMed

Chen, Cheng; Ozolek, John A; Wang, Wei; Rohde, Gustavo K

2011-01-01

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

Flightspeed Integral Image Analysis Toolkit

NASA Technical Reports Server (NTRS)

Thompson, David R.

2009-01-01

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Task-dependent modulations of prefrontal and hippocampal activity during intrinsic word production.

PubMed

Whitney, Carin; Weis, Susanne; Krings, Timo; Huber, Walter; Grossman, Murray; Kircher, Tilo

2009-04-01

Functional imaging studies of single word production have consistently reported activation of the lateral prefrontal and cingulate cortex. Its contribution has been shown to be sensitive to task demands, which can be manipulated by the degree of response specification. Compared with classical verbal fluency, free word association relies less on response restrictions but to a greater extent on associative binding processes, usually subserved by the hippocampus. To elucidate the relevance of the frontal and medial-temporal areas during verbal retrieval tasks, we applied varying degrees of response specification. During fMRI data acquisition, 18 subjects performed a free verbal association (FVA), a semantic verbal fluency (SVF) task, and a phonological verbal fluency (PVF) task. Externally guided word production served as a baseline condition to control for basic articulatory and reading processes. As expected, increased brain activity was observed in the left lateral and bilateral medial frontal cortices for SVF and PVF. The anterior cingulate gyrus was the only structure common to both fluency tasks in direct comparison to the less restricted FVA task. The hippocampus was engaged during associative and semantic retrieval. Interestingly, hippocampal activity was selectively evident during FVA in direct comparison to SVF when it was controlled for stimulus-response relations. The current data confirm the role of the left prefrontal-cingulate network in constrained word production. Hippocampal activity during spontaneous word production is a novel finding and seems to be dependent on the retrieval process (free vs. constrained) rather than the variety of stimulus-response relationships that is involved.

Pseudoneglect in line bisection judgement is associated with a modulation of right hemispheric spatial attention dominance in right-handers.

PubMed

Zago, Laure; Petit, Laurent; Jobard, Gael; Hay, Julien; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Karnath, Hans-Otto; Mellet, Emmanuel

2017-01-08

The objective of this study was to validate a line bisection judgement (LBJ) task for use in investigating the lateralized cerebral bases of spatial attention in a sample of 51 right-handed healthy participants. Using functional magnetic resonance imaging (fMRI), the participants performed a LBJ task that was compared to a visuomotor control task during which the participants made similar saccadic and motoric responses. Cerebral lateralization was determined using a voxel-based functional asymmetry analysis and a hemispheric functional lateralization index (HFLI) computed from fMRI contrast images. Behavioural attentional deviation biases were assessed during the LBJ task and a "paper and pencil" symbol cancellation task (SCT). Individual visuospatial skills were also evaluated. The results showed that both the LBJ and SCT tasks elicited leftward spatial biases in healthy subjects, although the biases were not correlated, which indicated their independence. Neuroimaging results showed that the LBJ task elicited a right hemispheric lateralization, with rightward asymmetries found in a large posterior occipito-parietal area, the posterior calcarine sulcus (V1p) and the temporo-occipital junction (TOJ) and in the inferior frontal gyrus, the anterior insula and the superior medial frontal gyrus. The comparison of the LBJ asymmetry map to the lesion map of neglect patients who suffer line bisection deviation demonstrated maximum overlap in a network that included the middle occipital gyrus (MOG), the TOJ, the anterior insula and the inferior frontal region, likely subtending spatial LBJ bias. Finally, the LBJ task-related cerebral lateralization was specifically correlated with the LBJ spatial bias but not with the SCT bias or with the visuospatial skills of the participants. Taken together, these results demonstrated that the LBJ task is adequate for investigating spatial lateralization in healthy subjects and is suitable for determining the factors underlying the variability of spatial cerebral lateralization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visual perceptual training reconfigures post-task resting-state functional connectivity with a feature-representation region.

PubMed

Sarabi, Mitra Taghizadeh; Aoki, Ryuta; Tsumura, Kaho; Keerativittayayut, Ruedeerat; Jimura, Koji; Nakahara, Kiyoshi

2018-01-01

The neural mechanisms underlying visual perceptual learning (VPL) have typically been studied by examining changes in task-related brain activation after training. However, the relationship between post-task "offline" processes and VPL remains unclear. The present study examined this question by obtaining resting-state functional magnetic resonance imaging (fMRI) scans of human brains before and after a task-fMRI session involving visual perceptual training. During the task-fMRI session, participants performed a motion coherence discrimination task in which they judged the direction of moving dots with a coherence level that varied between trials (20, 40, and 80%). We found that stimulus-induced activation increased with motion coherence in the middle temporal cortex (MT+), a feature-specific region representing visual motion. On the other hand, stimulus-induced activation decreased with motion coherence in the dorsal anterior cingulate cortex (dACC) and bilateral insula, regions involved in decision making under perceptual ambiguity. Moreover, by comparing pre-task and post-task rest periods, we revealed that resting-state functional connectivity (rs-FC) with the MT+ was significantly increased after training in widespread cortical regions including the bilateral sensorimotor and temporal cortices. In contrast, rs-FC with the MT+ was significantly decreased in subcortical regions including the thalamus and putamen. Importantly, the training-induced change in rs-FC was observed only with the MT+, but not with the dACC or insula. Thus, our findings suggest that perceptual training induces plastic changes in offline functional connectivity specifically in brain regions representing the trained visual feature, emphasising the distinct roles of feature-representation regions and decision-related regions in VPL.

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

PubMed

Brown, Andrew D; Marotta, Thomas R

2017-02-01

Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve. Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling. NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Regionally adaptive histogram equalization of the chest.

PubMed

Sherrier, R H; Johnson, G A

1987-01-01

Advances in the area of digital chest radiography have resulted in the acquisition of high-quality images of the human chest. With these advances, there arises a genuine need for image processing algorithms specific to the chest, in order to fully exploit this digital technology. We have implemented the well-known technique of histogram equalization, noting the problems encountered when it is adapted to chest images. These problems have been successfully solved with our regionally adaptive histogram equalization method. With this technique histograms are calculated locally and then modified according to both the mean pixel value of that region as well as certain characteristics of the cumulative distribution function. This process, which has allowed certain regions of the chest radiograph to be enhanced differentially, may also have broader implications for other image processing tasks.

Voting for image scoring and assessment (VISA)--theory and application of a 2 + 1 reader algorithm to improve accuracy of imaging endpoints in clinical trials.

PubMed

Gottlieb, Klaus; Hussain, Fez

2015-02-19

Independent central reading or off-site reading of imaging endpoints is increasingly used in clinical trials. Clinician-reported outcomes, such as endoscopic disease activity scores, have been shown to be subject to bias and random error. Central reading attempts to limit bias and improve accuracy of the assessment, two factors that are critical to trial success. Whether one central reader is sufficient and how to best integrate the input of more than one central reader into one output measure, is currently not known.In this concept paper we develop the theoretical foundations of a reading algorithm that can achieve both objectives without jeopardizing operational efficiency We examine the role of expert versus competent reader, frame scoring of imaging as a classification task, and propose a voting algorithm (VISA: Voting for Image Scoring and Assessment) as the most appropriate solution which could also be used to operationally define imaging gold standards. We propose two image readers plus an optional third reader in cases of disagreement (2 + 1) for ordinary scoring tasks. We argue that it is critical in trials with endoscopically determined endpoints to include the score determined by the site reader, at least in endoscopy clinical trials. Juries with more than 3 readers could define a reference standard that would allow a transition from measuring reader agreement to measuring reader accuracy. We support VISA by applying concepts from engineering (triple-modular redundancy) and voting theory (Condorcet's jury theorem) and illustrate our points with examples from inflammatory bowel disease trials, specifically, the endoscopy component of the Mayo Clinic Score of ulcerative colitis disease activity. Detailed flow-diagrams (pseudo-code) are provided that can inform program design.The VISA "2 + 1" reading algorithm, based on voting, can translate individual reader scores into a final score in a fashion that is both mathematically sound (by avoiding averaging of ordinal data) and in a manner that is consistent with the scoring task at hand (based on decisions about the presence or absence of features, a subjective classification task). While the VISA 2 + 1 algorithm is currently being used in clinical trials, empirical data of its performance have not yet been reported.

Content specificity of attentional bias to threat in post-traumatic stress disorder.

PubMed

Zinchenko, A; Al-Amin, M M; Alam, M M; Mahmud, W; Kabir, N; Reza, H M; Burne, T H J

2017-08-01

Attentional bias to affective information and reduced cognitive control may maintain the symptoms of post-traumatic stress disorder (PTSD) and impair cognitive functioning. However, the role of content specificity of affective stimuli (e.g., trauma-related, emotional trauma-unrelated) in the observed attentional bias and cognitive control is less clear, as this has not been tested simultaneously before. Therefore, we examined the content specificity of attentional bias to threat in PTSD. PTSD participants (survivors of a multistory factory collapse, n=30) and matched controls (n=30) performed an Eriksen Flanker task. They identified the direction of a centrally presented target arrow, which was flanked by several task-irrelevant distractor arrows pointed to the same (congruent) or opposite direction (incongruent). Additionally, participants were presented with a picture of a face (neutral, emotional) or building (neutral=normal, emotional=collapsed multistory factory) as a task-irrelevant background image. We found that PTSD participants produced overall larger conflict effects and longer reaction times (RT) to emotional than to neutral stimuli relative to their healthy counterparts. Moreover, PTSD, but not healthy participants showed a stimulus specific dissociation in processing emotional stimuli. Emotional faces elicited longer RTs compared to neutral faces, while emotional buildings elicited faster responses, compared to neutral buildings. PTSD patients show a content-sensitive attentional bias to emotional information and impaired cognitive control. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visual scan-path analysis with feature space transient fixation moments

NASA Astrophysics Data System (ADS)

Dempere-Marco, Laura; Hu, Xiao-Peng; Yang, Guang-Zhong

2003-05-01

The study of eye movements provides useful insight into the cognitive processes underlying visual search tasks. The analysis of the dynamics of eye movements has often been approached from a purely spatial perspective. In many cases, however, it may not be possible to define meaningful or consistent dynamics without considering the features underlying the scan paths. In this paper, the definition of the feature space has been attempted through the concept of visual similarity and non-linear low dimensional embedding, which defines a mapping from the image space into a low dimensional feature manifold that preserves the intrinsic similarity of image patterns. This has enabled the definition of perceptually meaningful features without the use of domain specific knowledge. Based on this, this paper introduces a new concept called Feature Space Transient Fixation Moments (TFM). The approach presented tackles the problem of feature space representation of visual search through the use of TFM. We demonstrate the practical values of this concept for characterizing the dynamics of eye movements in goal directed visual search tasks. We also illustrate how this model can be used to elucidate the fundamental steps involved in skilled search tasks through the evolution of transient fixation moments.

Point spread function engineering for iris recognition system design.

PubMed

Ashok, Amit; Neifeld, Mark A

2010-04-01

Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling. A task-specific optimization framework is used to engineer the optical PSF and optimize the postprocessing parameters to minimize the FRR. The optimized Zernike phase enhanced lens (ZPEL) imager design with one frame yields an improvement of nearly 33% relative to a thin observation module by bounded optics (TOMBO) imager with one frame. With four frames the optimized ZPEL imager achieves a FRR equal to that of the conventional imager without undersampling. Further, the ZPEL imager design using 16 frames yields a FRR that is actually 15% lower than that obtained with the conventional imager without undersampling.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

PubMed

Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

2016-05-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

Reliability of functional MR imaging with word-generation tasks for mapping Broca's area.

PubMed

Brannen, J H; Badie, B; Moritz, C H; Quigley, M; Meyerand, M E; Haughton, V M

2001-10-01

Functional MR (fMR) imaging of word generation has been used to map Broca's area in some patients selected for craniotomy. The purpose of this study was to measure the reliability, precision, and accuracy of word-generation tasks to identify Broca's area. The Brodmann areas activated during performance of word-generation tasks were tabulated in 34 consecutive patients referred for fMR imaging mapping of language areas. In patients performing two iterations of the letter word-generation tasks, test-retest reliability was quantified by using the concurrence ratio (CR), or the number of voxels activated by each iteration in proportion to the average number of voxels activated from both iterations of the task. Among patients who also underwent category or antonym word generation or both, the similarity of the activation from each task was assessed with the CR. In patients who underwent electrocortical stimulation (ECS) mapping of speech function during craniotomy while awake, the sites with speech function were compared with the locations of activation found during fMR imaging of word generation. In 31 of 34 patients, activation was identified in the inferior frontal gyri or middle frontal gyri or both in Brodmann areas 9, 44, 45, or 46, unilaterally or bilaterally, with one or more of the tasks. Activation was noted in the same gyri when the patient performed a second iteration of the letter word-generation task or second task. The CR for pixel precision in a single section averaged 49%. In patients who underwent craniotomy while awake, speech areas located with ECS coincided with areas of the brain activated during a word-generation task. fMR imaging with word-generation tasks produces technically satisfactory maps of Broca's area, which localize the area accurately and reliably.

Image Processing and Computer Aided Diagnosis in Computed Tomography of the Breast

DTIC Science & Technology

2007-03-01

TERMS breast imaging, breast CT, scatter compensation, denoising, CAD , Cone-beam CT 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...clinical projection images. The CAD tool based on signal known exactly (SKE) scenario is under development. Task 6: Test and compare the...performances of the CAD developed in Task 5 applied to processed projection data from Task 1 with the CAD performance on the projection data without Bayesian

PACS 2000: quality control using the task allocation chart

NASA Astrophysics Data System (ADS)

Norton, Gary S.; Romlein, John R.; Lyche, David K.; Richardson, Ronald R., Jr.

2000-05-01

Medical imaging's technological evolution in the next century will continue to include Picture Archive and Communication Systems (PACS) and teleradiology. It is difficult to predict radiology's future in the new millennium with both computed radiography and direct digital capture competing as the primary image acquisition methods for routine radiography. Changes in Computed Axial Tomography (CT) and Magnetic Resonance Imaging (MRI) continue to amaze the healthcare community. No matter how the acquisition, display, and archive functions change, Quality Control (QC) of the radiographic imaging chain will remain an important step in the imaging process. The Task Allocation Chart (TAC) is a tool that can be used in a medical facility's QC process to indicate the testing responsibilities of the image stakeholders and the medical informatics department. The TAC shows a grid of equipment to be serviced, tasks to be performed, and the organization assigned to perform each task. Additionally, skills, tasks, time, and references for each task can be provided. QC of the PACS must be stressed as a primary element of a PACS' implementation. The TAC can be used to clarify responsibilities during warranty and paid maintenance periods. Establishing a TAC a part of a PACS implementation has a positive affect on patient care and clinical acceptance.

NDSI products system based on Hadoop platform

NASA Astrophysics Data System (ADS)

Zhou, Yan; Jiang, He; Yang, Xiaoxia; Geng, Erhui

2015-12-01

Snow is solid state of water resources on earth, and plays an important role in human life. Satellite remote sensing is significant in snow extraction with the advantages of cyclical, macro, comprehensiveness, objectivity, timeliness. With the continuous development of remote sensing technology, remote sensing data access to the trend of multiple platforms, multiple sensors and multiple perspectives. At the same time, in view of the remote sensing data of compute-intensive applications demand increase gradually. However, current the producing system of remote sensing products is in a serial mode, and this kind of production system is used for professional remote sensing researchers mostly, and production systems achieving automatic or semi-automatic production are relatively less. Facing massive remote sensing data, the traditional serial mode producing system with its low efficiency has been difficult to meet the requirements of mass data timely and efficient processing. In order to effectively improve the production efficiency of NDSI products, meet the demand of large-scale remote sensing data processed timely and efficiently, this paper build NDSI products production system based on Hadoop platform, and the system mainly includes the remote sensing image management module, NDSI production module, and system service module. Main research contents and results including: (1)The remote sensing image management module: includes image import and image metadata management two parts. Import mass basis IRS images and NDSI product images (the system performing the production task output) into HDFS file system; At the same time, read the corresponding orbit ranks number, maximum/minimum longitude and latitude, product date, HDFS storage path, Hadoop task ID (NDSI products), and other metadata information, and then create thumbnails, and unique ID number for each record distribution, import it into base/product image metadata database. (2)NDSI production module: includes the index calculation, production tasks submission and monitoring two parts. Read HDF images related to production task in the form of a byte stream, and use Beam library to parse image byte stream to the form of Product; Use MapReduce distributed framework to perform production tasks, at the same time monitoring task status; When the production task complete, calls remote sensing image management module to store NDSI products. (3)System service module: includes both image search and DNSI products download. To image metadata attributes described in JSON format, return to the image sequence ID existing in the HDFS file system; For the given MapReduce task ID, package several task output NDSI products into ZIP format file, and return to the download link (4)System evaluation: download massive remote sensing data and use the system to process it to get the NDSI products testing the performance, and the result shows that the system has high extendibility, strong fault tolerance, fast production speed, and the image processing results with high accuracy.

Gamma activity modulated by naming of ambiguous and unambiguous images: intracranial recording

PubMed Central

Cho-Hisamoto, Yoshimi; Kojima, Katsuaki; Brown, Erik C; Matsuzaki, Naoyuki; Asano, Eishi

2014-01-01

OBJECTIVE Humans sometimes need to recognize objects based on vague and ambiguous silhouettes. Recognition of such images may require an intuitive guess. We determined the spatial-temporal characteristics of intracranially-recorded gamma activity (at 50–120 Hz) augmented differentially by naming of ambiguous and unambiguous images. METHODS We studied ten patients who underwent epilepsy surgery. Ambiguous and unambiguous images were presented during extraoperative electrocorticography recording, and patients were instructed to overtly name the object as it is first perceived. RESULTS Both naming tasks were commonly associated with gamma-augmentation sequentially involving the occipital and occipital-temporal regions, bilaterally, within 200 ms after the onset of image presentation. Naming of ambiguous images elicited gamma-augmentation specifically involving portions of the inferior-frontal, orbitofrontal, and inferior-parietal regions at 400 ms and after. Unambiguous images were associated with more intense gamma-augmentation in portions of the occipital and occipital-temporal regions. CONCLUSIONS Frontal-parietal gamma-augmentation specific to ambiguous images may reflect the additional cortical processing involved in exerting intuitive guess. Occipital gamma-augmentation enhanced during naming of unambiguous images can be explained by visual processing of stimuli with richer detail. SIGNIFICANCE Our results support the theoretical model that guessing processes in visual domain occur following the accumulation of sensory evidence resulting from the bottom-up processing in the occipital-temporal visual pathways. PMID:24815577

Imaging deductive reasoning and the new paradigm

PubMed Central

Oaksford, Mike

2015-01-01

There has been a great expansion of research into human reasoning at all of Marr’s explanatory levels. There is a tendency for this work to progress within a level largely ignoring the others which can lead to slippage between levels (Chater et al., 2003). It is argued that recent brain imaging research on deductive reasoning—implementational level—has largely ignored the new paradigm in reasoning—computational level (Over, 2009). Consequently, recent imaging results are reviewed with the focus on how they relate to the new paradigm. The imaging results are drawn primarily from a recent meta-analysis by Prado et al. (2011) but further imaging results are also reviewed where relevant. Three main observations are made. First, the main function of the core brain region identified is most likely elaborative, defeasible reasoning not deductive reasoning. Second, the subtraction methodology and the meta-analytic approach may remove all traces of content specific System 1 processes thought to underpin much human reasoning. Third, interpreting the function of the brain regions activated by a task depends on theories of the function that a task engages. When there are multiple interpretations of that function, interpreting what an active brain region is doing is not clear cut. It is concluded that there is a need to more tightly connect brain activation to function, which could be achieved using formalized computational level models and a parametric variation approach. PMID:25774130

Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning.

PubMed

Jing, Xiao-Yuan; Zhu, Xiaoke; Wu, Fei; Hu, Ruimin; You, Xinge; Wang, Yunhong; Feng, Hui; Yang, Jing-Yu

2017-03-01

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD 2 L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD 2 L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD 2 L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD 2 L (MVSLD 2 L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.

Impairment of working memory maintenance and response in schizophrenia: functional magnetic resonance imaging evidence.

PubMed

Driesen, Naomi R; Leung, Hoi-Chung; Calhoun, Vincent D; Constable, R Todd; Gueorguieva, Ralitza; Hoffman, Ralph; Skudlarski, Pawel; Goldman-Rakic, Patricia S; Krystal, John H

2008-12-15

Comparing prefrontal cortical activity during particular phases of working memory in healthy subjects and individuals diagnosed with schizophrenia might help to define the phase-specific deficits in cortical function that contribute to cognitive impairments associated with schizophrenia. This study featured a spatial working memory task, similar to that used in nonhuman primates, that was designed to facilitate separating brain activation into encoding, maintenance, and response phases. Fourteen patients with schizophrenia (4 medication-free) and 12 healthy comparison participants completed functional magnetic resonance imaging while performing a spatial working memory task with two levels of memory load. Task accuracy was similar in patients and healthy participants. However, patients showed reductions in brain activation during maintenance and response phases but not during the encoding phase. The reduced prefrontal activity during the maintenance phase of working memory was attributed to a greater rate of decay of prefrontal activity over time in patients. Cortical deficits in patients did not appear to be related to antipsychotic treatment. In patients and in healthy subjects, the time-dependent reduction in prefrontal activity during working memory maintenance correlated with poorer performance on the memory task. Overall, these data highlight that basic research insights into the distinct neurobiologies of the maintenance and response phases of working memory are of potential importance for understanding the neurobiology of cognitive impairment in schizophrenia and advancing its treatment.

Increased anterior cingulate cortex response precedes behavioural adaptation in anorexia nervosa

PubMed Central

Geisler, Daniel; Ritschel, Franziska; King, Joseph A.; Bernardoni, Fabio; Seidel, Maria; Boehm, Ilka; Runge, Franziska; Goschke, Thomas; Roessner, Veit; Smolka, Michael N.; Ehrlich, Stefan

2017-01-01

Patients with anorexia nervosa (AN) are characterised by increased self-control, cognitive rigidity and impairments in set-shifting, but the underlying neural mechanisms are poorly understood. Here we used functional magnetic resonance imaging (fMRI) to elucidate the neural correlates of behavioural adaptation to changes in reward contingencies in young acutely ill AN patients. Thirty-six adolescent/young adult, non-chronic female AN patients and 36 age-matched healthy females completed a well-established probabilistic reversal learning task during fMRI. We analysed hemodynamic responses in empirically-defined regions of interest during positive feedback and negative feedback not followed/followed by behavioural adaptation and conducted functional connectivity analyses. Although overall task performance was comparable between groups, AN showed increased shifting after receiving negative feedback (lose-shift behaviour) and altered dorsal anterior cingulate cortex (dACC) responses as a function of feedback. Specifically, patients had increased dACC responses (which correlated with perfectionism) and task-related coupling with amygdala preceding behavioural adaption. Given the generally preserved task performance in young AN, elevated dACC responses specifically during behavioural adaption is suggestive of increased monitoring for the need to adjust performance strategies. Higher dACC-amygdala coupling and increased adaptation after negative feedback underlines this interpretation and could be related to intolerance of uncertainty which has been suggested for AN. PMID:28198813

Eye movements reduce vividness and emotionality of "flashforwards".

PubMed

Engelhard, Iris M; van den Hout, Marcel A; Janssen, Wilco C; van der Beek, Jorinde

2010-05-01

Earlier studies have shown that eye movements during retrieval of disturbing images about past events reduce their vividness and emotionality, which may be due to both tasks competing for working memory resources. This study examined whether eye movements reduce vividness and emotionality of visual distressing images about feared future events: "flashforwards". A non-clinical sample was asked to select two images of feared future events, which were self-rated for vividness and emotionality. These images were retrieved while making eye movements or without a concurrent secondary task, and then vividness and emotionality were rated again. Relative to the no-dual task condition, eye movements while thinking of future-oriented images resulted in decreased ratings of image vividness and emotional intensity. Apparently, eye movements reduce vividness and emotionality of visual images about past and future feared events. This is in line with a working memory account of the beneficial effects of eye movements, which predicts that any task that taxes working memory during retrieval of disturbing mental images will be beneficial. Copyright 2010 Elsevier Ltd. All rights reserved.

Connecting a cognitive architecture to robotic perception

NASA Astrophysics Data System (ADS)

Kurup, Unmesh; Lebiere, Christian; Stentz, Anthony; Hebert, Martial

2012-06-01

We present an integrated architecture in which perception and cognition interact and provide information to each other leading to improved performance in real-world situations. Our system integrates the Felzenswalb et. al. object-detection algorithm with the ACT-R cognitive architecture. The targeted task is to predict and classify pedestrian behavior in a checkpoint scenario, most specifically to discriminate between normal versus checkpoint-avoiding behavior. The Felzenswalb algorithm is a learning-based algorithm for detecting and localizing objects in images. ACT-R is a cognitive architecture that has been successfully used to model human cognition with a high degree of fidelity on tasks ranging from basic decision-making to the control of complex systems such as driving or air traffic control. The Felzenswalb algorithm detects pedestrians in the image and provides ACT-R a set of features based primarily on their locations. ACT-R uses its pattern-matching capabilities, specifically its partial-matching and blending mechanisms, to track objects across multiple images and classify their behavior based on the sequence of observed features. ACT-R also provides feedback to the Felzenswalb algorithm in the form of expected object locations that allow the algorithm to eliminate false-positives and improve its overall performance. This capability is an instance of the benefits pursued in developing a richer interaction between bottom-up perceptual processes and top-down goal-directed cognition. We trained the system on individual behaviors (only one person in the scene) and evaluated its performance across single and multiple behavior sets.

MO-DE-BRA-05: Developing Effective Medical Physics Knowledge Structures: Models and Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sprawls, P

Purpose: Develop a method and supporting online resources to be used by medical physics educators for teaching medical imaging professionals and trainees so they develop highly-effective physics knowledge structures that can contribute to improved diagnostic image quality on a global basis. Methods: The different types of mental knowledge structures were analyzed and modeled with respect to both the learning and teaching process for their development and the functions or tasks that can be performed with the knowledge. While symbolic verbal and mathematical knowledge structures are very important in medical physics for many purposes, the tasks of applying physics in clinicalmore » imaging--especially to optimize image quality and diagnostic accuracy--requires a sensory conceptual knowledge structure, specifically, an interconnected network of visually based concepts. This type of knowledge supports tasks such as analysis, evaluation, problem solving, interacting, and creating solutions. Traditional educational methods including lectures, online modules, and many texts are serial procedures and limited with respect to developing interconnected conceptual networks. A method consisting of the synergistic combination of on-site medical physics teachers and the online resource, CONET (Concept network developer), has been developed and made available for the topic Radiographic Image Quality. This was selected as the inaugural topic, others to follow, because it can be used by medical physicists teaching the large population of medical imaging professionals, such as radiology residents, who can apply the knowledge. Results: Tutorials for medical physics educators on developing effective knowledge structures are being presented and published and CONET is available with open access for all to use. Conclusion: An adjunct to traditional medical physics educational methods with the added focus on sensory concept development provides opportunities for medical physics teachers to share their knowledge and experience at a higher cognitive level and produce medical professionals with the enhanced ability to apply physics to clinical procedures.« less

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

PubMed Central

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-01-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections. PMID:28615794

BreakingNews: Article Annotation by Image and Text Processing.

PubMed

Ramisa, Arnau; Yan, Fei; Moreno-Noguer, Francesc; Mikolajczyk, Krystian

2018-05-01

Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.

Enhanced visualization of inner ear structures

NASA Astrophysics Data System (ADS)

Niemczyk, Kazimierz; Kucharski, Tomasz; Kujawinska, Malgorzata; Bruzgielewicz, Antoni

2004-07-01

Recently surgery requires extensive support from imaging technologies in order to increase effectiveness and safety of operations. One of important tasks is to enhance visualisation of quasi-phase (transparent) 3d structures. Those structures are characterized by very low contrast. It makes differentiation of tissues in field of view very difficult. For that reason the surgeon may be extremly uncertain during operation. This problem is connected with supporting operations of inner ear during which physician has to perform cuts at specific places of quasi-transparent velums. Conventionally during such operations medical doctor views the operating field through stereoscopic microscope. In the paper we propose a 3D visualisation system based on Helmet Mounted Display. Two CCD cameras placed at the output of microscope perform acquisition of stereo pairs of images. The images are processed in real-time with the goal of enhancement of quasi-phased structures. The main task is to create algorithm that is not sensitive to changes in intensity distribution. The disadvantages of existing algorithms is their lack of adaptation to occuring reflexes and shadows in field of view. The processed images from both left and right channels are overlaid on the actual images exported and displayed at LCD's of Helmet Mounted Display. A physician observes by HMD (Helmet Mounted Display) a stereoscopic operating scene with indication of the places of special interest. The authors present the hardware ,procedures applied and initial results of inner ear structure visualisation. Several problems connected with processing of stereo-pair images are discussed.

Why is quality estimation judgment fast? Comparison of gaze control strategies in quality and difference estimation tasks

NASA Astrophysics Data System (ADS)

Radun, Jenni; Leisti, Tuomas; Virtanen, Toni; Nyman, Göte; Häkkinen, Jukka

2014-11-01

To understand the viewing strategies employed in a quality estimation task, we compared two visual tasks-quality estimation and difference estimation. The estimation was done for a pair of natural images having small global changes in quality. Two groups of observers estimated the same set of images, but with different instructions. One group estimated the difference in quality and the other the difference between image pairs. The results demonstrated the use of different visual strategies in the tasks. The quality estimation was found to include more visual planning during the first fixation than the difference estimation, but afterward needed only a few long fixations on the semantically important areas of the image. The difference estimation used many short fixations. Salient image areas were mainly attended to when these areas were also semantically important. The results support the hypothesis that these tasks' general characteristics (evaluation time, number of fixations, area fixated on) show differences in processing, but also suggest that examining only single fixations when comparing tasks is too narrow a view. When planning a subjective experiment, one must remember that a small change in the instructions might lead to a noticeable change in viewing strategy.

The functional neuroanatomy of multitasking: combining dual tasking with a short term memory task.

PubMed

Deprez, Sabine; Vandenbulcke, Mathieu; Peeters, Ron; Emsell, Louise; Amant, Frederic; Sunaert, Stefan

2013-09-01

Insight into the neural architecture of multitasking is crucial when investigating the pathophysiology of multitasking deficits in clinical populations. Presently, little is known about how the brain combines dual-tasking with a concurrent short-term memory task, despite the relevance of this mental operation in daily life and the frequency of complaints related to this process, in disease. In this study we aimed to examine how the brain responds when a memory task is added to dual-tasking. Thirty-three right-handed healthy volunteers (20 females, mean age 39.9 ± 5.8) were examined with functional brain imaging (fMRI). The paradigm consisted of two cross-modal single tasks (a visual and auditory temporal same-different task with short delay), a dual-task combining both single tasks simultaneously and a multi-task condition, combining the dual-task with an additional short-term memory task (temporal same-different visual task with long delay). Dual-tasking compared to both individual visual and auditory single tasks activated a predominantly right-sided fronto-parietal network and the cerebellum. When adding the additional short-term memory task, a larger and more bilateral frontoparietal network was recruited. We found enhanced activity during multitasking in components of the network that were already involved in dual-tasking, suggesting increased working memory demands, as well as recruitment of multitask-specific components including areas that are likely to be involved in online holding of visual stimuli in short-term memory such as occipito-temporal cortex. These results confirm concurrent neural processing of a visual short-term memory task during dual-tasking and provide evidence for an effective fMRI multitasking paradigm. © 2013 Elsevier Ltd. All rights reserved.

Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state.

PubMed

Cao, Hengyi; Plichta, Michael M; Schäfer, Axel; Haddad, Leila; Grimm, Oliver; Schneider, Michael; Esslinger, Christine; Kirsch, Peter; Meyer-Lindenberg, Andreas; Tost, Heike

2014-01-01

The investigation of the brain connectome with functional magnetic resonance imaging (fMRI) and graph theory analyses has recently gained much popularity, but little is known about the robustness of these properties, in particular those derived from active fMRI tasks. Here, we studied the test-retest reliability of brain graphs calculated from 26 healthy participants with three established fMRI experiments (n-back working memory, emotional face-matching, resting state) and two parcellation schemes for node definition (AAL atlas, functional atlas proposed by Power et al.). We compared the intra-class correlation coefficients (ICCs) of five different data processing strategies and demonstrated a superior reliability of task-regression methods with condition-specific regressors. The between-task comparison revealed significantly higher ICCs for resting state relative to the active tasks, and a superiority of the n-back task relative to the face-matching task for global and local network properties. While the mean ICCs were typically lower for the active tasks, overall fair to good reliabilities were detected for global and local connectivity properties, and for the n-back task with both atlases, smallworldness. For all three tasks and atlases, low mean ICCs were seen for the local network properties. However, node-specific good reliabilities were detected for node degree in regions known to be critical for the challenged functions (resting-state: default-mode network nodes, n-back: fronto-parietal nodes, face-matching: limbic nodes). Between-atlas comparison demonstrated significantly higher reliabilities for the functional parcellations for global and local network properties. Our findings can inform the choice of processing strategies, brain atlases and outcome properties for fMRI studies using active tasks, graph theory methods, and within-subject designs, in particular future pharmaco-fMRI studies. © 2013 Elsevier Inc. All rights reserved.

Histology image analysis for carcinoma detection and grading

PubMed Central

He, Lei; Long, L. Rodney; Antani, Sameer; Thoma, George R.

2012-01-01

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems. PMID:22436890

The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

PubMed

Clark, Kenneth; Vendt, Bruce; Smith, Kirk; Freymann, John; Kirby, Justin; Koppel, Paul; Moore, Stephen; Phillips, Stanley; Maffitt, David; Pringle, Michael; Tarbox, Lawrence; Prior, Fred

2013-12-01

The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.

A combined learning algorithm for prostate segmentation on 3D CT images.

PubMed

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M; Fei, Baowei

2017-11-01

Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images. We combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images. The proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation. By combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate. © 2017 American Association of Physicists in Medicine.

Label-aligned Multi-task Feature Learning for Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zu, Chen; Jie, Biao; Liu, Mingxia; Chen, Songcan

2015-01-01

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. PMID:26572145

The role of uncertainty and reward on eye movements in a virtual driving task

PubMed Central

Sullivan, Brian T.; Johnson, Leif; Rothkopf, Constantin A.; Ballard, Dana; Hayhoe, Mary

2012-01-01

Eye movements during natural tasks are well coordinated with ongoing task demands and many variables could influence gaze strategies. Sprague and Ballard (2003) proposed a gaze-scheduling model that uses a utility-weighted uncertainty metric to prioritize fixations on task-relevant objects and predicted that human gaze should be influenced by both reward structure and task-relevant uncertainties. To test this conjecture, we tracked the eye movements of participants in a simulated driving task where uncertainty and implicit reward (via task priority) were varied. Participants were instructed to simultaneously perform a Follow Task where they followed a lead car at a specific distance and a Speed Task where they drove at an exact speed. We varied implicit reward by instructing the participants to emphasize one task over the other and varied uncertainty in the Speed Task with the presence or absence of uniform noise added to the car's velocity. Subjects' gaze data were classified for the image content near fixation and segmented into looks. Gaze measures, including look proportion, duration and interlook interval, showed that drivers more closely monitor the speedometer if it had a high level of uncertainty, but only if it was also associated with high task priority or implicit reward. The interaction observed appears to be an example of a simple mechanism whereby the reduction of visual uncertainty is gated by behavioral relevance. This lends qualitative support for the primary variables controlling gaze allocation proposed in the Sprague and Ballard model. PMID:23262151

Should I Stop or Should I Go? The Role of Associations and Expectancies

PubMed Central

2015-01-01

Following exposure to consistent stimulus–stop mappings, response inhibition can become automatized with practice. What is learned is less clear, even though this has important theoretical and practical implications. A recent analysis indicates that stimuli can become associated with a stop signal or with a stop goal. Furthermore, expectancy may play an important role. Previous studies that have used stop or no-go signals to manipulate stimulus–stop learning cannot distinguish between stimulus-signal and stimulus-goal associations, and expectancy has not been measured properly. In the present study, participants performed a task that combined features of the go/no-go task and the stop-signal task in which the stop-signal rule changed at the beginning of each block. The go and stop signals were superimposed over 40 task-irrelevant images. Our results show that participants can learn direct associations between images and the stop goal without mediation via the stop signal. Exposure to the image-stop associations influenced task performance during training, and expectancies measured following task completion or measured within the task. But, despite this, we found an effect of stimulus–stop learning on test performance only when the task increased the task-relevance of the images. This could indicate that the influence of stimulus–stop learning on go performance is strongly influenced by attention to both task-relevant and task-irrelevant stimulus features. More generally, our findings suggest a strong interplay between automatic and controlled processes. PMID:26322688

Forming impressions of facial attractiveness is mandatory.

PubMed

Ritchie, Kay L; Palermo, Romina; Rhodes, Gillian

2017-03-28

First impressions of social traits, such as attractiveness, from faces are often claimed to be made automatically, given their speed and reliability. However, speed of processing is only one aspect of automaticity. Here we address a further aspect, asking whether impression formation is mandatory. Mandatory formation requires that impressions are formed about social traits even when this is task-irrelevant, and that once formed, these impressions are difficult to inhibit. In two experiments, participants learned what new people looked like for the purpose of future identification, from sets of images high or low in attractiveness. They then rated middle-attractiveness images of each person, for attractiveness. Even though instructed to rate the specific images, not the people, their ratings were biased by the attractiveness of the learned images. A third control experiment, with participants rating names, demonstrated that participants in Experiments 1 and 2 were not simply rating the people, rather than the specific images as instructed. These results show that the formation of attractiveness impressions from faces is mandatory, thus broadening the evidence for automaticity of facial impressions. The mandatory formation of impressions is likely to have an important impact in real-world situations such as online dating sites.

Social-cognitive deficits in normal aging

PubMed Central

Moran, Joseph M.; Jolly, Eshin; Mitchell, Jason P.

2012-01-01

A sizeable number of studies have implicated the default network (e.g., medial prefrontal and parietal cortices) in tasks that require participants to infer the mental states of others—that is, to mentalize. Parallel research has demonstrated that default network function declines over the lifespan, suggesting that older adults may show impairments in social-cognitive tasks that require mentalizing. Older and younger human adults were scanned using functional magnetic resonance imaging (fMRI) while performing three different social-cognitive tasks. Across three mentalizing paradigms, younger and older adults viewed animated shapes in brief social vignettes, stories about a person's moral actions and false belief stories. Consistent with predictions, older adults responded less accurately to stories about others' false beliefs and made less use of actors' intentions to judge the moral permissibility of behavior. These impairments in performance during social-cognitive tasks were accompanied by age-related decreases across all three paradigms in the BOLD response of a single brain region—dorsomedial prefrontal cortex. These findings suggest specific, task-independent age-related deficits in mentalizing that are localizeable to changes in circumscribed subregions of the default network. PMID:22514317

Evaluating visual and auditory contributions to the cognitive restoration effect.

PubMed

Emfield, Adam G; Neider, Mark B

2014-01-01

It has been suggested that certain real-world environments can have a restorative effect on an individual, as expressed in changes in cognitive performance and mood. Much of this research builds on Attention Restoration Theory (ART), which suggests that environments that have certain characteristics induce cognitive restoration via variations in attentional demands. Specifically, natural environments that require little top-down processing have a positive effect on cognitive performance, while city-like environments show no effect. We characterized the cognitive restoration effect further by examining (1) whether natural visual stimuli, such as blue spaces, were more likely to provide a restorative effect over urban visual stimuli, (2) if increasing immersion with environment-related sound produces a similar or superior effect, (3) if this effect extends to other cognitive tasks, such as the functional field of view (FFOV), and (4) if we could better understand this effect by providing controls beyond previous works. We had 202 participants complete a cognitive task battery, consisting of a reverse digit span task, the attention network task, and the FFOV task prior to and immediately after a restoration period. In the restoration period, participants were assigned to one of seven conditions in which they listened to natural or urban sounds, watched images of natural or urban environments, or a combination of both. Additionally, some participants were in a control group with exposure to neither picture nor sound. While we found some indication of practice effects, there were no differential effects of restoration observed in any of our cognitive tasks, regardless of condition. We did, however, find evidence that our nature images and sounds were more relaxing than their urban counterparts. Overall, our findings suggest that acute exposure to relaxing pictorial and auditory stimulus is insufficient to induce improvements in cognitive performance.

Rapid visuomotor processing of phobic images in spider- and snake-fearful participants.

PubMed

Haberkamp, Anke; Schmidt, Filipp; Schmidt, Thomas

2013-10-01

This study investigates enhanced visuomotor processing of phobic compared to fear-relevant and neutral stimuli. We used a response priming design to measure rapid, automatic motor activation by natural images (spiders, snakes, mushrooms, and flowers) in spider-fearful, snake-fearful, and control participants. We found strong priming effects in all tasks and conditions; however, results showed marked differences between groups. Most importantly, in the group of spider-fearful individuals, spider pictures had a strong and specific influence on even the fastest motor responses: Phobic primes entailed the largest priming effects, and phobic targets accelerated responses, both effects indicating speeded response activation by phobic images. In snake-fearful participants, this processing enhancement for phobic material was less pronounced and extended to both snake and spider images. We conclude that spider phobia leads to enhanced processing capacity for phobic images. We argue that this is enabled by long-term perceptual learning processes. © 2013.

Super-resolution Imaging of Chemical Synapses in the Brain

PubMed Central

Dani, Adish; Huang, Bo; Bergan, Joseph; Dulac, Catherine; Zhuang, Xiaowei

2010-01-01

Determination of the molecular architecture of synapses requires nanoscopic image resolution and specific molecular recognition, a task that has so far defied many conventional imaging approaches. Here we present a super-resolution fluorescence imaging method to visualize the molecular architecture of synapses in the brain. Using multicolor, three-dimensional stochastic optical reconstruction microscopy, the distributions of synaptic proteins can be measured with nanometer precision. Furthermore, the wide-field, volumetric imaging method enables high-throughput, quantitative analysis of a large number of synapses from different brain regions. To demonstrate the capabilities of this approach, we have determined the organization of ten protein components of the presynaptic active zone and the postsynaptic density. Variations in synapse morphology, neurotransmitter receptor composition, and receptor distribution were observed both among synapses and across different brain regions. Combination with optogenetics further allowed molecular events associated with synaptic plasticity to be resolved at the single-synapse level. PMID:21144999

Singular spectrum decomposition of Bouligand-Minkowski fractal descriptors: an application to the classification of texture Images

NASA Astrophysics Data System (ADS)

Florindo, João. Batista

2018-04-01

This work proposes the use of Singular Spectrum Analysis (SSA) for the classification of texture images, more specifically, to enhance the performance of the Bouligand-Minkowski fractal descriptors in this task. Fractal descriptors are known to be a powerful approach to model and particularly identify complex patterns in natural images. Nevertheless, the multiscale analysis involved in those descriptors makes them highly correlated. Although other attempts to address this point was proposed in the literature, none of them investigated the relation between the fractal correlation and the well-established analysis employed in time series. And SSA is one of the most powerful techniques for this purpose. The proposed method was employed for the classification of benchmark texture images and the results were compared with other state-of-the-art classifiers, confirming the potential of this analysis in image classification.

The prestimulus default mode network state predicts cognitive task performance levels on a mental rotation task.

PubMed

Kamp, Tabea; Sorger, Bettina; Benjamins, Caroline; Hausfeld, Lars; Goebel, Rainer

2018-06-22

Linking individual task performance to preceding, regional brain activation is an ongoing goal of neuroscientific research. Recently, it could be shown that the activation and connectivity within large-scale brain networks prior to task onset influence performance levels. More specifically, prestimulus default mode network (DMN) effects have been linked to performance levels in sensory near-threshold tasks, as well as cognitive tasks. However, it still remains uncertain how the DMN state preceding cognitive tasks affects performance levels when the period between task trials is long and flexible, allowing participants to engage in different cognitive states. We here investigated whether the prestimulus activation and within-network connectivity of the DMN are predictive of the correctness and speed of task performance levels on a cognitive (match-to-sample) mental rotation task, employing a sparse event-related functional magnetic resonance imaging (fMRI) design. We found that prestimulus activation in the DMN predicted the speed of correct trials, with a higher amplitude preceding correct fast response trials compared to correct slow response trials. Moreover, we found higher connectivity within the DMN before incorrect trials compared to correct trials. These results indicate that pre-existing activation and connectivity states within the DMN influence task performance on cognitive tasks, both effecting the correctness and speed of task execution. The findings support existing theories and empirical work on relating mind-wandering and cognitive task performance to the DMN and expand these by establishing a relationship between the prestimulus DMN state and the speed of cognitive task performance. © 2018 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.

Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports. The prediction model uses a machine learning text classification framework. In this study, we used radiology reports from 18,384 patients with at least one abdomen computed tomography study in their imaging record at Stanford Health Care as the training set. We modeled the radiology reports in a vector space and trained a support vector machine classifier for this prediction task. We evaluated our model on a separate test set of 4791 patients. In addition to high prediction accuracy, in our method, we aimed at achieving high specificity to identify patients at high risk for high imaging utilization. Our results (accuracy: 94.0%, sensitivity: 74.4%, specificity: 97.9%, positive predictive value: 87.3%, negative predictive value: 95.1%) show that a prediction model can enable healthcare providers to identify in advance patients who are likely to be high utilizers of imaging services. Machine learning classifiers developed from narrative radiology reports are feasible methods to predict imaging utilization. Such systems can be used to identify high utilizers, inform future image ordering behavior, and encourage judicious use of imaging. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

User-oriented evaluation of a medical image retrieval system for radiologists.

PubMed

Markonis, Dimitrios; Holzer, Markus; Baroz, Frederic; De Castaneda, Rafael Luis Ruiz; Boyer, Célia; Langs, Georg; Müller, Henning

2015-10-01

This article reports the user-oriented evaluation of a text- and content-based medical image retrieval system. User tests with radiologists using a search system for images in the medical literature are presented. The goal of the tests is to assess the usability of the system, identify system and interface aspects that need improvement and useful additions. Another objective is to investigate the system's added value to radiology information retrieval. The study provides an insight into required specifications and potential shortcomings of medical image retrieval systems through a concrete methodology for conducting user tests. User tests with a working image retrieval system of images from the biomedical literature were performed in an iterative manner, where each iteration had the participants perform radiology information seeking tasks and then refining the system as well as the user study design itself. During these tasks the interaction of the users with the system was monitored, usability aspects were measured, retrieval success rates recorded and feedback was collected through survey forms. In total, 16 radiologists participated in the user tests. The success rates in finding relevant information were on average 87% and 78% for image and case retrieval tasks, respectively. The average time for a successful search was below 3 min in both cases. Users felt quickly comfortable with the novel techniques and tools (after 5 to 15 min), such as content-based image retrieval and relevance feedback. User satisfaction measures show a very positive attitude toward the system's functionalities while the user feedback helped identifying the system's weak points. The participants proposed several potentially useful new functionalities, such as filtering by imaging modality and search for articles using image examples. The iterative character of the evaluation helped to obtain diverse and detailed feedback on all system aspects. Radiologists are quickly familiar with the functionalities but have several comments on desired functionalities. The analysis of the results can potentially assist system refinement for future medical information retrieval systems. Moreover, the methodology presented as well as the discussion on the limitations and challenges of such studies can be useful for user-oriented medical image retrieval evaluation, as user-oriented evaluation of interactive system is still only rarely performed. Such interactive evaluations can be limited in effort if done iteratively and can give many insights for developing better systems. Copyright © 2015. Published by Elsevier Ireland Ltd.

Featural and temporal attention selectively enhance task-appropriate representations in human V1

PubMed Central

Warren, Scott; Yacoub, Essa; Ghose, Geoffrey

2015-01-01

Our perceptions are often shaped by focusing our attention toward specific features or periods of time irrespective of location. We explore the physiological bases of these non-spatial forms of attention by imaging brain activity while subjects perform a challenging change detection task. The task employs a continuously varying visual stimulus that, for any moment in time, selectively activates functionally distinct subpopulations of primary visual cortex (V1) neurons. When subjects are cued to the timing and nature of the change, the mapping of orientation preference across V1 was systematically shifts toward the cued stimulus just prior to its appearance. A simple linear model can explain this shift: attentional changes are selectively targeted toward neural subpopulations representing the attended feature at the times the feature was anticipated. Our results suggest that featural attention is mediated by a linear change in the responses of task-appropriate neurons across cortex during appropriate periods of time. PMID:25501983

Implementing An Image Understanding System Architecture Using Pipe

NASA Astrophysics Data System (ADS)

Luck, Randall L.

1988-03-01

This paper will describe PIPE and how it can be used to implement an image understanding system. Image understanding is the process of developing a description of an image in order to make decisions about its contents. The tasks of image understanding are generally split into low level vision and high level vision. Low level vision is performed by PIPE -a high performance parallel processor with an architecture specifically designed for processing video images at up to 60 fields per second. High level vision is performed by one of several types of serial or parallel computers - depending on the application. An additional processor called ISMAP performs the conversion from iconic image space to symbolic feature space. ISMAP plugs into one of PIPE's slots and is memory mapped into the high level processor. Thus it forms the high speed link between the low and high level vision processors. The mechanisms for bottom-up, data driven processing and top-down, model driven processing are discussed.

fMRI brain response during sentence reading comprehension in children with benign epilepsy with centro-temporal spikes.

PubMed

Malfait, D; Tucholka, A; Mendizabal, S; Tremblay, J; Poulin, C; Oskoui, M; Srour, M; Carmant, L; Major, P; Lippé, S

2015-11-01

Children with benign epilepsy with centro-temporal spikes (BECTS) often have language problems. Abnormal epileptic activity is found in central and temporal brain regions, which are involved in reading and semantic and syntactic comprehension. Using functional magnetic resonance imaging (fMRI), we examined reading networks in BECTS children with a new sentence reading comprehension task involving semantic and syntactic processing. Fifteen children with BECTS (age=11y 1m ± 16 m; 12 boys) and 18 healthy controls (age=11 y 8m ± 20 m; 11 boys) performed an fMRI reading comprehension task in which they read a pair of syntactically complex sentences and decided whether the target sentence (the second sentence in the pair) was true or false with respect to the first sentence. All children also underwent an exhaustive neuropsychological assessment. We demonstrated weaknesses in several cognitive domains in BECTS children. During the sentence reading fMRI task, left inferior frontal regions and bilateral temporal areas were activated in BECTS children and healthy controls. However, additional brain regions such as the left hippocampus and precuneus were activated in BECTS children. Moreover, specific activation was found in the left caudate and putamen in BECTS children but not in healthy controls. Cognitive results and accuracy during the fMRI task were associated with specific brain activation patterns. BECTS children recruited a wider network to perform the fMRI sentence reading comprehension task, with specific activation in the left dorsal striatum. BECTS cognitive performance differently predicted functional activation in frontal and temporal regions compared to controls, suggesting differences in brain network organisation that contribute to reading comprehension. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.

Performance characteristics of a visual-search human-model observer with sparse PET image data

NASA Astrophysics Data System (ADS)

Gifford, Howard C.

2012-02-01

As predictors of human performance in detection-localization tasks, statistical model observers can have problems with tasks that are primarily limited by target contrast or structural noise. Model observers with a visual-search (VS) framework may provide a more reliable alternative. This framework provides for an initial holistic search that identifies suspicious locations for analysis by a statistical observer. A basic VS observer for emission tomography focuses on hot "blobs" in an image and uses a channelized nonprewhitening (CNPW) observer for analysis. In [1], we investigated this model for a contrast-limited task with SPECT images; herein, a statisticalnoise limited task involving PET images is considered. An LROC study used 2D image slices with liver, lung and soft-tissue tumors. Human and model observers read the images in coronal, sagittal and transverse display formats. The study thus measured the detectability of tumors in a given organ as a function of display format. The model observers were applied under several task variants that tested their response to structural noise both at the organ boundaries alone and over the organs as a whole. As measured by correlation with the human data, the VS observer outperformed the CNPW scanning observer.

Comprehensive, powerful, efficient, intuitive: a new software framework for clinical imaging applications

NASA Astrophysics Data System (ADS)

Augustine, Kurt E.; Holmes, David R., III; Hanson, Dennis P.; Robb, Richard A.

2006-03-01

One of the greatest challenges for a software engineer is to create a complex application that is comprehensive enough to be useful to a diverse set of users, yet focused enough for individual tasks to be carried out efficiently with minimal training. This "powerful yet simple" paradox is particularly prevalent in advanced medical imaging applications. Recent research in the Biomedical Imaging Resource (BIR) at Mayo Clinic has been directed toward development of an imaging application framework that provides powerful image visualization/analysis tools in an intuitive, easy-to-use interface. It is based on two concepts very familiar to physicians - Cases and Workflows. Each case is associated with a unique patient and a specific set of routine clinical tasks, or a workflow. Each workflow is comprised of an ordered set of general-purpose modules which can be re-used for each unique workflow. Clinicians help describe and design the workflows, and then are provided with an intuitive interface to both patient data and analysis tools. Since most of the individual steps are common to many different workflows, the use of general-purpose modules reduces development time and results in applications that are consistent, stable, and robust. While the development of individual modules may reflect years of research by imaging scientists, new customized workflows based on the new modules can be developed extremely fast. If a powerful, comprehensive application is difficult to learn and complicated to use, it will be unacceptable to most clinicians. Clinical image analysis tools must be intuitive and effective or they simply will not be used.

Model observer design for multi-signal detection in the presence of anatomical noise

NASA Astrophysics Data System (ADS)

Wen, Gezheng; Markey, Mia K.; Park, Subok

2017-02-01

As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Model observers are typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g. multifocal multicentric (MFMC) breast cancer), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g. digital breast tomosynthesis may be more effective for diagnosis of MFMC than mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model observer to detect multiple signals in an image dataset. A novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. The PLS channels are adaptive to the characteristics of signals and the background, and they capture the interactions among signal locations. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our results show that: (1) the model observer can achieve high performance with a reasonably small number of channels; (2) the model observer with PLS channels outperforms that with benchmark modified Laguerre-Gauss channels, especially when realistic signal shapes and complex background statistics are involved; (3) the tasks of clinical interest, and other constraints such as sample size would alter the optimal design of the model observer.

Sex-Specificity in the Reward Value of Facial Attractiveness.

PubMed

Hahn, Amanda C; Fisher, Claire I; DeBruine, Lisa M; Jones, Benedict C

2016-05-01

Studies of the sex-specificity of sexual arousal in adults (i.e., the tendency to respond more strongly to preferred-sex individuals than non-preferred sex individuals) have suggested that heterosexual men, homosexual men, and homosexual women show stronger sex-specific responses than do heterosexual women. Evidence for a similar pattern of results in studies investigating the reward value of faces is equivocal. Consequently, we investigated the effects of (1) sexual orientation (homosexual vs. heterosexual), (2) sex (male vs. female), (3) image sex (preferred-sex vs. non-preferred-sex), and (4) the physical attractiveness of the individual shown in the image on the reward value of faces. Participants were 130 heterosexual men, 130 homosexual men, 130 heterosexual women, and 130 homosexual women. The reward value of faces was assessed using a standard key-press task. Multilevel modeling of responses indicated that images of preferred-sex individuals were more rewarding than images of non-preferred-sex individuals and that this preferred-sex bias was particularly pronounced when more physically attractive faces were presented. These effects were not qualified by interactions involving either the sexual orientation or the sex of our participants, however, suggesting that the preferred-sex bias in the reward value of faces is similar in heterosexual men, homosexual men, heterosexual women, and homosexual women.

TXM-Wizard: a program for advanced data collection and evaluation in full-field transmission X-ray microscopy

PubMed Central

Liu, Yijin; Meirer, Florian; Williams, Phillip A.; Wang, Junyue; Andrews, Joy C.; Pianetta, Piero

2012-01-01

Transmission X-ray microscopy (TXM) has been well recognized as a powerful tool for non-destructive investigation of the three-dimensional inner structure of a sample with spatial resolution down to a few tens of nanometers, especially when combined with synchrotron radiation sources. Recent developments of this technique have presented a need for new tools for both system control and data analysis. Here a software package developed in MATLAB for script command generation and analysis of TXM data is presented. The first toolkit, the script generator, allows automating complex experimental tasks which involve up to several thousand motor movements. The second package was designed to accomplish computationally intense tasks such as data processing of mosaic and mosaic tomography datasets; dual-energy contrast imaging, where data are recorded above and below a specific X-ray absorption edge; and TXM X-ray absorption near-edge structure imaging datasets. Furthermore, analytical and iterative tomography reconstruction algorithms were implemented. The compiled software package is freely available. PMID:22338691

A Virtual Radial Arm Maze for the Study of Multiple Memory Systems in a Functional Magnetic Resonance Imaging Environment

PubMed Central

Xu, Dongrong; Hao, Xuejun; Wang, Zhishun; Duan, Yunsuo; Liu, Feng; Marsh, Rachel; Yu, Shan; Peterson, Bradley S.

2015-01-01

An increasing number of functional brain imaging studies are employing computer-based virtual reality (VR) to study changes in brain activity during the performance of high-level psychological and cognitive tasks. We report the development of a VR radial arm maze that adapts for human use in a scanning environment with the same general experimental design of behavioral tasks as that has been used with remarkable effectiveness for the study of multiple memory systems in rodents. The software platform is independent of specific computer hardware and operating systems, as we aim to provide shared access to this technology by the research community. We hope that doing so will provide greater standardization of software platform and study paradigm that will reduce variability and improve the comparability of findings across studies. We report the details of the design and implementation of this platform and provide information for downloading of the system for demonstration and research applications. PMID:26366052

Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

PubMed

Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

2013-12-01

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

Processing of Facial Emotion in Bipolar Depression and Euthymia.

PubMed

Robinson, Lucy J; Gray, John M; Burt, Mike; Ferrier, I Nicol; Gallagher, Peter

2015-10-01

Previous studies of facial emotion processing in bipolar disorder (BD) have reported conflicting findings. In independently conducted studies, we investigate facial emotion labeling in euthymic and depressed BD patients using tasks with static and dynamically morphed images of different emotions displayed at different intensities. Study 1 included 38 euthymic BD patients and 28 controls. Participants completed two tasks: labeling of static images of basic facial emotions (anger, disgust, fear, happy, sad) shown at different expression intensities; the Eyes Test (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001), which involves recognition of complex emotions using only the eye region of the face. Study 2 included 53 depressed BD patients and 47 controls. Participants completed two tasks: labeling of "dynamic" facial expressions of the same five basic emotions; the Emotional Hexagon test (Young, Perret, Calder, Sprengelmeyer, & Ekman, 2002). There were no significant group differences on any measures of emotion perception/labeling, compared to controls. A significant group by intensity interaction was observed in both emotion labeling tasks (euthymia and depression), although this effect did not survive the addition of measures of executive function/psychomotor speed as covariates. Only 2.6-15.8% of euthymic patients and 7.8-13.7% of depressed patients scored below the 10th percentile of the controls for total emotion recognition accuracy. There was no evidence of specific deficits in facial emotion labeling in euthymic or depressed BD patients. Methodological variations-including mood state, sample size, and the cognitive demands of the tasks-may contribute significantly to the variability in findings between studies.

Verbal working memory performance correlates with regional white matter structures in the frontoparietal regions.

PubMed

Takeuchi, Hikaru; Taki, Yasuyuki; Sassa, Yuko; Hashizume, Hiroshi; Sekiguchi, Atsushi; Fukushima, Ai; Kawashima, Ryuta

2011-10-01

Working memory is the limited capacity storage system involved in the maintenance and manipulation of information over short periods of time. Previous imaging studies have suggested that the frontoparietal regions are activated during working memory tasks; a putative association between the structure of the frontoparietal regions and working memory performance has been suggested based on the analysis of individuals with varying pathologies. This study aimed to identify correlations between white matter and individual differences in verbal working memory performance in normal young subjects. We performed voxel-based morphometry (VBM) analyses using T1-weighted structural images as well as voxel-based analyses of fractional anisotropy (FA) using diffusion tensor imaging. Using the letter span task, we measured verbal working memory performance in normal young adult men and women (mean age, 21.7 years, SD=1.44; 42 men and 13 women). We observed positive correlations between working memory performance and regional white matter volume (rWMV) in the frontoparietal regions. In addition, FA was found to be positively correlated with verbal working memory performance in a white matter region adjacent to the right precuneus. These regions are consistently recruited by working memory. Our findings suggest that, among normal young subjects, verbal working memory performance is associated with various regions that are recruited during working memory tasks, and this association is not limited to specific parts of the working memory network. Copyright © 2011 Elsevier Ltd. All rights reserved.

The influence of expertise on brain activation of the action observation network during anticipation of tennis and volleyball serves.

PubMed

Balser, Nils; Lorey, Britta; Pilgramm, Sebastian; Naumann, Tim; Kindermann, Stefan; Stark, Rudolf; Zentgraf, Karen; Williams, A Mark; Munzert, Jörn

2014-01-01

In many daily activities, and especially in sport, it is necessary to predict the effects of others' actions in order to initiate appropriate responses. Recently, researchers have suggested that the action-observation network (AON) including the cerebellum plays an essential role during such anticipation, particularly in sport expert performers. In the present study, we examined the influence of task-specific expertise on the AON by investigating differences between two expert groups trained in different sports while anticipating action effects. Altogether, 15 tennis and 16 volleyball experts anticipated the direction of observed tennis and volleyball serves while undergoing functional magnetic resonance imaging (fMRI). The expert group in each sport acted as novice controls in the other sport with which they had only little experience. When contrasting anticipation in both expertise conditions with the corresponding untrained sport, a stronger activation of AON areas (SPL, SMA), and particularly of cerebellar structures, was observed. Furthermore, the neural activation within the cerebellum and the SPL was linearly correlated with participant's anticipation performance, irrespective of the specific expertise. For the SPL, this relationship also holds when an expert performs a domain-specific anticipation task. Notably, the stronger activation of the cerebellum as well as of the SMA and the SPL in the expertise conditions suggests that experts rely on their more fine-tuned perceptual-motor representations that have improved during years of training when anticipating the effects of others' actions in their preferred sport. The association of activation within the SPL and the cerebellum with the task achievement suggests that these areas are the predominant brain sites involved in fast motor predictions. The SPL reflects the processing of domain-specific contextual information and the cerebellum the usage of a predictive internal model to solve the anticipation task.

fMRI identifies chronotype-specific brain activation associated with attention to motion--why we need to know when subjects go to bed.

PubMed

Reske, Martina; Rosenberg, Jessica; Plapp, Sabrina; Kellermann, Thilo; Shah, N Jon

2015-05-01

Human cognition relies on attentional capacities which, among others, are influenced by factors like tiredness or mood. Based on their inherent preferences in sleep and wakefulness, individuals can be classified as specific "chronotypes". The present study investigated how early, intermediate and late chronotypes (EC, IC, LC) differ neurally on an attention-to-motion task. Twelve EC, 18 IC and 17 LC were included into the study. While undergoing functional magnetic resonance imaging (fMRI) scans, subjects looked at vertical bars in an attention-to-motion task. In the STATIONARY condition, subjects focused on a central fixation cross. During Fix-MOVING and Attend-MOVING, bars were moving horizontally. Only during the Attend-MOVING, subjects were required to attend to changes in the velocity of bars and indicate those by button presses. A two-way repeated measures ANOVA probed group by attentional load effects. The dorsolateral prefrontal cortex (DLPFC), insula and anterior cingulate cortex showed group by attention specific activations. Specifically, EC and LC presented attenuated DLPFC activation under high attentional load (Attend-MOVING), while EC showed less anterior insula activation than IC. LC compared to IC exhibited attenuation of superior parietal cortex. Our study reveals that individual sleep preferences are associated with characteristic brain activation in areas crucial for attention and bodily awareness. These results imply that considering sleep preferences in neuroimaging studies is crucial when administering cognitive tasks. Our study also has socio-economic implications. Task performance in non-optimal times of the day (e.g. shift workers), may result in cognitive impairments leading to e.g. increased error rates and slower reaction times. Copyright © 2015 Elsevier Inc. All rights reserved.

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

2017-12-01

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

NASA Astrophysics Data System (ADS)

Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang

2018-03-01

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.

Sentence processing in the cerebral cortex.

PubMed

Sakai, K L; Hashimoto, R; Homae, F

2001-01-01

Human language is a unique faculty of the mind. It has been the ultimate mystery throughout the history of neuroscience. Despite many aphasia and functional imaging studies, the exact correlation between cortical language areas and subcomponents of the linguistic system has not been established. One notable drawback is that most functional imaging studies have tested language tasks at the word level, such as lexical decision and word generation tasks, thereby neglecting the syntactic aspects of the language faculty. As proposed by Chomsky, the critical knowledge of language involves universal grammar (UG), which governs the syntactic structure of sentences. In this article, we will review recent advances made by functional neuroimaging studies of language, focusing especially on sentence processing in the cerebral cortex. We also present the recent results of our functional magnetic resonance imaging (fMRI) study intended to identify cortical areas specifically involved in syntactic processing. A study of sentence processing that employs a newly developed technique, optical topography (OT), is also presented. Based on these findings, we propose a modular specialization of Broca's area, Wernicke's area, and the angular gyrus/supramarginal gyrus. The current direction of research in neuroscience is beginning to establish the existence of distinct modules responsible for our knowledge of language.

A functional magnetic resonance imaging study of working memory abnormalities in schizophrenia.

PubMed

Johnson, Matthew R; Morris, Nicholas A; Astur, Robert S; Calhoun, Vince D; Mathalon, Daniel H; Kiehl, Kent A; Pearlson, Godfrey D

2006-07-01

Previous neuroimaging studies of working memory (WM) in schizophrenia, typically focusing on dorsolateral prefrontal cortex, yield conflicting results, possibly because of varied choice of tasks and analysis techniques. We examined neural function changes at several WM loads to derive a more complete picture of WM dysfunction in schizophrenia. We used a version of the Sternberg Item Recognition Paradigm to test WM function at five distinct loads. Eighteen schizophrenia patients and 18 matched healthy controls were scanned with functional magnetic resonance imaging at 3 Tesla. Patterns of both overactivation and underactivation in patients were observed depending on WM load. Patients' activation was generally less responsive to load changes than control subjects', and different patterns of between-group differences were observed for memory encoding and retrieval. In the specific case of successful retrieval, patients recruited additional neural circuits unused by control subjects. Behavioral effects were generally consistent with these imaging results. Differential findings of overactivation and underactivation may be attributable to patients' decreased ability to focus and allocate neural resources at task-appropriate levels. Additionally, differences between encoding and retrieval suggest that WM dysfunction may be manifested differently during the distinct phases of encoding, maintenance, and retrieval.

Evidential Reasoning in Expert Systems for Image Analysis.

DTIC Science & Technology

1985-02-01

techniques to image analysis (IA). There is growing evidence that these techniques offer significant improvements in image analysis , particularly in the...2) to provide a common framework for analysis, (3) to structure the ER process for major expert-system tasks in image analysis , and (4) to identify...approaches to three important tasks for expert systems in the domain of image analysis . This segment concluded with an assessment of the strengths

Encoding processes during retrieval tasks.

PubMed

Buckner, R L; Wheeler, M E; Sheridan, M A

2001-04-01

Episodic memory encoding is pervasive across many kinds of task and often arises as a secondary processing effect in tasks that do not require intentional memorization. To illustrate the pervasive nature of information processing that leads to episodic encoding, a form of incidental encoding was explored based on the "Testing" phenomenon: The incidental-encoding task was an episodic memory retrieval task. Behavioral data showed that performing a memory retrieval task was as effective as intentional instructions at promoting episodic encoding. During fMRI imaging, subjects viewed old and new words and indicated whether they remembered them. Relevant to encoding, the fate of the new words was examined using a second, surprise test of recognition after the imaging session. fMRI analysis of those new words that were later remembered revealed greater activity in left frontal regions than those that were later forgotten - the same pattern of results as previously observed for traditional incidental and intentional episodic encoding tasks. This finding may offer a partial explanation for why repeated testing improves memory performance. Furthermore, the observation of correlates of episodic memory encoding during retrieval tasks challenges some interpretations that arise from direct comparisons between "encoding tasks" and "retrieval tasks" in imaging data. Encoding processes and their neural correlates may arise in many tasks, even those nominally labeled as retrieval tasks by the experimenter.

Inter-Association Task Force Report on Image.

ERIC Educational Resources Information Center

Special Libraries Association, Washington, DC.

In 1988, the Board of Directors of the Special Libraries Association provided funding to a task force to gather data which would determine how certain segments of society perceive librarians, how librarians view themselves and their colleagues, and to provide recommendations for addressing the issue of image. The task force project consisted of…

Intrusive images and voluntary memory for affective pictures: contextualization and dual-task interference.

PubMed

Krans, Julie; Langner, Oliver; Reinecke, Andrea; Pearson, David G

2013-12-01

The present study addressed the role of context information and dual-task interference during the encoding of negative pictures on intrusion development and voluntary recall. Healthy participants were shown negative pictures with or without context information. Pictures were either viewed alone or concurrently with a visuospatial or verbal task. Participants reported their intrusive images of the pictures in a diary. At follow-up, perceptual and contextual memory was tested. Participants in the context group reported more intrusive images and perceptual voluntary memory than participants in the no context group. No effects of the concurrent tasks were found on intrusive image frequency, but perceptual and contextual memory was affected according to the cognitive load of the task. The analogue method cannot be generalized to real-life trauma and the secondary tasks may differ in cognitive load. The findings challenge a dual memory model of PTSD but support an account in which retrieval strategy, rather than encoding processes, accounts for the experience of involuntary versus voluntary recall. Copyright © 2013 Elsevier Ltd. All rights reserved.

Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

PubMed Central

Jiang, Jiefeng; Egner, Tobias

2014-01-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

Forward Looking Radar Imaging by Truncated Singular Value Decomposition and Its Application for Adverse Weather Aircraft Landing.

PubMed

Huang, Yulin; Zha, Yuebo; Wang, Yue; Yang, Jianyu

2015-06-18

The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing. Then, we convert the forward looking radar imaging task into a corresponding deconvolution problem, which is solved in the framework of algebraic theory using truncated singular decomposition method. The key issue regarding the selecting of the truncated parameter is addressed using generalized cross validation approach. Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

A preference for mathematical processing outweighs the selectivity for Arabic numbers in the inferior temporal gyrus.

PubMed

Grotheer, Mareike; Jeska, Brianna; Grill-Spector, Kalanit

2018-03-28

A region in the posterior inferior temporal gyrus (ITG), referred to as the number form area (NFA, here ITG-numbers) has been implicated in the visual processing of Arabic numbers. However, it is unknown if this region is specifically involved in the visual encoding of Arabic numbers per se or in mathematical processing more broadly. Using functional magnetic resonance imaging (fMRI) during experiments that systematically vary tasks and stimuli, we find that mathematical processing, not preference to Arabic numbers, consistently drives both mean and distributed responses in the posterior ITG. While we replicated findings of higher responses in ITG-numbers to numbers than other visual stimuli during a 1-back task, this preference to numbers was abolished when participants engaged in mathematical processing. In contrast, an ITG region (ITG-math) that showed higher responses during an adding task vs. other tasks maintained this preference for mathematical processing across a wide range of stimuli including numbers, number/letter morphs, hands, and dice. Analysis of distributed responses across an anatomically-defined posterior ITG expanse further revealed that mathematical task but not Arabic number form can be successfully and consistently decoded from these distributed responses. Together, our findings suggest that the function of neuronal regions in the posterior ITG goes beyond the specific visual processing of Arabic numbers. We hypothesize that they ascribe numerical content to the visual input, irrespective of the format of the stimulus. Copyright © 2018 Elsevier Inc. All rights reserved.

Neural correlates of working memory performance in primary insomnia.

PubMed

Drummond, Sean P A; Walker, Matthew; Almklov, Erin; Campos, Manuel; Anderson, Dane E; Straus, Laura D

2013-09-01

To examine neural correlates of working memory performance in patients with primary insomnia (PIs) compared with well-matched good sleepers (GSs). Twenty-five PIs and 25 GSs underwent functional MRI while performing an N-back working memory task. VA hospital sleep laboratory and University-based functional imaging center. 25 PIs, 25 GSs. N/A. Although PIs did not differ from GSs in cognitive performance, PIs showed the expected differences from GSs in both self-reported and objective sleep measures. PIs, relative to GSs, showed reduced activation of task-related working memory regions. This manifested both as an overall reduction in activation of task-related regions and specifically as reduced modulation of right dorsolateral prefrontal cortex with increasing task difficulty. Similarly, PIs showed reduced modulation (i.e., reduced deactivation) of default mode regions with increasing task difficulty, relative to GSs. However, PIs showed intact performance. These data establish a profile of abnormal neural function in primary insomnia, reflected both in reduced engagement of task-appropriate brain regions and an inability to modulate task-irrelevant (i.e., default mode) brain areas during working memory performance. These data have implications for better understanding the neuropathophysiology of the well established, yet little understood, discrepancy between ubiquitous subjective cognitive complaints in primary insomnia and the rarely found objective deficits during testing.

Cascaded systems analysis of noise and detectability in dual-energy cone-beam CT

PubMed Central

Gang, Grace J.; Zbijewski, Wojciech; Webster Stayman, J.; Siewerdsen, Jeffrey H.

2012-01-01

Purpose: Dual-energy computed tomography and dual-energy cone-beam computed tomography (DE-CBCT) are promising modalities for applications ranging from vascular to breast, renal, hepatic, and musculoskeletal imaging. Accordingly, the optimization of imaging techniques for such applications would benefit significantly from a general theoretical description of image quality that properly incorporates factors of acquisition, reconstruction, and tissue decomposition in DE tomography. This work reports a cascaded systems analysis model that includes the Poisson statistics of x rays (quantum noise), detector model (flat-panel detectors), anatomical background, image reconstruction (filtered backprojection), DE decomposition (weighted subtraction), and simple observer models to yield a task-based framework for DE technique optimization. Methods: The theoretical framework extends previous modeling of DE projection radiography and CBCT. Signal and noise transfer characteristics are propagated through physical and mathematical stages of image formation and reconstruction. Dual-energy decomposition was modeled according to weighted subtraction of low- and high-energy images to yield the 3D DE noise-power spectrum (NPS) and noise-equivalent quanta (NEQ), which, in combination with observer models and the imaging task, yields the dual-energy detectability index (d′). Model calculations were validated with NPS and NEQ measurements from an experimental imaging bench simulating the geometry of a dedicated musculoskeletal extremities scanner. Imaging techniques, including kVp pair and dose allocation, were optimized using d′ as an objective function for three example imaging tasks: (1) kidney stone discrimination; (2) iodine vs bone in a uniform, soft-tissue background; and (3) soft tissue tumor detection on power-law anatomical background. Results: Theoretical calculations of DE NPS and NEQ demonstrated good agreement with experimental measurements over a broad range of imaging conditions. Optimization results suggest a lower fraction of total dose imparted by the low-energy acquisition, a finding consistent with previous literature. The selection of optimal kVp pair reveals the combined effect of both quantum noise and contrast in the kidney stone discrimination and soft-tissue tumor detection tasks, whereas the K-edge effect of iodine was the dominant factor in determining kVp pairs in the iodine vs bone task. The soft-tissue tumor task illustrated the benefit of dual-energy imaging in eliminating anatomical background noise and improving detectability beyond that achievable by single-energy scans. Conclusions: This work established a task-based theoretical framework that is predictive of DE image quality. The model can be utilized in optimizing a broad range of parameters in image acquisition, reconstruction, and decomposition, providing a useful tool for maximizing DE-CBCT image quality and reducing dose. PMID:22894440

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Adult brains don't fully overcome biases that lead to incorrect performance during cognitive development: an fMRI study in young adults completing a Piaget-like task.

PubMed

Leroux, Gaëlle; Spiess, Jeanne; Zago, Laure; Rossi, Sandrine; Lubin, Amélie; Turbelin, Marie-Renée; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie; Houdé, Olivier; Joliot, Marc

2009-03-01

A current issue in developmental science is that greater continuity in cognition between children and adults may exist than is usually appreciated in Piaget-like (stages or 'staircase') models. This phenomenon has been demonstrated at the behavioural level, but never at the brain level. Here we show with functional magnetic resonance imaging (fMRI), for the first time, that adult brains do not fully overcome the biases of childhood. More specifically, the aim of this fMRI study was to evaluate whether the perceptual bias that leads to incorrect performance during cognitive development in a Piaget-like task is still a bias in the adult brain and hence requires an executive network to overcome it. Here, we compared two numerical-judgment tasks, one being a Piaget-like task with number-length interference (called 'INT') and the other being a control task with number-length covariation ('COV'). We also used a colour-detection task to control for stimuli numerosity, spatial distribution, and frequency. Our behavioural results confirmed that INT remains a difficult task for young adults. Indeed, response times were significantly higher in INT than in COV. Moreover, we observed that only in INT did response times increase linearly as a function of the number of items. The fMRI results indicate that the brain network common to INT and COV shows a large rightward functional asymmetry, emphasizing the visuospatial nature of these two tasks. When INT was compared with COV, activations were found within a right frontal network, including the pre-supplementary motor area, the anterior cingulate cortex, and the middle frontal gyrus, which probably reflect detection of the number/length conflict and inhibition of the 'length-equals-number' response strategy. Finally, activations related to visuospatial and quantitative processing, enhanced or specifically recruited in the Piaget-like task, were found in bilateral posterior areas.

Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samei, Ehsan, E-mail: samei@duke.edu; Richard, Samuel

2015-01-15

Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD,more » Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance. Conclusions: The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.« less

Image processing and recognition for biological images

PubMed Central

Uchida, Seiichi

2013-01-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. PMID:23560739

Interaction techniques for radiology workstations: impact on users' productivity

NASA Astrophysics Data System (ADS)

Moise, Adrian; Atkins, M. Stella

2004-04-01

As radiologists progress from reading images presented on film to modern computer systems with images presented on high-resolution displays, many new problems arise. Although the digital medium has many advantages, the radiologist"s job becomes cluttered with many new tasks related to image manipulation. This paper presents our solution for supporting radiologists" interpretation of digital images by automating image presentation during sequential interpretation steps. Our method supports scenario based interpretation, which group data temporally, according to the mental paradigm of the physician. We extended current hanging protocols with support for "stages". A stage reflects the presentation of digital information required to complete a single step within a complex task. We demonstrated the benefits of staging in a user study with 20 lay subjects involved in a visual conjunctive search for targets, similar to a radiology task of identifying anatomical abnormalities. We designed a task and a set of stimuli which allowed us to simulate the interpretation workflow from a typical radiology scenario - reading a chest computed radiography exam when a prior study is also available. The simulation was possible by abstracting the radiologist"s task and the basic workstation navigation functionality. We introduced "Stages," an interaction technique attuned to the radiologist"s interpretation task. Compared to the traditional user interface, Stages generated a 14% reduction in the average interpretation.

Nonparametric EROC analysis for observer performance evaluation on joint detection and estimation tasks

NASA Astrophysics Data System (ADS)

Wunderlich, Adam; Goossens, Bart

2014-03-01

The majority of the literature on task-based image quality assessment has focused on lesion detection tasks, using the receiver operating characteristic (ROC) curve, or related variants, to measure performance. However, since many clinical image evaluation tasks involve both detection and estimation (e.g., estimation of kidney stone composition, estimation of tumor size), there is a growing interest in performance evaluation for joint detection and estimation tasks. To evaluate observer performance on such tasks, Clarkson introduced the estimation ROC (EROC) curve, and the area under the EROC curve as a summary figure of merit. In the present work, we propose nonparametric estimators for practical EROC analysis from experimental data, including estimators for the area under the EROC curve and its variance. The estimators are illustrated with a practical example comparing MRI images reconstructed from different k-space sampling trajectories.

Neural Substrates for Processing Task-Irrelevant Sad Images in Adolescents

ERIC Educational Resources Information Center

Wang, Lihong; Huettel, Scott; De Bellis, Michael D.

2008-01-01

Neural systems related to cognitive and emotional processing were examined in adolescents using event-related functional magnetic resonance imaging (fMRI). Ten healthy adolescents performed an emotional oddball task. Subjects detected infrequent circles (targets) within a continual stream of phase-scrambled images (standards). Sad and neutral…

The enhancement of visuospatial processing efficiency through Buddhist Deity meditation.

PubMed

Kozhevnikov, Maria; Louchakova, Olga; Josipovic, Zoran; Motes, Michael A

2009-05-01

This study examined the effects of meditation on mental imagery, evaluating Buddhist monks' reports concerning their extraordinary imagery skills. Practitioners of Buddhist meditation were divided into two groups according to their preferred meditation style: Deity Yoga (focused attention on an internal visual image) or Open Presence (evenly distributed attention, not directed to any particular object). Both groups of meditators completed computerized mental-imagery tasks before and after meditation. Their performance was compared with that of control groups, who either rested or performed other visuospatial tasks between testing sessions. The results indicate that all the groups performed at the same baseline level, but after meditation, Deity Yoga practitioners demonstrated a dramatic increase in performance on imagery tasks compared with the other groups. The results suggest that Deity meditation specifically trains one's capacity to access heightened visuospatial processing resources, rather than generally improving visuospatial imagery abilities.

ASTER and USGS EROS emergency imaging for hurricane disasters: Chapter 4D in Science and the storms-the USGS response to the hurricanes of 2005

USGS Publications Warehouse

Duda, Kenneth A.; Abrams, Michael

2007-01-01

Satellite images have been extremely useful in a variety of emergency response activities, including hurricane disasters. This article discusses the collaborative efforts of the U.S. Geological Survey (USGS), the Joint United States-Japan Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Science Team, and the National Aeronautics and Space Administration (NASA) in responding to crisis situations by tasking the ASTER instrument and rapidly providing information to initial responders. Insight is provided on the characteristics of the ASTER systems, and specific details are presented regarding Hurricane Katrina support.

A generic nuclei detection method for histopathological breast images

NASA Astrophysics Data System (ADS)

Kost, Henning; Homeyer, André; Bult, Peter; Balkenhol, Maschenka C. A.; van der Laak, Jeroen A. W. M.; Hahn, Horst K.

2016-03-01

The detection of cell nuclei plays a key role in various histopathological image analysis problems. Considering the high variability of its applications, we propose a novel generic and trainable detection approach. Adaption to specific nuclei detection tasks is done by providing training samples. A trainable deconvolution and classification algorithm is used to generate a probability map indicating the presence of a nucleus. The map is processed by an extended watershed segmentation step to identify the nuclei positions. We have tested our method on data sets with different stains and target nuclear types. We obtained F1-measures between 0.83 and 0.93.

Amygdala task-evoked activity and task-free connectivity independently contribute to feelings of arousal.

PubMed

Touroutoglou, Alexandra; Bickart, Kevin C; Barrett, Lisa Feldman; Dickerson, Bradford C

2014-10-01

Individual differences in the intensity of feelings of arousal while viewing emotional pictures have been associated with the magnitude of task-evoked blood-oxygen dependent (BOLD) response in the amygdala. Recently, we reported that individual differences in feelings of arousal are associated with task-free (resting state) connectivity within the salience network. There has not yet been an investigation of whether these two types of functional magnetic resonance imaging (MRI) measures are redundant or independent in their relationships to behavior. Here we tested the hypothesis that a combination of task-evoked amygdala activation and task-free amygdala connectivity within the salience network relate to individual differences in feelings of arousal while viewing of negatively potent images. In 25 young adults, results revealed that greater task-evoked amygdala activation and stronger task-free amygdala connectivity within the salience network each contributed independently to feelings of arousal, predicting a total of 45% of its variance. Individuals who had both increased task-evoked amygdala activation and stronger task-free amygdala connectivity within the salience network had the most heightened levels of arousal. Task-evoked amygdala activation and task-free amygdala connectivity within the salience network were not related to each other, suggesting that resting-state and task-evoked dynamic brain imaging measures may provide independent and complementary information about affective experience, and likely other kinds of behaviors as well. Copyright © 2014 Wiley Periodicals, Inc.

Automatic CT simulation optimization for radiation therapy: A general strategy.

PubMed

Li, Hua; Yu, Lifeng; Anastasio, Mark A; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M; Low, Daniel A; Mutic, Sasa

2014-03-01

In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes of 38, 43, 48, 53, and 58 cm were 120, 140, 140, 140, and 140 kVp, respectively, and the corresponding minimum CTDIvol for achieving the optimal image quality index 4.4 were 9.8, 32.2, 100.9, 241.4, and 274.1 mGy, respectively. For patients with lateral sizes of 43-58 cm, 120-kVp scan protocols yielded up to 165% greater radiation dose relative to 140-kVp protocols, and 140-kVp protocols always yielded a greater image quality index compared to the same dose-level 120-kVp protocols. The trace of target and organ dosimetry coverage and the γ passing rates of seven IMRT dose distribution pairs indicated the feasibility of the proposed image quality index for the predication strategy. A general strategy to predict the optimal CT simulation protocols in a flexible and quantitative way was developed that takes into account patient size, treatment planning task, and radiation dose. The experimental study indicated that the optimal CT simulation protocol and the corresponding radiation dose varied significantly for different patient sizes, contouring accuracy, and radiation treatment planning tasks.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

PubMed Central

Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

2016-01-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132.

PubMed

Brock, Kristy K; Mutic, Sasa; McNutt, Todd R; Li, Hua; Kessler, Marc L

2017-07-01

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. © 2017 American Association of Physicists in Medicine.

The impact of verbal framing on brain activity evoked by emotional images.

PubMed

Kisley, Michael A; Campbell, Alana M; Larson, Jenna M; Naftz, Andrea E; Regnier, Jesse T; Davalos, Deana B

2011-12-01

Emotional stimuli generally command more brain processing resources than non-emotional stimuli, but the magnitude of this effect is subject to voluntary control. Cognitive reappraisal represents one type of emotion regulation that can be voluntarily employed to modulate responses to emotional stimuli. Here, the late positive potential (LPP), a specific event-related brain potential (ERP) component, was measured in response to neutral, positive and negative images while participants performed an evaluative categorization task. One experimental group adopted a "negative frame" in which images were categorized as negative or not. The other adopted a "positive frame" in which the exact same images were categorized as positive or not. Behavioral performance confirmed compliance with random group assignment, and peak LPP amplitude to negative images was affected by group membership: brain responses to negative images were significantly reduced in the "positive frame" group. This suggests that adopting a more positive appraisal frame can modulate brain activity elicited by negative stimuli in the environment.

Software phantom with realistic speckle modeling for validation of image analysis methods in echocardiography

NASA Astrophysics Data System (ADS)

Law, Yuen C.; Tenbrinck, Daniel; Jiang, Xiaoyi; Kuhlen, Torsten

2014-03-01

Computer-assisted processing and interpretation of medical ultrasound images is one of the most challenging tasks within image analysis. Physical phenomena in ultrasonographic images, e.g., the characteristic speckle noise and shadowing effects, make the majority of standard methods from image analysis non optimal. Furthermore, validation of adapted computer vision methods proves to be difficult due to missing ground truth information. There is no widely accepted software phantom in the community and existing software phantoms are not exible enough to support the use of specific speckle models for different tissue types, e.g., muscle and fat tissue. In this work we propose an anatomical software phantom with a realistic speckle pattern simulation to _ll this gap and provide a exible tool for validation purposes in medical ultrasound image analysis. We discuss the generation of speckle patterns and perform statistical analysis of the simulated textures to obtain quantitative measures of the realism and accuracy regarding the resulting textures.

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Southeast Asian palm leaf manuscript images: a review of handwritten text line segmentation methods and new challenges

NASA Astrophysics Data System (ADS)

Kesiman, Made Windu Antara; Valy, Dona; Burie, Jean-Christophe; Paulus, Erick; Sunarya, I. Made Gede; Hadi, Setiawan; Sok, Kim Heng; Ogier, Jean-Marc

2017-01-01

Due to their specific characteristics, palm leaf manuscripts provide new challenges for text line segmentation tasks in document analysis. We investigated the performance of six text line segmentation methods by conducting comparative experimental studies for the collection of palm leaf manuscript images. The image corpus used in this study comes from the sample images of palm leaf manuscripts of three different Southeast Asian scripts: Balinese script from Bali and Sundanese script from West Java, both from Indonesia, and Khmer script from Cambodia. For the experiments, four text line segmentation methods that work on binary images are tested: the adaptive partial projection line segmentation approach, the A* path planning approach, the shredding method, and our proposed energy function for shredding method. Two other methods that can be directly applied on grayscale images are also investigated: the adaptive local connectivity map method and the seam carving-based method. The evaluation criteria and tool provided by ICDAR2013 Handwriting Segmentation Contest were used in this experiment.

Network dysfunction predicts speech production after left hemisphere stroke.

PubMed

Geranmayeh, Fatemeh; Leech, Robert; Wise, Richard J S

2016-03-09

To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. © 2016 American Academy of Neurology.

Network dysfunction predicts speech production after left hemisphere stroke

PubMed Central

Leech, Robert; Wise, Richard J.S.

2016-01-01

Objective: To investigate the role of multiple distributed brain networks, including the default mode, fronto-temporo-parietal, and cingulo-opercular networks, which mediate domain-general and task-specific processes during speech production after aphasic stroke. Methods: We conducted an observational functional MRI study to investigate the effects of a previous left hemisphere stroke on functional connectivity within and between distributed networks as patients described pictures. Study design included various baseline tasks, and we compared results to those of age-matched healthy participants performing the same tasks. We used independent component and psychophysiological interaction analyses. Results: Although activity within individual networks was not predictive of speech production, relative activity between networks was a predictor of both within-scanner and out-of-scanner language performance, over and above that predicted from lesion volume, age, sex, and years of education. Specifically, robust functional imaging predictors were the differential activity between the default mode network and both the left and right fronto-temporo-parietal networks, respectively activated and deactivated during speech. We also observed altered between-network functional connectivity of these networks in patients during speech production. Conclusions: Speech production is dependent on complex interactions among widely distributed brain networks, indicating that residual speech production after stroke depends on more than the restoration of local domain-specific functions. Our understanding of the recovery of function following focal lesions is not adequately captured by consideration of ipsilesional or contralesional brain regions taking over lost domain-specific functions, but is perhaps best considered as the interaction between what remains of domain-specific networks and domain-general systems that regulate behavior. PMID:26962070

What makes a face photo a 'good likeness'?

PubMed

Ritchie, Kay L; Kramer, Robin S S; Burton, A Mike

2018-01-01

Photographs of people are commonly said to be 'good likenesses' or 'poor likenesses', and this is a concept that we readily understand. Despite this, there has been no systematic investigation of what makes an image a good likeness, or of which cognitive processes are involved in making such a judgement. In three experiments, we investigate likeness judgements for different types of images: natural images of film stars (Experiment 1), images of film stars from specific films (Experiment 2), and iconic images and face averages (Experiment 3). In all three experiments, participants rated images for likeness and completed speeded name verification tasks. We consistently show that participants are faster to identify images which they have previously rated asa good likeness compared to a poor likeness. We also consistently show that the more familiar we are with someone, the higher likeness rating we give to all images of them. A key finding is that our perception of likeness is idiosyncratic (Experiments 1 and 2), and can be tied to our specific experience of each individual (Experiment 2). We argue that likeness judgements require a comparison between the stimulus and our own representation of the person, and that this representation differs according to our prior experience with that individual. This has theoretical implications for our understanding of how we represent familiar people, and practical implications for how we go about selecting images for identity purposes such as photo-ID. Copyright © 2017 Elsevier B.V. All rights reserved.

Emotion Modulation of Visual Attention: Categorical and Temporal Characteristics

PubMed Central

Ciesielski, Bethany G.; Armstrong, Thomas; Zald, David H.; Olatunji, Bunmi O.

2010-01-01

Background Experimental research has shown that emotional stimuli can either enhance or impair attentional performance. However, the relative effects of specific emotional stimuli and the specific time course of these differential effects are unclear. Methodology/Principal Findings In the present study, participants (n = 50) searched for a single target within a rapid serial visual presentation of images. Irrelevant fear, disgust, erotic or neutral images preceded the target by two, four, six, or eight items. At lag 2, erotic images induced the greatest deficits in subsequent target processing compared to other images, consistent with a large emotional attentional blink. Fear and disgust images also produced a larger attentional blinks at lag 2 than neutral images. Erotic, fear, and disgust images continued to induce greater deficits than neutral images at lag 4 and 6. However, target processing deficits induced by erotic, fear, and disgust images at intermediate lags (lag 4 and 6) did not consistently differ from each other. In contrast to performance at lag 2, 4, and 6, enhancement in target processing for emotional stimuli was observed in comparison to neutral stimuli at lag 8. Conclusions/Significance These findings suggest that task-irrelevant emotion information, particularly erotica, impairs intentional allocation of attention at early temporal stages, but at later temporal stages, emotional stimuli can have an enhancing effect on directed attention. These data suggest that the effects of emotional stimuli on attention can be both positive and negative depending upon temporal factors. PMID:21079773

SU-F-J-178: A Computer Simulation Model Observer for Task-Based Image Quality Assessment in Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolly, S; Mutic, S; Anastasio, M

Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework wasmore » developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation of additional modules to include any aspect of the treatment process, and therefore has great potential for both assessment and optimization within radiation therapy.« less

Accuracy of Presurgical Functional MR Imaging for Language Mapping of Brain Tumors: A Systematic Review and Meta-Analysis.

PubMed

Weng, Hsu-Huei; Noll, Kyle R; Johnson, Jason M; Prabhu, Sujit S; Tsai, Yuan-Hsiung; Chang, Sheng-Wei; Huang, Yen-Chu; Lee, Jiann-Der; Yang, Jen-Tsung; Yang, Cheng-Ta; Tsai, Ying-Huang; Yang, Chun-Yuh; Hazle, John D; Schomer, Donald F; Liu, Ho-Ling

2018-02-01

Purpose To compare functional magnetic resonance (MR) imaging for language mapping (hereafter, language functional MR imaging) with direct cortical stimulation (DCS) in patients with brain tumors and to assess factors associated with its accuracy. Materials and Methods PubMed/MEDLINE and related databases were searched for research articles published between January 2000 and September 2016. Findings were pooled by using bivariate random-effects and hierarchic summary receiver operating characteristic curve models. Meta-regression and subgroup analyses were performed to evaluate whether publication year, functional MR imaging paradigm, magnetic field strength, statistical threshold, and analysis software affected classification accuracy. Results Ten articles with a total of 214 patients were included in the analysis. On a per-patient basis, the pooled sensitivity and specificity of functional MR imaging was 44% (95% confidence interval [CI]: 14%, 78%) and 80% (95% CI: 54%, 93%), respectively. On a per-tag basis (ie, each DCS stimulation site or "tag" was considered a separate data point across all patients), the pooled sensitivity and specificity were 67% (95% CI: 51%, 80%) and 55% (95% CI: 25%, 82%), respectively. The per-tag analysis showed significantly higher sensitivity for studies with shorter functional MR imaging session times (P = .03) and relaxed statistical threshold (P = .05). Significantly higher specificity was found when expressive language task (P = .02), longer functional MR imaging session times (P < .01), visual presentation of stimuli (P = .04), and stringent statistical threshold (P = .01) were used. Conclusion Results of this study showed moderate accuracy of language functional MR imaging when compared with intraoperative DCS, and the included studies displayed significant methodologic heterogeneity. © RSNA, 2017 Online supplemental material is available for this article.

Task-based optimization of image reconstruction in breast CT

NASA Astrophysics Data System (ADS)

Sanchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan

2014-03-01

We demonstrate a task-based assessment of image quality in dedicated breast CT in order to optimize the number of projection views acquired. The methodology we employ is based on the Hotelling Observer (HO) and its associated metrics. We consider two tasks: the Rayleigh task of discerning between two resolvable objects and a single larger object, and the signal detection task of classifying an image as belonging to either a signalpresent or signal-absent hypothesis. HO SNR values are computed for 50, 100, 200, 500, and 1000 projection view images, with the total imaging radiation dose held constant. We use the conventional fan-beam FBP algorithm and investigate the effect of varying the width of a Hanning window used in the reconstruction, since this affects both the noise properties of the image and the under-sampling artifacts which can arise in the case of sparse-view acquisitions. Our results demonstrate that fewer projection views should be used in order to increase HO performance, which in this case constitutes an upper-bound on human observer performance. However, the impact on HO SNR of using fewer projection views, each with a higher dose, is not as significant as the impact of employing regularization in the FBP reconstruction through a Hanning filter.

Pictorial encoding effects and memory confusions in the Deese-Roediger-McDermott paradigm: evidence for the activation of spontaneous imagery.

PubMed

Foley, Mary Ann; Foy, Jeffrey

2008-10-01

The purpose of the experiments reported in this paper was to examine the possible role of spontaneous imagery and list-specific cues on pictorial encoding effects induced by the Deese-Roediger-McDermott (DRM) task. After viewing pictures and words referring to thematically related materials, by way of a picture/word source-judgement task, participants were asked to remember the way in which these materials were presented. Participants reported "seeing" pictures of items that were presented as words, an effect predicted by the imaginal activation hypothesis in its suggestion that incidental images experienced during encoding will later be mistaken as memories for pictures. Whether participants made the same picture misattributions on related lures (or non-presented related items) depended on the way in which the lures' respective thematic lists were experienced during encoding (Experiments 1 and 2), pointing to the effects of list-specific cues in picture/word judgements. These findings have intriguing implications for interpretations of picture-encoding effects induced by the DRM task. The findings also speak to the use of DRM false-memory rates when marshalling evidence against the use of imagery in applied settings.

Neural correlates of the object-recall process in semantic memory.

PubMed

Assaf, Michal; Calhoun, Vince D; Kuzu, Cheedem H; Kraut, Michael A; Rivkin, Paul R; Hart, John; Pearlson, Godfrey D

2006-10-30

The recall of an object from features is a specific operation in semantic memory in which the thalamus and pre-supplementary motor area (pre-SMA) are integrally involved. Other higher-order semantic cortices are also likely to be involved. We used the object-recall-from-features paradigm, with more sensitive scanning techniques and larger sample size, to replicate and extend our previous results. Eighteen right-handed healthy participants performed an object-recall task and an association semantic task, while undergoing functional magnetic resonance imaging. During object-recall, subjects determined whether words pairs describing object features combined to recall an object; during the association task they decided if two words were related. Of brain areas specifically involved in object recall, in addition to the thalamus and pre-SMA, other regions included the left dorsolateral prefrontal cortex, inferior parietal lobule, and middle temporal gyrus, and bilateral rostral anterior cingulate and inferior frontal gyri. These regions are involved in semantic processing, verbal working memory and response-conflict detection and monitoring. The thalamus likely helps to coordinate activity of these different brain areas. Understanding the circuit that normally mediates this process is relevant for schizophrenia, where many regions in this circuit are functionally abnormal and semantic memory is impaired.

Encoding and immediate retrieval tasks in patients with epilepsy: A functional MRI study of verbal and visual memory.

PubMed

Saddiki, Najat; Hennion, Sophie; Viard, Romain; Ramdane, Nassima; Lopes, Renaud; Baroncini, Marc; Szurhaj, William; Reyns, Nicolas; Pruvo, Jean Pierre; Delmaire, Christine

2018-05-01

Medial lobe temporal structures and more specifically the hippocampus play a decisive role in episodic memory. Most of the memory functional magnetic resonance imaging (fMRI) studies evaluate the encoding phase; the retrieval phase being performed outside the MRI. We aimed to determine the ability to reveal greater hippocampal fMRI activations during retrieval phase. Thirty-five epileptic patients underwent a two-step memory fMRI. During encoding phase, subjects were requested to identify the feminine or masculine gender of faces and words presented, in order to encourage stimulus encoding. One hour after, during retrieval phase, subjects had to recognize the word and face. We used an event-related design to identify hippocampal activations. There was no significant difference between patients with left temporal lobe epilepsy, patients with right temporal lobe epilepsy and patients with extratemporal lobe epilepsy on verbal and visual learning task. For words, patients demonstrated significantly more bilateral hippocampal activation for retrieval task than encoding task and when the tasks were associated than during encoding alone. Significant difference was seen between face-encoding alone and face retrieval alone. This study demonstrates the essential contribution of the retrieval task during a fMRI memory task but the number of patients with hippocampal activations was greater when the two tasks were taken into account. Copyright © 2018. Published by Elsevier Masson SAS.

Dissociable Brain States Linked to Common and Creative Object Use

PubMed Central

Chrysikou, Evangelia G.; Thompson-Schill, Sharon L.

2013-01-01

Studies of conceptual processing have revealed that the prefrontal cortex is implicated in close-ended, deliberate memory retrieval, especially the left ventrolateral prefrontal regions. However, much of human thought—particularly that which is characterized as creative—requires more open-ended, spontaneous memory retrieval. To explore the neural systems that support conceptual processing under these two distinct circumstances, we obtained functional magnetic resonance images from 24 participants either while retrieving the common use of an everyday object (e.g., “blowing your nose,” in response to a picture of a tissue) or while generating a creative (i.e., uncommon but plausible) use for it (e.g., “protective padding in a package”). The patterns of activation during open- and closed-ended tasks were reliably different, with regard to the magnitude of anterior versus posterior activation. Specifically, the close-ended task (i.e., Common Use task) reliably activated regions of lateral prefrontal cortex, whereas the open-ended task (i.e., Uncommon Use task) reliably activated regions of occipito-temporal cortex. Furthermore, there was variability across subjects in the types of responses produced on the open-ended task that was associated with the magnitude of activation in the middle occipital gyrus on this task. The present experiment is the first to demonstrate a dynamic tradeoff between anterior frontal and posterior occipitotemporal regions brought about by the close- or open-ended task demands. PMID:20533561

Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

PubMed

Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

2018-06-01

Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

Brain activity during auditory and visual phonological, spatial and simple discrimination tasks.

PubMed

Salo, Emma; Rinne, Teemu; Salonen, Oili; Alho, Kimmo

2013-02-16

We used functional magnetic resonance imaging to measure human brain activity during tasks demanding selective attention to auditory or visual stimuli delivered in concurrent streams. Auditory stimuli were syllables spoken by different voices and occurring in central or peripheral space. Visual stimuli were centrally or more peripherally presented letters in darker or lighter fonts. The participants performed a phonological, spatial or "simple" (speaker-gender or font-shade) discrimination task in either modality. Within each modality, we expected a clear distinction between brain activations related to nonspatial and spatial processing, as reported in previous studies. However, within each modality, different tasks activated largely overlapping areas in modality-specific (auditory and visual) cortices, as well as in the parietal and frontal brain regions. These overlaps may be due to effects of attention common for all three tasks within each modality or interaction of processing task-relevant features and varying task-irrelevant features in the attended-modality stimuli. Nevertheless, brain activations caused by auditory and visual phonological tasks overlapped in the left mid-lateral prefrontal cortex, while those caused by the auditory and visual spatial tasks overlapped in the inferior parietal cortex. These overlapping activations reveal areas of multimodal phonological and spatial processing. There was also some evidence for intermodal attention-related interaction. Most importantly, activity in the superior temporal sulcus elicited by unattended speech sounds was attenuated during the visual phonological task in comparison with the other visual tasks. This effect might be related to suppression of processing irrelevant speech presumably distracting the phonological task involving the letters. Copyright © 2012 Elsevier B.V. All rights reserved.

Ideal-observer detectability in photon-counting differential phase-contrast imaging using a linear-systems approach

PubMed Central

Fredenberg, Erik; Danielsson, Mats; Stayman, J. Webster; Siewerdsen, Jeffrey H.; Åslund, Magnus

2012-01-01

Purpose: To provide a cascaded-systems framework based on the noise-power spectrum (NPS), modulation transfer function (MTF), and noise-equivalent number of quanta (NEQ) for quantitative evaluation of differential phase-contrast imaging (Talbot interferometry) in relation to conventional absorption contrast under equal-dose, equal-geometry, and, to some extent, equal-photon-economy constraints. The focus is a geometry for photon-counting mammography. Methods: Phase-contrast imaging is a promising technology that may emerge as an alternative or adjunct to conventional absorption contrast. In particular, phase contrast may increase the signal-difference-to-noise ratio compared to absorption contrast because the difference in phase shift between soft-tissue structures is often substantially larger than the absorption difference. We have developed a comprehensive cascaded-systems framework to investigate Talbot interferometry, which is a technique for differential phase-contrast imaging. Analytical expressions for the MTF and NPS were derived to calculate the NEQ and a task-specific ideal-observer detectability index under assumptions of linearity and shift invariance. Talbot interferometry was compared to absorption contrast at equal dose, and using either a plane wave or a spherical wave in a conceivable mammography geometry. The impact of source size and spectrum bandwidth was included in the framework, and the trade-off with photon economy was investigated in some detail. Wave-propagation simulations were used to verify the analytical expressions and to generate example images. Results: Talbot interferometry inherently detects the differential of the phase, which led to a maximum in NEQ at high spatial frequencies, whereas the absorption-contrast NEQ decreased monotonically with frequency. Further, phase contrast detects differences in density rather than atomic number, and the optimal imaging energy was found to be a factor of 1.7 higher than for absorption contrast. Talbot interferometry with a plane wave increased detectability for 0.1-mm tumor and glandular structures by a factor of 3–4 at equal dose, whereas absorption contrast was the preferred method for structures larger than ∼0.5 mm. Microcalcifications are small, but differ from soft tissue in atomic number more than density, which is favored by absorption contrast, and Talbot interferometry was barely beneficial at all within the resolution limit of the system. Further, Talbot interferometry favored detection of “sharp” as opposed to “smooth” structures, and discrimination tasks by about 50% compared to detection tasks. The technique was relatively insensitive to spectrum bandwidth, whereas the projected source size was more important. If equal photon economy was added as a restriction, phase-contrast efficiency was reduced so that the benefit for detection tasks almost vanished compared to absorption contrast, but discrimination tasks were still improved close to a factor of 2 at the resolution limit. Conclusions: Cascaded-systems analysis enables comprehensive and intuitive evaluation of phase-contrast efficiency in relation to absorption contrast under requirements of equal dose, equal geometry, and equal photon economy. The benefit of Talbot interferometry was highly dependent on task, in particular detection versus discrimination tasks, and target size, shape, and material. Requiring equal photon economy weakened the benefit of Talbot interferometry in mammography. PMID:22957600

Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning.

PubMed

Hearne, Luke J; Cocchi, Luca; Zalesky, Andrew; Mattingley, Jason B

2017-08-30

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity. SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting-state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand. Copyright © 2017 the authors 0270-6474/17/378399-13$15.00/0.

Simultaneous fNIRS and thermal infrared imaging during cognitive task reveal autonomic correlates of prefrontal cortex activity

NASA Astrophysics Data System (ADS)

Pinti, Paola; Cardone, Daniela; Merla, Arcangelo

2015-12-01

Functional Near Infrared-Spectroscopy (fNIRS) represents a powerful tool to non-invasively study task-evoked brain activity. fNIRS assessment of cortical activity may suffer for contamination by physiological noises of different origin (e.g. heart beat, respiration, blood pressure, skin blood flow), both task-evoked and spontaneous. Spontaneous changes occur at different time scales and, even if they are not directly elicited by tasks, their amplitude may result task-modulated. In this study, concentration changes of hemoglobin were recorded over the prefrontal cortex while simultaneously recording the facial temperature variations of the participants through functional infrared thermal (fIR) imaging. fIR imaging provides touch-less estimation of the thermal expression of peripheral autonomic. Wavelet analysis revealed task-modulation of the very low frequency (VLF) components of both fNIRS and fIR signals and strong coherence between them. Our results indicate that subjective cognitive and autonomic activities are intimately linked and that the VLF component of the fNIRS signal is affected by the autonomic activity elicited by the cognitive task. Moreover, we showed that task-modulated changes in vascular tone occur both at a superficial and at larger depth in the brain. Combined use of fNIRS and fIR imaging can effectively quantify the impact of VLF autonomic activity on the fNIRS signals.

Multi-Image Registration for an Enhanced Vision System

NASA Technical Reports Server (NTRS)

Hines, Glenn; Rahman, Zia-Ur; Jobson, Daniel; Woodell, Glenn

2002-01-01

An Enhanced Vision System (EVS) utilizing multi-sensor image fusion is currently under development at the NASA Langley Research Center. The EVS will provide enhanced images of the flight environment to assist pilots in poor visibility conditions. Multi-spectral images obtained from a short wave infrared (SWIR), a long wave infrared (LWIR), and a color visible band CCD camera, are enhanced and fused using the Retinex algorithm. The images from the different sensors do not have a uniform data structure: the three sensors not only operate at different wavelengths, but they also have different spatial resolutions, optical fields of view (FOV), and bore-sighting inaccuracies. Thus, in order to perform image fusion, the images must first be co-registered. Image registration is the task of aligning images taken at different times, from different sensors, or from different viewpoints, so that all corresponding points in the images match. In this paper, we present two methods for registering multiple multi-spectral images. The first method performs registration using sensor specifications to match the FOVs and resolutions directly through image resampling. In the second method, registration is obtained through geometric correction based on a spatial transformation defined by user selected control points and regression analysis.

High-level context effects on spatial displacement: the effects of body orientation and language on memory

PubMed Central

Vinson, David W.; Abney, Drew H.; Dale, Rick; Matlock, Teenie

2014-01-01

Three decades of research suggests that cognitive simulation of motion is involved in the comprehension of object location, bodily configuration, and linguistic meaning. For example, the remembered location of an object associated with actual or implied motion is typically displaced in the direction of motion. In this paper, two experiments explore context effects in spatial displacement. They provide a novel approach to estimating the remembered location of an implied motion image by employing a cursor-positioning task. Both experiments examine how the remembered spatial location of a person is influenced by subtle differences in implied motion, specifically, by shifting the orientation of the person’s body to face upward or downward, and by pairing the image with motion language that differed on intentionality, fell versus jumped. The results of Experiment 1, a survey-based experiment, suggest that language and body orientation influenced vertical spatial displacement. Results of Experiment 2, a task that used Adobe Flash and Amazon Mechanical Turk, showed consistent effects of body orientation on vertical spatial displacement but no effect of language. Our findings are in line with previous work on spatial displacement that uses a cursor-positioning task with implied motion stimuli. We discuss how different ways of simulating motion can influence spatial memory. PMID:25071628

A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

PubMed

Jin, Hongsheng; Li, Zongyao; Tong, Ruofeng; Lin, Lanfen

2018-05-01

The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction plays a significant role in the detection. In this paper, we focus on the false-positive reduction task and propose an effective method for this task. We construct a deep 3D residual CNN (convolution neural network) to reduce false-positive nodules from candidate nodules. The proposed network is much deeper than the traditional 3D CNNs used in medical image processing. Specifically, in the network, we design a spatial pooling and cropping (SPC) layer to extract multilevel contextual information of CT data. Moreover, we employ an online hard sample selection strategy in the training process to make the network better fit hard samples (e.g., nodules with irregular shapes). Our method is evaluated on 888 CT scans from the dataset of the LUNA16 Challenge. The free-response receiver operating characteristic (FROC) curve shows that the proposed method achieves a high detection performance. Our experiments confirm that our method is robust and that the SPC layer helps increase the prediction accuracy. Additionally, the proposed method can easily be extended to other 3D object detection tasks in medical image processing. © 2018 American Association of Physicists in Medicine.

Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

NASA Astrophysics Data System (ADS)

He, Zhi; Liu, Lin

2016-11-01

Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2 -norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods.

High-level context effects on spatial displacement: the effects of body orientation and language on memory.

PubMed

Vinson, David W; Abney, Drew H; Dale, Rick; Matlock, Teenie

2014-01-01

Three decades of research suggests that cognitive simulation of motion is involved in the comprehension of object location, bodily configuration, and linguistic meaning. For example, the remembered location of an object associated with actual or implied motion is typically displaced in the direction of motion. In this paper, two experiments explore context effects in spatial displacement. They provide a novel approach to estimating the remembered location of an implied motion image by employing a cursor-positioning task. Both experiments examine how the remembered spatial location of a person is influenced by subtle differences in implied motion, specifically, by shifting the orientation of the person's body to face upward or downward, and by pairing the image with motion language that differed on intentionality, fell versus jumped. The results of Experiment 1, a survey-based experiment, suggest that language and body orientation influenced vertical spatial displacement. Results of Experiment 2, a task that used Adobe Flash and Amazon Mechanical Turk, showed consistent effects of body orientation on vertical spatial displacement but no effect of language. Our findings are in line with previous work on spatial displacement that uses a cursor-positioning task with implied motion stimuli. We discuss how different ways of simulating motion can influence spatial memory.

Registration of angiographic image on real-time fluoroscopic image for image-guided percutaneous coronary intervention.

PubMed

Kim, Dongkue; Park, Sangsoo; Jeong, Myung Ho; Ryu, Jeha

2018-02-01

In percutaneous coronary intervention (PCI), cardiologists must study two different X-ray image sources: a fluoroscopic image and an angiogram. Manipulating a guidewire while alternately monitoring the two separate images on separate screens requires a deep understanding of the anatomy of coronary vessels and substantial training. We propose 2D/2D spatiotemporal image registration of the two images in a single image in order to provide cardiologists with enhanced visual guidance in PCI. The proposed 2D/2D spatiotemporal registration method uses a cross-correlation of two ECG series in each image to temporally synchronize two separate images and register an angiographic image onto the fluoroscopic image. A guidewire centerline is then extracted from the fluoroscopic image in real time, and the alignment of the centerline with vessel outlines of the chosen angiographic image is optimized using the iterative closest point algorithm for spatial registration. A proof-of-concept evaluation with a phantom coronary vessel model with engineering students showed an error reduction rate greater than 74% on wrong insertion to nontarget branches compared to the non-registration method and more than 47% reduction in the task completion time in performing guidewire manipulation for very difficult tasks. Evaluation with a small number of experienced doctors shows a potentially significant reduction in both task completion time and error rate for difficult tasks. The total registration time with real procedure X-ray (angiographic and fluoroscopic) images takes [Formula: see text] 60 ms, which is within the fluoroscopic image acquisition rate of 15 Hz. By providing cardiologists with better visual guidance in PCI, the proposed spatiotemporal image registration method is shown to be useful in advancing the guidewire to the coronary vessel branches, especially those difficult to insert into.

Increased Cortical Activity in Binge Drinkers during Working Memory Task: A Preliminary Assessment through a Functional Magnetic Resonance Imaging Study

PubMed Central

Campanella, Salvatore; Peigneux, Philippe; Petit, Géraldine; Lallemand, Frédéric; Saeremans, Mélanie; Noël, Xavier; Metens, Thierry; Nouali, Mustapha; De Tiège, Xavier; De Witte, Philippe; Ward, Roberta; Verbanck, Paul

2013-01-01

Background Cerebral dysfunction is a common feature of both chronic alcohol abusers and binge drinkers. Here, we aimed to study whether, at equated behavioral performance levels, binge drinkers exhibited increased neural activity while performing simple cognitive tasks. Methods Thirty-two participants (16 binge drinkers and 16 matched controls) were scanned using functional magnetic resonance imaging (fMRI) while performing an n-back working memory task. In the control zero-back (N0) condition, subjects were required to press a button with the right hand when the number “2″ was displayed. In the two-back (N2) condition, subjects had to press a button when the displayed number was identical to the number shown two trials before. Results fMRI analyses revealed higher bilateral activity in the pre-supplementary motor area in binge drinkers than matched controls, even though behavioral performances were similar. Moreover, binge drinkers showed specific positive correlations between the number of alcohol doses consumed per occasion and higher activity in the dorsomedial prefrontal cortex, as well as between the number of drinking occasions per week and higher activity in cerebellum, thalamus and insula while performing the N2 memory task. Conclusions Binge alcohol consumption leads to possible compensatory cerebral changes in binge drinkers that facilitate normal behavioral performance. These changes in cerebral responses may be considered as vulnerability factors for developing adult substance use disorders. PMID:23638017

Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks.

PubMed

Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena

2014-01-01

A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic-phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits.

Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks

PubMed Central

Aboitiz, Francisco; Ossandón, Tomás; Zamorano, Francisco; Palma, Bárbara; Carrasco, Ximena

2014-01-01

A cardinal symptom of attention deficit and hyperactivity disorder (ADHD) is a general distractibility where children and adults shift their attentional focus to stimuli that are irrelevant to the ongoing behavior. This has been attributed to a deficit in dopaminergic signaling in cortico-striatal networks that regulate goal-directed behavior. Furthermore, recent imaging evidence points to an impairment of large scale, antagonistic brain networks that normally contribute to attentional engagement and disengagement, such as the task-positive networks and the default mode network (DMN). Related networks are the ventral attentional network (VAN) involved in attentional shifting, and the salience network (SN) related to task expectancy. Here we discuss the tonic–phasic dynamics of catecholaminergic signaling in the brain, and attempt to provide a link between this and the activities of the large-scale cortical networks that regulate behavior. More specifically, we propose that a disbalance of tonic catecholamine levels during task performance produces an emphasis of phasic signaling and increased excitability of the VAN, yielding distractibility symptoms. Likewise, immaturity of the SN may relate to abnormal tonic signaling and an incapacity to build up a proper executive system during task performance. We discuss different lines of evidence including pharmacology, brain imaging and electrophysiology, that are consistent with our proposal. Finally, restoring the pharmacodynamics of catecholaminergic signaling seems crucial to alleviate ADHD symptoms; however, the possibility is open to explore cognitive rehabilitation strategies to top-down modulate network dynamics compensating the pharmacological deficits. PMID:24723897