Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

PubMed

Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

2013-01-01

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

CT of hepatic schistosomiasis mansoni

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

Fataar, S.; Bassiony, H.; Satyanath, S.

1985-07-01

Schistosomal periportal fibrosis produced a typical pattern on computed tomography in five patients. Low-density periportal tissue, present throughout the liver, enhanced strongly after the administration of contrast medium. While rounded in cross section, the thickened periportal tissue produced linear and branching patterns when imaged in longitudinal section. In all cases, the sonographic features were typical of schistosomal periportal fibrosis. A lack of awareness of the distinctive features of periportal fibrosis may result in a mistaken diagnosis of hepatic metastases.

Neuromuscular choristoma: characteristic magnetic resonance imaging findings and association with post-biopsy fibromatosis.

PubMed

Niederhauser, Blake D; Spinner, Robert J; Jentoft, Mark E; Everist, Brian M; Matsumoto, Jane M; Amrami, Kimberly K

2013-04-01

To describe imaging characteristics of neuromuscular choristomas (NMC) and to differentiate them from fibrolipomatous hamartomas (FLH). Clinical and imaging characteristics of six patients with biopsy-proven NMC and six patients with FLH were reviewed by musculoskeletal, a pediatric, and two in-training radiologists with a literature review to define typical magnetic resonance imaging features by consensus. Five radiology trainees blinded to cases and naive to the diagnosis of NMC and a musculoskeletal-trained radiologist rated each lesion as having more than or less than 50% intralesional fat, as well as an overall impression using axial T1 images. Sensitivity, specificity, accuracy, and interobserver agreement kappa were determined. Typical features of NMC include smoothly tapering, fusiform enlargement of the sciatic nerve or brachial plexus elements with T1 and T2 signal characteristics closely following those of muscle. Longitudinal bands of intervening low T1 and T2 signal were often present and likely corresponded to fibrous tissue by pathology. Four of five patients with long-term follow-up (80%) developed aggressive fibromatosis after percutaneous or surgical biopsy. Nerve fascicle thickening often resulted in a "coaxial cable" appearance similar to classic FLH, however, using a cutoff of <50% intralesional fat allowed for differentiation with 100% sensitivity by all reviewers and 100% specificity when all imaging features were utilized for impressions. Agreement was excellent with all differentiating methods (kappa 0.861-1.0). NMC can be confidently differentiated from FLH and malignancies using characteristic imaging and clinical features. When a diagnosis is made, biopsy should be avoided given frequent complication by aggressive fibromatosis.

Visualizing dispersive features in 2D image via minimum gradient method

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

He, Yu; Wang, Yan; Shen, Zhi -Xun

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative applicationmore » to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.« less

Visualizing dispersive features in 2D image via minimum gradient method

DOE PAGES

He, Yu; Wang, Yan; Shen, Zhi -Xun

2017-07-24

Here, we developed a minimum gradient based method to track ridge features in a 2D image plot, which is a typical data representation in many momentum resolved spectroscopy experiments. Through both analytic formulation and numerical simulation, we compare this new method with existing DC (distribution curve) based and higher order derivative based analyses. We find that the new method has good noise resilience and enhanced contrast especially for weak intensity features and meanwhile preserves the quantitative local maxima information from the raw image. An algorithm is proposed to extract 1D ridge dispersion from the 2D image plot, whose quantitative applicationmore » to angle-resolved photoemission spectroscopy measurements on high temperature superconductors is demonstrated.« less

Ultra-Widefield Steering-Based SD-OCT Imaging of the Retinal Periphery

PubMed Central

Choudhry, Netan; Golding, John; Manry, Matthew W.; Rao, Rajesh C.

2016-01-01

Objective To describe the spectral-domain optical coherence tomography (SD-OCT) features of peripheral retinal findings using an ultra-widefield (UWF) steering technique to image the retinal periphery. Design Observational study. Participants 68 patients (68 eyes) with 19 peripheral retinal features. Main Outcome Measures SD-OCT-based structural features. Methods Nineteen peripheral retinal features including: vortex vein, congenital hypertrophy of the retinal pigment epithelium (CHRPE), pars plana, ora serrata pearl, typical cystoid degeneration (TCD), cystic retinal tuft, meridional fold, lattice and cobblestone degeneration, retinal hole, retinal tear, rhegmatogenous retinal detachment (RRD), typical degenerative senile retinoschisis, peripheral laser coagulation scars, ora tooth, cryopexy scars (retinal tear and treated retinoblastoma scar), bone spicules, white without pressure, and peripheral drusen were identified by peripheral clinical examination. Near infrared (NIR) scanning laser ophthalmoscopy (SLO) images and SD-OCT of these entities were registered to UWF color photographs. Results SD-OCT resolved structural features of all peripheral findings. Dilated hyporeflective tubular structures within the choroid were observed in the vortex vein. Loss of retinal lamination, neural retinal attenuation, RPE loss or hypertrophy were seen in several entities including CHRPE, ora serrata pearl, TCD, cystic retinal tuft, meridional fold, lattice and cobblestone degenerations. Hyporeflective intraretinal spaces, indicating cystoid or schitic fluid, were seen in ora serrata pearl, ora tooth, TCD, cystic retinal tuft, meridional fold, retinal hole, and typical degenerative senile retinoschisis. The vitreoretinal interface, which often consisted of lamellae-like structures of the condensed cortical vitreous near or adherent to the neural retina, appeared clearly in most peripheral findings, confirming its association with many low-risk and vision-threatening pathologies such as lattice degeneration, meridional folds, retinal breaks, and RRDs. Conclusions UWF steering technique-based SD-OCT imaging of the retinal periphery is feasible with current commercially available devices, and provides detailed anatomical information of the peripheral retina, including benign and pathological entities, not previously imaged. This imaging technique may deepen our structural understanding of these entities, their potentially associated macular and systemic pathologies, and may influence decision-making in clinical practice, particularly in areas with teleretinal capabilities but poor access to retinal specialists. PMID:26992837

Sensor-oriented feature usability evaluation in fingerprint segmentation

NASA Astrophysics Data System (ADS)

Li, Ying; Yin, Yilong; Yang, Gongping

2013-06-01

Existing fingerprint segmentation methods usually process fingerprint images captured by different sensors with the same feature or feature set. We propose to improve the fingerprint segmentation result in view of an important fact that images from different sensors have different characteristics for segmentation. Feature usability evaluation, which means to evaluate the usability of features to find the personalized feature or feature set for different sensors to improve the performance of segmentation. The need for feature usability evaluation for fingerprint segmentation is raised and analyzed as a new issue. To address this issue, we present a decision-tree-based feature-usability evaluation method, which utilizes a C4.5 decision tree algorithm to evaluate and pick the best suitable feature or feature set for fingerprint segmentation from a typical candidate feature set. We apply the novel method on the FVC2002 database of fingerprint images, which are acquired by four different respective sensors and technologies. Experimental results show that the accuracy of segmentation is improved, and time consumption for feature extraction is dramatically reduced with selected feature(s).

Novel and general approach to linear filter design for contrast-to-noise ratio enhancement of magnetic resonance images with multiple interfering features in the scene

NASA Astrophysics Data System (ADS)

Soltanian-Zadeh, Hamid; Windham, Joe P.

1992-04-01

Maximizing the minimum absolute contrast-to-noise ratios (CNRs) between a desired feature and multiple interfering processes, by linear combination of images in a magnetic resonance imaging (MRI) scene sequence, is attractive for MRI analysis and interpretation. A general formulation of the problem is presented, along with a novel solution utilizing the simple and numerically stable method of Gram-Schmidt orthogonalization. We derive explicit solutions for the case of two interfering features first, then for three interfering features, and, finally, using a typical example, for an arbitrary number of interfering feature. For the case of two interfering features, we also provide simplified analytical expressions for the signal-to-noise ratios (SNRs) and CNRs of the filtered images. The technique is demonstrated through its applications to simulated and acquired MRI scene sequences of a human brain with a cerebral infarction. For these applications, a 50 to 100% improvement for the smallest absolute CNR is obtained.

Ultra-wideband three-dimensional optoacoustic tomography.

PubMed

Gateau, Jérôme; Chekkoury, Andrei; Ntziachristos, Vasilis

2013-11-15

Broadband optoacoustic waves generated by biological tissues excited with nanosecond laser pulses carry information corresponding to a wide range of geometrical scales. Typically, the frequency content present in the signals generated during optoacoustic imaging is much larger compared to the frequency band captured by common ultrasonic detectors, the latter typically acting as bandpass filters. To image optical absorption within structures ranging from entire organs to microvasculature in three dimensions, we implemented optoacoustic tomography with two ultrasound linear arrays featuring a center frequency of 6 and 24 MHz, respectively. In the present work, we show that complementary information on anatomical features could be retrieved and provide a better understanding on the localization of structures in the general anatomy by analyzing multi-bandwidth datasets acquired on a freshly excised kidney.

Unusual scarring patterns on cardiac magnetic resonance imaging: A potentially treatable etiology not to be missed.

PubMed

Jordan, Andrew; Lyne, Jonathan; Wong, Tom

2010-04-01

A case of cardiomyopathy and ventricular tachycardia previously assumed to be idiopathic in origin is described. Investigation with cardiac magnetic resonance imaging prompted the diagnosis and successful treatment of an underlying disorder based on typical scarring patterns seen with late gadolinium enhancement. The present report suggests that clinicians should have a low threshold for actively excluding this condition in patients presenting with cardiomyopathy, even in the absence of other disease features, particularly if typical scarring patterns are found on cardiac magnetic resonance imaging because disease-specific therapy appears to significantly improve both symptoms and prognosis.

Imaging in the assessment and management of athletic pubalgia.

PubMed

Robinson, Philip; Bhat, Vineet; English, Bryan

2011-02-01

This article reviews the clinical, anatomical, and biomechanical basis of pubalgia and relates it to the potential imaging findings and subsequent management. Although the magnetic resonance imaging features typically seen in symptomatic athletes are emphasized, this condition remains a complex clinical problem, and treatment addressing the functional rehabilitation of the entire region is highlighted. © Thieme Medical Publishers.

Real-space post-processing correction of thermal drift and piezoelectric actuator nonlinearities in scanning tunneling microscope images.

PubMed

Yothers, Mitchell P; Browder, Aaron E; Bumm, Lloyd A

2017-01-01

We have developed a real-space method to correct distortion due to thermal drift and piezoelectric actuator nonlinearities on scanning tunneling microscope images using Matlab. The method uses the known structures typically present in high-resolution atomic and molecularly resolved images as an internal standard. Each image feature (atom or molecule) is first identified in the image. The locations of each feature's nearest neighbors are used to measure the local distortion at that location. The local distortion map across the image is simultaneously fit to our distortion model, which includes thermal drift in addition to piezoelectric actuator hysteresis and creep. The image coordinates of the features and image pixels are corrected using an inverse transform from the distortion model. We call this technique the thermal-drift, hysteresis, and creep transform. Performing the correction in real space allows defects, domain boundaries, and step edges to be excluded with a spatial mask. Additional real-space image analyses are now possible with these corrected images. Using graphite(0001) as a model system, we show lattice fitting to the corrected image, averaged unit cell images, and symmetry-averaged unit cell images. Statistical analysis of the distribution of the image features around their best-fit lattice sites measures the aggregate noise in the image, which can be expressed as feature confidence ellipsoids.

Real-space post-processing correction of thermal drift and piezoelectric actuator nonlinearities in scanning tunneling microscope images

NASA Astrophysics Data System (ADS)

Yothers, Mitchell P.; Browder, Aaron E.; Bumm, Lloyd A.

2017-01-01

We have developed a real-space method to correct distortion due to thermal drift and piezoelectric actuator nonlinearities on scanning tunneling microscope images using Matlab. The method uses the known structures typically present in high-resolution atomic and molecularly resolved images as an internal standard. Each image feature (atom or molecule) is first identified in the image. The locations of each feature's nearest neighbors are used to measure the local distortion at that location. The local distortion map across the image is simultaneously fit to our distortion model, which includes thermal drift in addition to piezoelectric actuator hysteresis and creep. The image coordinates of the features and image pixels are corrected using an inverse transform from the distortion model. We call this technique the thermal-drift, hysteresis, and creep transform. Performing the correction in real space allows defects, domain boundaries, and step edges to be excluded with a spatial mask. Additional real-space image analyses are now possible with these corrected images. Using graphite(0001) as a model system, we show lattice fitting to the corrected image, averaged unit cell images, and symmetry-averaged unit cell images. Statistical analysis of the distribution of the image features around their best-fit lattice sites measures the aggregate noise in the image, which can be expressed as feature confidence ellipsoids.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Image-guided filtering for improving photoacoustic tomographic image reconstruction.

PubMed

Awasthi, Navchetan; Kalva, Sandeep Kumar; Pramanik, Manojit; Yalavarthy, Phaneendra K

2018-06-01

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

PubMed

Larue, Ruben T H M; Defraene, Gilles; De Ruysscher, Dirk; Lambin, Philippe; van Elmpt, Wouter

2017-02-01

Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years, quantitative imaging features derived from CT, positron emission tomography and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare owing to a lack of standardized methodologies to conduct quantitative image analyses. In this review, we aim to present an overview of the current challenges, technical routines and protocols that are involved in quantitative imaging studies. The first issue that should be overcome is the dependency of several features on the scan acquisition and image reconstruction parameters. Adopting consistent methods in the subsequent target segmentation step is evenly crucial. To further establish robust quantitative image analyses, standardization or at least calibration of imaging features based on different feature extraction settings is required, especially for texture- and filter-based features. Several open-source and commercial software packages to perform feature extraction are currently available, all with slightly different functionalities, which makes benchmarking quite challenging. The number of imaging features calculated is typically larger than the number of patients studied, which emphasizes the importance of proper feature selection and prediction model-building routines to prevent overfitting. Even though many of these challenges still need to be addressed before quantitative imaging can be brought into daily clinical practice, radiomics is expected to be a critical component for the integration of image-derived information to personalize treatment in the future.

Feature Selection Methods for Zero-Shot Learning of Neural Activity.

PubMed

Caceres, Carlos A; Roos, Matthew J; Rupp, Kyle M; Milsap, Griffin; Crone, Nathan E; Wolmetz, Michael E; Ratto, Christopher R

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

The Value of 5-Aminolevulinic Acid in Low-grade Gliomas and High-grade Gliomas Lacking Glioblastoma Imaging Features: An Analysis Based on Fluorescence, Magnetic Resonance Imaging, 18F-Fluoroethyl Tyrosine Positron Emission Tomography, and Tumor Molecular Factors.

PubMed

Jaber, Mohammed; Wölfer, Johannes; Ewelt, Christian; Holling, Markus; Hasselblatt, Martin; Niederstadt, Thomas; Zoubi, Tarek; Weckesser, Matthias; Stummer, Walter

2016-03-01

Approximately 20% of grade II and most grade III gliomas fluoresce after 5-aminolevulinic acid (5-ALA) application. Conversely, approximately 30% of nonenhancing gliomas are actually high grade. The aim of this study was to identify preoperative factors (ie, age, enhancement, 18F-fluoroethyl tyrosine positron emission tomography [F-FET PET] uptake ratios) for predicting fluorescence in gliomas without typical glioblastomas imaging features and to determine whether fluorescence will allow prediction of tumor grade or molecular characteristics. Patients harboring gliomas without typical glioblastoma imaging features were given 5-ALA. Fluorescence was recorded intraoperatively, and biopsy specimens collected from fluorescing tissue. World Health Organization (WHO) grade, Ki-67/MIB-1 index, IDH1 (R132H) mutation status, O-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and 1p/19q co-deletion status were assessed. Predictive factors for fluorescence were derived from preoperative magnetic resonance imaging and F-FET PET. Classification and regression tree analysis and receiver-operating-characteristic curves were generated for defining predictors. Of 166 tumors, 82 were diagnosed as WHO grade II, 76 as grade III, and 8 as glioblastomas grade IV. Contrast enhancement, tumor volume, and F-FET PET uptake ratio >1.85 predicted fluorescence. Fluorescence correlated with WHO grade (P < .001) and Ki-67/MIB-1 index (P < .001), but not with MGMT promoter methylation status, IDH1 mutation status, or 1p19q co-deletion status. The Ki-67/MIB-1 index in fluorescing grade III gliomas was higher than in nonfluorescing tumors, whereas in fluorescing and nonfluorescing grade II tumors, no differences were noted. Age, tumor volume, and F-FET PET uptake are factors predicting 5-ALA-induced fluorescence in gliomas without typical glioblastoma imaging features. Fluorescence was associated with an increased Ki-67/MIB-1 index and high-grade pathology. Whether fluorescence in grade II gliomas identifies a subtype with worse prognosis remains to be determined.

Ultra-Widefield Steering-Based Spectral-Domain Optical Coherence Tomography Imaging of the Retinal Periphery.

PubMed

Choudhry, Netan; Golding, John; Manry, Matthew W; Rao, Rajesh C

2016-06-01

To describe the spectral-domain optical coherence tomography (SD OCT) features of peripheral retinal findings using an ultra-widefield (UWF) steering technique to image the retinal periphery. Observational study. A total of 68 patients (68 eyes) with 19 peripheral retinal features. Spectral-domain OCT-based structural features. Nineteen peripheral retinal features, including vortex vein, congenital hypertrophy of the retinal pigment epithelium, pars plana, ora serrata pearl, typical cystoid degeneration (TCD), cystic retinal tuft, meridional fold, lattice and cobblestone degeneration, retinal hole, retinal tear, rhegmatogenous retinal detachment, typical degenerative senile retinoschisis, peripheral laser coagulation scars, ora tooth, cryopexy scars (retinal tear and treated retinoblastoma scar), bone spicules, white without pressure, and peripheral drusen, were identified by peripheral clinical examination. Near-infrared scanning laser ophthalmoscopy images and SD OCT of these entities were registered to UWF color photographs. Spectral-domain OCT resolved structural features of all peripheral findings. Dilated hyporeflective tubular structures within the choroid were observed in the vortex vein. Loss of retinal lamination, neural retinal attenuation, retinal pigment epithelium loss, or hypertrophy was seen in several entities, including congenital hypertrophy of the retinal pigment epithelium, ora serrata pearl, TCD, cystic retinal tuft, meridional fold, lattice, and cobblestone degenerations. Hyporeflective intraretinal spaces, indicating cystoid or schitic fluid, were seen in ora serrata pearl, ora tooth, TCD, cystic retinal tuft, meridional fold, retinal hole, and typical degenerative senile retinoschisis. The vitreoretinal interface, which often consisted of lamellae-like structures of the condensed cortical vitreous near or adherent to the neural retina, appeared clearly in most peripheral findings, confirming its association with many low-risk and vision-threatening pathologies, such as lattice degeneration, meridional folds, retinal breaks, and rhegmatogenous retinal detachments. Ultra-widefield steering-based SD OCT imaging of the retinal periphery is feasible with current commercially available devices and provides detailed anatomic information of the peripheral retina, including benign and pathologic entities, not previously imaged. This imaging technique may deepen our structural understanding of these entities and their potentially associated macular and systemic pathologies, and may influence decision-making in clinical practice, particularly in areas with teleretinal capabilities but poor access to retinal specialists. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Magnetic resonance imaging (MRI) of oak trees infected with Phytophthora ramorum to determine potential avenues of infection in bark

Treesearch

Edwin R. Florance

2006-01-01

Non-destructive magnetic resonance imaging (MRI) revealed pathological anatomical features of coast live oak trees (Quercus agrifolia) that were naturally infected with Phytophthora ramorum. Fresh excised whole slices showing typical macroscopic cankers and bleeding were examined. Infected areas (i.e. cankers) were compared to...

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

NASA Astrophysics Data System (ADS)

Leijenaar, Ralph T. H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J. C.; Troost, Esther G. C.; Boellaard, Ronald; Aerts, Hugo J. W. L.; Gillies, Robert J.; Lambin, Philippe

2015-08-01

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values-which was used as a surrogate for textural feature interpretation-between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Machine Learning for Medical Imaging

PubMed Central

Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054

Machine Learning for Medical Imaging.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Infiltrative cervical lesions causing symptomatic occipital neuralgia.

PubMed

Sierra-Hidalgo, F; Ruíz, J; Morales-Cartagena, A; Martínez-Salio, A; Serna, J de la; Hernández-Gallego, J

2011-10-01

Occipital neuralgia is a well-recognized cause of posterior head and neck pain that may associate mild sensory changes in the cutaneous distribution of the occipital nerves, lacking a recognizable local structural aetiology in most cases. Atypical clinical features or an abnormal neurological examination are alerts for a potential underlying cause of pain, although cases of clinically typical occipital neuralgia as isolated manifestation of lesions of the cervical spinal cord, cervical roots, or occipital nerves have been increasingly reported. We describe two cases (one with typical and another one with atypical clinical features) of occipital neuralgia secondary to paravertebral pyomyositis and vertebral relapse of multiple myeloma in patients with relevant medical history that aroused the possibility of an underlying structural lesion. We discuss the need for cranio-cervical magnetic resonance imaging in all patients with occipital neuralgia, even when typical clinical features are present and neurological examination is completely normal.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

PubMed Central

Caceres, Carlos A.; Roos, Matthew J.; Rupp, Kyle M.; Milsap, Griffin; Crone, Nathan E.; Wolmetz, Michael E.; Ratto, Christopher R.

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy. PMID:28690513

Tunable filters for multispectral imaging of aeronomical features

NASA Astrophysics Data System (ADS)

Goenka, C.; Semeter, J. L.; Noto, J.; Dahlgren, H.; Marshall, R.; Baumgardner, J.; Riccobono, J.; Migliozzi, M.

2013-10-01

Multispectral imaging of optical emissions in the Earth's upper atmosphere unravels vital information about dynamic phenomena in the Earth-space environment. Wavelength tunable filters allow us to accomplish this without using filter wheels or multiple imaging setups, but with identifiable caveats and trade-offs. We evaluate one such filter, a liquid crystal Fabry-Perot etalon, as a potential candidate for the next generation of imagers for aeronomy. The tunability of such a filter can be exploited in imaging features such as the 6300-6364 Å oxygen emission doublet, or studying the rotational temperature of N2+ in the 4200-4300 Å range, observations which typically require multiple instruments. We further discuss the use of this filter in an optical instrument, called the Liquid Crystal Hyperspectral Imager (LiCHI), which will be developed to make simultaneous measurements in various wavelength ranges.

Iris Matching Based on Personalized Weight Map.

PubMed

Dong, Wenbo; Sun, Zhenan; Tan, Tieniu

2011-09-01

Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.

Improving depth estimation from a plenoptic camera by patterned illumination

NASA Astrophysics Data System (ADS)

Marshall, Richard J.; Meah, Chris J.; Turola, Massimo; Claridge, Ela; Robinson, Alex; Bongs, Kai; Gruppetta, Steve; Styles, Iain B.

2015-05-01

Plenoptic (light-field) imaging is a technique that allows a simple CCD-based imaging device to acquire both spatially and angularly resolved information about the "light-field" from a scene. It requires a microlens array to be placed between the objective lens and the sensor of the imaging device1 and the images under each microlens (which typically span many pixels) can be computationally post-processed to shift perspective, digital refocus, extend the depth of field, manipulate the aperture synthetically and generate a depth map from a single image. Some of these capabilities are rigid functions that do not depend upon the scene and work by manipulating and combining a well-defined set of pixels in the raw image. However, depth mapping requires specific features in the scene to be identified and registered between consecutive microimages. This process requires that the image has sufficient features for the registration, and in the absence of such features the algorithms become less reliable and incorrect depths are generated. The aim of this study is to investigate the generation of depth-maps from light-field images of scenes with insufficient features for accurate registration, using projected patterns to impose a texture on the scene that provides sufficient landmarks for the registration methods.

No Effect of Featural Attention on Body Size Aftereffects

PubMed Central

Stephen, Ian D.; Bickersteth, Chloe; Mond, Jonathan; Stevenson, Richard J.; Brooks, Kevin R.

2016-01-01

Prolonged exposure to images of narrow bodies has been shown to induce a perceptual aftereffect, such that observers’ point of subjective normality (PSN) for bodies shifts toward narrower bodies. The converse effect is shown for adaptation to wide bodies. In low-level stimuli, object attention (attention directed to the object) and spatial attention (attention directed to the location of the object) have been shown to increase the magnitude of visual aftereffects, while object-based attention enhances the adaptation effect in faces. It is not known whether featural attention (attention directed to a specific aspect of the object) affects the magnitude of adaptation effects in body stimuli. Here, we manipulate the attention of Caucasian observers to different featural information in body images, by asking them to rate the fatness or sex typicality of male and female bodies manipulated to appear fatter or thinner than average. PSNs for body fatness were taken at baseline and after adaptation, and a change in PSN (ΔPSN) was calculated. A body size adaptation effect was found, with observers who viewed fat bodies showing an increased PSN, and those exposed to thin bodies showing a reduced PSN. However, manipulations of featural attention to body fatness or sex typicality produced equivalent results, suggesting that featural attention may not affect the strength of the body size aftereffect. PMID:27597835

No Effect of Featural Attention on Body Size Aftereffects.

PubMed

Stephen, Ian D; Bickersteth, Chloe; Mond, Jonathan; Stevenson, Richard J; Brooks, Kevin R

2016-01-01

Prolonged exposure to images of narrow bodies has been shown to induce a perceptual aftereffect, such that observers' point of subjective normality (PSN) for bodies shifts toward narrower bodies. The converse effect is shown for adaptation to wide bodies. In low-level stimuli, object attention (attention directed to the object) and spatial attention (attention directed to the location of the object) have been shown to increase the magnitude of visual aftereffects, while object-based attention enhances the adaptation effect in faces. It is not known whether featural attention (attention directed to a specific aspect of the object) affects the magnitude of adaptation effects in body stimuli. Here, we manipulate the attention of Caucasian observers to different featural information in body images, by asking them to rate the fatness or sex typicality of male and female bodies manipulated to appear fatter or thinner than average. PSNs for body fatness were taken at baseline and after adaptation, and a change in PSN (ΔPSN) was calculated. A body size adaptation effect was found, with observers who viewed fat bodies showing an increased PSN, and those exposed to thin bodies showing a reduced PSN. However, manipulations of featural attention to body fatness or sex typicality produced equivalent results, suggesting that featural attention may not affect the strength of the body size aftereffect.

Harvesting geographic features from heterogeneous raster maps

NASA Astrophysics Data System (ADS)

Chiang, Yao-Yi

2010-11-01

Raster maps offer a great deal of geospatial information and are easily accessible compared to other geospatial data. However, harvesting geographic features locked in heterogeneous raster maps to obtain the geospatial information is challenging. This is because of the varying image quality of raster maps (e.g., scanned maps with poor image quality and computer-generated maps with good image quality), the overlapping geographic features in maps, and the typical lack of metadata (e.g., map geocoordinates, map source, and original vector data). Previous work on map processing is typically limited to a specific type of map and often relies on intensive manual work. In contrast, this thesis investigates a general approach that does not rely on any prior knowledge and requires minimal user effort to process heterogeneous raster maps. This approach includes automatic and supervised techniques to process raster maps for separating individual layers of geographic features from the maps and recognizing geographic features in the separated layers (i.e., detecting road intersections, generating and vectorizing road geometry, and recognizing text labels). The automatic technique eliminates user intervention by exploiting common map properties of how road lines and text labels are drawn in raster maps. For example, the road lines are elongated linear objects and the characters are small connected-objects. The supervised technique utilizes labels of road and text areas to handle complex raster maps, or maps with poor image quality, and can process a variety of raster maps with minimal user input. The results show that the general approach can handle raster maps with varying map complexity, color usage, and image quality. By matching extracted road intersections to another geospatial dataset, we can identify the geocoordinates of a raster map and further align the raster map, separated feature layers from the map, and recognized features from the layers with the geospatial dataset. The road vectorization and text recognition results outperform state-of-art commercial products, and with considerably less user input. The approach in this thesis allows us to make use of the geospatial information of heterogeneous maps locked in raster format.

Iris recognition using possibilistic fuzzy matching on local features.

PubMed

Tsai, Chung-Chih; Lin, Heng-Yi; Taur, Jinshiuh; Tao, Chin-Wang

2012-02-01

In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

An update of commercial infrared sensing and imaging instruments

NASA Technical Reports Server (NTRS)

Kaplan, Herbert

1989-01-01

A classification of infrared sensing instruments by type and application, listing commercially available instruments, from single point thermal probes to on-line control sensors, to high speed, high resolution imaging systems is given. A review of performance specifications follows, along with a discussion of typical thermographic display approaches utilized by various imager manufacturers. An update report on new instruments, new display techniques and newly introduced features of existing instruments is given.

a New Paradigm for Matching - and Aerial Images

NASA Astrophysics Data System (ADS)

Koch, T.; Zhuo, X.; Reinartz, P.; Fraundorfer, F.

2016-06-01

This paper investigates the performance of SIFT-based image matching regarding large differences in image scaling and rotation, as this is usually the case when trying to match images captured from UAVs and airplanes. This task represents an essential step for image registration and 3d-reconstruction applications. Various real world examples presented in this paper show that SIFT, as well as A-SIFT perform poorly or even fail in this matching scenario. Even if the scale difference in the images is known and eliminated beforehand, the matching performance suffers from too few feature point detections, ambiguous feature point orientations and rejection of many correct matches when applying the ratio-test afterwards. Therefore, a new feature matching method is provided that overcomes these problems and offers thousands of matches by a novel feature point detection strategy, applying a one-to-many matching scheme and substitute the ratio-test by adding geometric constraints to achieve geometric correct matches at repetitive image regions. This method is designed for matching almost nadir-directed images with low scene depth, as this is typical in UAV and aerial image matching scenarios. We tested the proposed method on different real world image pairs. While standard SIFT failed for most of the datasets, plenty of geometrical correct matches could be found using our approach. Comparing the estimated fundamental matrices and homographies with ground-truth solutions, mean errors of few pixels can be achieved.

Feature-based pairwise retinal image registration by radial distortion correction

NASA Astrophysics Data System (ADS)

Lee, Sangyeol; Abràmoff, Michael D.; Reinhardt, Joseph M.

2007-03-01

Fundus camera imaging is widely used to document disorders such as diabetic retinopathy and macular degeneration. Multiple retinal images can be combined together through a procedure known as mosaicing to form an image with a larger field of view. Mosaicing typically requires multiple pairwise registrations of partially overlapped images. We describe a new method for pairwise retinal image registration. The proposed method is unique in that the radial distortion due to image acquisition is corrected prior to the geometric transformation. Vessel lines are detected using the Hessian operator and are used as input features to the registration. Since the overlapping region is typically small in a retinal image pair, only a few correspondences are available, thus limiting the applicable model to an afine transform at best. To recover the distortion due to curved-surface of retina and lens optics, a combined approach of an afine model with a radial distortion correction is proposed. The parameters of the image acquisition and radial distortion models are estimated during an optimization step that uses Powell's method driven by the vessel line distance. Experimental results using 20 pairs of green channel images acquired from three subjects with a fundus camera confirmed that the afine model with distortion correction could register retinal image pairs to within 1.88+/-0.35 pixels accuracy (mean +/- standard deviation) assessed by vessel line error, which is 17% better than the afine-only approach. Because the proposed method needs only two correspondences, it can be applied to obtain good registration accuracy even in the case of small overlap between retinal image pairs.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report

PubMed Central

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-01-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor. PMID:29552133

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report.

PubMed

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-04-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor.

Pitfalls in soft tissue sarcoma imaging: chronic expanding hematomas.

PubMed

Jahed, Kiarash; Khazai, Behnaz; Umpierrez, Monica; Subhawong, Ty K; Singer, Adam D

2018-01-01

Solid or nodular enhancement is typical of soft tissue sarcomas although high grade soft tissue sarcomas and those with internal hemorrhage often appear heterogeneous with areas of nonenhancement and solid or nodular enhancement. These MRI findings often prompt an orthopedic oncology referral, a biopsy or surgery. However, not all masses with these imaging findings are malignant. We report the multimodality imaging findings of two surgically proven chronic expanding hematomas (CEH) with imaging features that mimicked sarcomas. A third case of nonenhancing CEH of the lower extremity is also presented as a comparison. It is important that in the correct clinical scenario with typical imaging findings, the differential diagnosis of a chronic expanding hematoma be included in the workup of these patients. An image-guided biopsy of nodular tissue within such masses that proves to be negative for malignancy should not necessarily be considered discordant. A correct diagnosis may prevent a morbid unnecessary surgery and may indicate the need for a conservative noninvasive follow-up with imaging.

CNS cavernous haemangioma: "popcorn" in the brain and spinal cord.

PubMed

Hegde, A N; Mohan, S; Lim, C C T

2012-04-01

Cavernous haemangiomas (CH) are relatively uncommon non-shunting vascular malformations of the central nervous system and can present with seizures or with neurological deficits due to haemorrhage. Radiologists can often suggest the diagnosis of CH based on characteristic magnetic resonance imaging (MRI) features, thus avoiding further invasive procedures such as digital subtraction angiography or surgical biopsy. Although typical MRI appearance combined with the presence of multiple focal low signal lesions on T2*-weighted images or the presence of one or more developmental venous anomaly within the brain can improve the diagnostic confidence, serial imaging studies are often required if a solitary CH presents at a time when the imaging appearances had not yet matured to the typical "popcorn" appearance. Copyright © 2011 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Somatomedin C deficiency in Asian sisters.

PubMed Central

McGraw, M E; Price, D A; Hill, D J

1986-01-01

Two sisters of Asian origin showed typical clinical and biochemical features of primary somatomedin C (SM-C) deficiency (Laron dwarfism). Abnormalities of SM-C binding proteins were observed, one sister lacking the high molecular weight (150 Kd) protein. Images Figure PMID:2434036

Feature selection and definition for contours classification of thermograms in breast cancer detection

NASA Astrophysics Data System (ADS)

Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold; Cichosz, Paweł

2016-09-01

This contribution introduces the method of cancer pathologies detection on breast skin temperature distribution images. The use of thermosensitive foils applied to the breasts skin allows to create thermograms, which displays the amount of infrared energy emitted by all breast cells. The significant foci of hyperthermia or inflammation are typical for cancer cells. That foci can be recognized on thermograms as a contours, which are the areas of higher temperature. Every contour can be converted to a feature set that describe it, using the raw, central, Hu, outline, Fourier and colour moments of image pixels processing. This paper defines also the new way of describing a set of contours through theirs neighbourhood relations. Contribution introduces moreover the way of ranking and selecting most relevant features. Authors used Neural Network with Gevrey`s concept and recursive feature elimination, to estimate feature importance.

Clinicopathological features of pulmonary cryptococcosis with cryptococcal titan cells: a comparative analysis of 27 cases.

PubMed

Wang, Jing-Mei; Zhou, Qiang; Cai, Hou-Rong; Zhuang, Yi; Zhang, Yi-Fen; Xin, Xiao-Yan; Meng, Fan-Qing; Wang, Ya-Ping

2014-01-01

In addition to the typical size, Cryptococcus neoformans can enlarge its size to form titan cells during infection, and its diameter can reach up to 100 μm. Clinical reports about cryptococcal titan cells are rare. Most studies focus on aspects of animal models of infection with titan cells. Herein, we report the clinical and imaging characteristics and histopathologic features of 3 patients with titan cells and 27 patients with pathogens of typical size, and describe the morphological characteristics of titan cells in details. Histologically, 3 patients with titan cells show necrosis, fibrosis and macrophage accumulation. The titan cells appear in necrotic tissue and between macrophages, and have thick wall with unstained halo around them and diameters range from 20 to 80 μm with characteristic of narrow-necked single budding. There are also organisms with typical size. All 27 patients with normal pathogens show epithelioid granulomatous lesions. There is no significantly difference in clinical and imaging feature between the two groups. Cryptococcus neoformans exhibits a striking morphological change for the formation of titan cells during pulmonary infection, which will result in misdiagnosis and under diagnosis. The histopathological changes may be new manifestation, which need to be further confirmed by the study with animal models of infection and the observation of more clinical cases. Careful observation of the tissue sections is necessary.

Quantitative Imaging in Cancer Evolution and Ecology

PubMed Central

Grove, Olya; Gillies, Robert J.

2013-01-01

Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral Darwinian dynamics before and during therapy. Advances in image analysis will place clinical imaging in an increasingly central role in the development of evolution-based patient-specific cancer therapy. © RSNA, 2013 PMID:24062559

Does the choice of display system influence perception and visibility of clinically relevant features in digital pathology images?

NASA Astrophysics Data System (ADS)

Kimpe, Tom; Rostang, Johan; Avanaki, Ali; Espig, Kathryn; Xthona, Albert; Cocuranu, Ioan; Parwani, Anil V.; Pantanowitz, Liron

2014-03-01

Digital pathology systems typically consist of a slide scanner, processing software, visualization software, and finally a workstation with display for visualization of the digital slide images. This paper studies whether digital pathology images can look different when presenting them on different display systems, and whether these visual differences can result in different perceived contrast of clinically relevant features. By analyzing a set of four digital pathology images of different subspecialties on three different display systems, it was concluded that pathology images look different when visualized on different display systems. The importance of these visual differences is elucidated when they are located in areas of the digital slide that contain clinically relevant features. Based on a calculation of dE2000 differences between background and clinically relevant features, it was clear that perceived contrast of clinically relevant features is influenced by the choice of display system. Furthermore, it seems that the specific calibration target chosen for the display system has an important effect on the perceived contrast of clinically relevant features. Preliminary results suggest that calibrating to DICOM GSDF calibration performed slightly worse than sRGB, while a new experimental calibration target CSDF performed better than both DICOM GSDF and sRGB. This result is promising as it suggests that further research work could lead to better definition of an optimized calibration target for digital pathology images resulting in a positive effect on clinical performance.

High-performance camera module for fast quality inspection in industrial printing applications

NASA Astrophysics Data System (ADS)

Fürtler, Johannes; Bodenstorfer, Ernst; Mayer, Konrad J.; Brodersen, Jörg; Heiss, Dorothea; Penz, Harald; Eckel, Christian; Gravogl, Klaus; Nachtnebel, Herbert

2007-02-01

Today, printing products which must meet highest quality standards, e.g., banknotes, stamps, or vouchers, are automatically checked by optical inspection systems. Typically, the examination of fine details of the print or security features demands images taken from various perspectives, with different spectral sensitivity (visible, infrared, ultraviolet), and with high resolution. Consequently, the inspection system is equipped with several cameras and has to cope with an enormous data rate to be processed in real-time. Hence, it is desirable to move image processing tasks into the camera to reduce the amount of data which has to be transferred to the (central) image processing system. The idea is to transfer relevant information only, i.e., features of the image instead of the raw image data from the sensor. These features are then further processed. In this paper a color line-scan camera for line rates up to 100 kHz is presented. The camera is based on a commercial CMOS (complementary metal oxide semiconductor) area image sensor and a field programmable gate array (FPGA). It implements extraction of image features which are well suited to detect print flaws like blotches of ink, color smears, splashes, spots and scratches. The camera design and several image processing methods implemented on the FPGA are described, including flat field correction, compensation of geometric distortions, color transformation, as well as decimation and neighborhood operations.

Diagenetic Features Analyzed by ChemCam/Curiosity at Pahrump Hills, Gale Crater, Mars

NASA Technical Reports Server (NTRS)

Nachon, M.; Mangold, N.; Cousin, A.; Forni, O.; Anderson, R. B.; Blank, J. G.; Calef, F.; Clegg, S.; Fabre, C.; Fisk, M.;

Breed-Specific Magnetic Resonance Imaging Characteristics of Necrotizing Encephalitis in Dogs

PubMed Central

Flegel, Thomas

2017-01-01

Diagnosing necrotizing encephalitis, with its subcategories of necrotizing leukoencephalitis and necrotizing meningoencephalitis, based on magnetic resonance imaging alone can be challenging. However, there are breed-specific imaging characteristics in both subcategories that allow establishing a clinical diagnosis with a relatively high degree of certainty. Typical breed specific imaging features, such as lesion distribution, signal intensity, contrast enhancement, and gross changes of brain structure (midline shift, ventriculomegaly, and brain herniation) are summarized here, using current literature, for the most commonly affected canine breeds: Yorkshire Terrier, French Bulldog, Pug, and Chihuahua. PMID:29255715

Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

PubMed

Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

2016-07-01

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

Image segmentation by hierarchial agglomeration of polygons using ecological statistics

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-04-23

A method for rapid hierarchical image segmentation based on perceptually driven contour completion and scene statistics is disclosed. The method begins with an initial fine-scale segmentation of an image, such as obtained by perceptual completion of partial contours into polygonal regions using region-contour correspondences established by Delaunay triangulation of edge pixels as implemented in VISTA. The resulting polygons are analyzed with respect to their size and color/intensity distributions and the structural properties of their boundaries. Statistical estimates of granularity of size, similarity of color, texture, and saliency of intervening boundaries are computed and formulated into logical (Boolean) predicates. The combined satisfiability of these Boolean predicates by a pair of adjacent polygons at a given segmentation level qualifies them for merging into a larger polygon representing a coarser, larger-scale feature of the pixel image and collectively obtains the next level of polygonal segments in a hierarchy of fine-to-coarse segmentations. The iterative application of this process precipitates textured regions as polygons with highly convolved boundaries and helps distinguish them from objects which typically have more regular boundaries. The method yields a multiscale decomposition of an image into constituent features that enjoy a hierarchical relationship with features at finer and coarser scales. This provides a traversable graph structure from which feature content and context in terms of other features can be derived, aiding in automated image understanding tasks. The method disclosed is highly efficient and can be used to decompose and analyze large images.

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.

Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

PubMed

Li, Jiansen; Song, Ying; Zhu, Zhen; Zhao, Jun

2017-05-01

Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction.

Body Talk: Body Image Commentary on Queerty.com.

PubMed

Schwartz, Joseph; Grimm, Josh

2016-08-01

In this study, we conducted a content analysis of 243 photographic images of men published on the gay male-oriented blog Queerty.com. We also analyzed 435 user-generated comments from a randomly selected 1-year sample. Focusing on images' body types, we found that the range of body types featured on the blog was quite narrow-the vast majority of images had very low levels of body fat and very high levels of muscularity. Users' body image-related comments typically endorsed and celebrated images; critiques of images were comparatively rare. Perspectives from objectification theory and social comparison theory suggest that the images and commentary found on the blog likely reinforce unhealthy body image in gay male communities.

Live-cell Imaging Approaches for the Investigation of Xenobiotic-Induced Oxidant Stress

EPA Science Inventory

BACKGROUND: Oxidant stress is arguably a universal feature in toxicology. Research studies on the role of oxidant stress induced by xenobiotic exposures have typically relied on the identification of damaged biomolecules using a variety of conventional biochemical and molecular t...

Image Reconstruction from Under sampled Fourier Data Using the Polynomial Annihilation Transform

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

Archibald, Richard K.; Gelb, Anne; Platte, Rodrigo

Fourier samples are collected in a variety of applications including magnetic resonance imaging and synthetic aperture radar. The data are typically under-sampled and noisy. In recent years, l 1 regularization has received considerable attention in designing image reconstruction algorithms from under-sampled and noisy Fourier data. The underlying image is assumed to have some sparsity features, that is, some measurable features of the image have sparse representation. The reconstruction algorithm is typically designed to solve a convex optimization problem, which consists of a fidelity term penalized by one or more l 1 regularization terms. The Split Bregman Algorithm provides a fastmore » explicit solution for the case when TV is used for the l1l1 regularization terms. Due to its numerical efficiency, it has been widely adopted for a variety of applications. A well known drawback in using TV as an l 1 regularization term is that the reconstructed image will tend to default to a piecewise constant image. This issue has been addressed in several ways. Recently, the polynomial annihilation edge detection method was used to generate a higher order sparsifying transform, and was coined the “polynomial annihilation (PA) transform.” This paper adapts the Split Bregman Algorithm for the case when the PA transform is used as the l 1 regularization term. In so doing, we achieve a more accurate image reconstruction method from under-sampled and noisy Fourier data. Our new method compares favorably to the TV Split Bregman Algorithm, as well as to the popular TGV combined with shearlet approach.« less

Trading efficiency for effectiveness in similarity-based indexing for image databases

NASA Astrophysics Data System (ADS)

Barros, Julio E.; French, James C.; Martin, Worthy N.; Kelly, Patrick M.

1995-11-01

Image databases typically manage feature data that can be viewed as points in a feature space. Some features, however, can be better expressed as a collection of points or described by a probability distribution function (PDF) rather than as a single point. In earlier work we introduced a similarity measure and a method for indexing and searching the PDF descriptions of these items that guarantees an answer equivalent to sequential search. Unfortunately, certain properties of the data can restrict the efficiency of that method. In this paper we extend that work and examine trade-offs between efficiency and answer quality or effectiveness. These trade-offs reduce the amount of work required during a search by reducing the number of undesired items fetched without excluding an excessive number of the desired ones.

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

PubMed

Valous, Nektarios A; Mendoza, Fernando; Sun, Da-Wen; Allen, Paul

2010-03-01

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values. Copyright 2009 Elsevier Ltd. All rights reserved.

Semi-automatic segmentation of nonviable cardiac tissue using cine and delayed enhancement magnetic resonance images

NASA Astrophysics Data System (ADS)

O'Donnell, Thomas P.; Xu, Ning; Setser, Randolph M.; White, Richard D.

2003-05-01

Post myocardial infarction, the identification and assessment of non-viable (necrotic) tissues is necessary for effective development of intervention strategies and treatment plans. Delayed Enhancement Magnetic Resonance (DEMR) imaging is a technique whereby non-viable cardiac tissue appears with increased signal intensity. Radiologists typically acquire these images in conjunction with other functional modalities (e.g., MR Cine), and use domain knowledge and experience to isolate the non-viable tissues. In this paper, we present a technique for automatically segmenting these tissues given the delineation of myocardial borders in the DEMR and in the End-systolic and End-diastolic MR Cine images. Briefly, we obtain a set of segmentations furnished by an expert and employ an artificial intelligence technique, Support Vector Machines (SVMs), to "learn" the segmentations based on features culled from the images. Using those features we then allow the SVM to predict the segmentations the expert would provide on previously unseen images.

The Value of 5-Aminolevulinic Acid in Low-grade Gliomas and High-grade Gliomas Lacking Glioblastoma Imaging Features: An Analysis Based on Fluorescence, Magnetic Resonance Imaging, 18F-Fluoroethyl Tyrosine Positron Emission Tomography, and Tumor Molecular Factors

PubMed Central

Jaber, Mohammed; Wölfer, Johannes; Ewelt, Christian; Holling, Markus; Hasselblatt, Martin; Niederstadt, Thomas; Zoubi, Tarek; Weckesser, Matthias

2015-01-01

BACKGROUND: Approximately 20% of grade II and most grade III gliomas fluoresce after 5-aminolevulinic acid (5-ALA) application. Conversely, approximately 30% of nonenhancing gliomas are actually high grade. OBJECTIVE: The aim of this study was to identify preoperative factors (ie, age, enhancement, 18F-fluoroethyl tyrosine positron emission tomography [18F-FET PET] uptake ratios) for predicting fluorescence in gliomas without typical glioblastomas imaging features and to determine whether fluorescence will allow prediction of tumor grade or molecular characteristics. METHODS: Patients harboring gliomas without typical glioblastoma imaging features were given 5-ALA. Fluorescence was recorded intraoperatively, and biopsy specimens collected from fluorescing tissue. World Health Organization (WHO) grade, Ki-67/MIB-1 index, IDH1 (R132H) mutation status, O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and 1p/19q co-deletion status were assessed. Predictive factors for fluorescence were derived from preoperative magnetic resonance imaging and 18F-FET PET. Classification and regression tree analysis and receiver-operating-characteristic curves were generated for defining predictors. RESULTS: Of 166 tumors, 82 were diagnosed as WHO grade II, 76 as grade III, and 8 as glioblastomas grade IV. Contrast enhancement, tumor volume, and 18F-FET PET uptake ratio >1.85 predicted fluorescence. Fluorescence correlated with WHO grade (P < .001) and Ki-67/MIB-1 index (P < .001), but not with MGMT promoter methylation status, IDH1 mutation status, or 1p19q co-deletion status. The Ki-67/MIB-1 index in fluorescing grade III gliomas was higher than in nonfluorescing tumors, whereas in fluorescing and nonfluorescing grade II tumors, no differences were noted. CONCLUSION: Age, tumor volume, and 18F-FET PET uptake are factors predicting 5-ALA-induced fluorescence in gliomas without typical glioblastoma imaging features. Fluorescence was associated with an increased Ki-67/MIB-1 index and high-grade pathology. Whether fluorescence in grade II gliomas identifies a subtype with worse prognosis remains to be determined. ABBREVIATIONS: 5-ALA, 5-aminolevulinic acid CRT, classification and regression tree 18F-FET PET, 18F-fluoroethyl tyrosine positron emission tomography FLAIR, fluid-attenuated inversion recovery GBM, glioblastoma multiforme O6-MGMT, methylguanine DNA methyltransferase ROC, receiver-operating characteristic SUV, standardized uptake value WHO, World Health Organization PMID:26366972

Morphological analysis of oligomeric vs. fibrillar forms of α-synuclein aggregates with super-resolution BALM imaging

NASA Astrophysics Data System (ADS)

Huh, Hyun; Lee, Jinwoo; Kim, Hyung Jun; Hohng, Sungchul; Kim, Seong Keun

2017-12-01

Application of BALM (binding activated localization microcopy) was shown to allow facile imaging of amyloid fibrils with a typical diameter of ∼14 nm FWHM. We also observed a twisted ribbon-like substructure of mutant amyloid fibrils and even what appear to be toxic amyloid oligomers with their characteristic morphological features consistent with TEM images. Use of an easily available staining dye in this method greatly enhances the prospect of addressing amyloid-related diseases in their diagnosis and drug tests by allowing facile in situ and in vivo detection by optical imaging.

A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images.

PubMed

Acharya, U Rajendra; Bhat, Shreya; Koh, Joel E W; Bhandary, Sulatha V; Adeli, Hojjat

2017-09-01

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system. Copyright © 2017 Elsevier Ltd. All rights reserved.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

PubMed

Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

2015-06-01

Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for detecting the laminae and facet joints in ultrasound images has been proposed. The system has the potential to assist the anesthesiologists in quickly finding the target plane for epidural steroid injections and facet joint injections.

Texture Classification by Texton: Statistical versus Binary

PubMed Central

Guo, Zhenhua; Zhang, Zhongcheng; Li, Xiu; Li, Qin; You, Jane

2014-01-01

Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical_MR8), image patch (Statistical_Joint) and locally invariant fractal (Statistical_Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary_MR8, Binary_Joint, and Binary_Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor. PMID:24520346

Adamantinoma with plasmacytoid features: expanding the spectrum of a diagnostically challenging entity.

PubMed

Walters, Matthew P; Baynes, Keith; Carrera, Guillermo F; King, David M; Wang, Dian; Charlson, John; Zambrano, Eduardo

2011-10-01

Adamantinoma is a rare neoplasm that characteristically involves the tibia. In many instances, typical location within the tibia, very slow course, and a typical radiographic appearance can strongly suggest the correct diagnosis. We present a case that has both unusual radiographic findings and uncharacteristic histology. In this case, radiologic imaging showed a poorly defined lytic lesion within the distal, lateral tibia extending to the joint with central necrosis, overlying periosteal reaction and possible tumor spread into soft tissue. The histology of this lesion showed pronounced vascularity and surrounding large neoplastic cells with plasmacytoid morphology. The combination of these features led to an initial misdiagnosis as metastatic carcinoma from unknown primary. Copyright © 2011 Elsevier Inc. All rights reserved.

Research on application of LADAR in ground vehicle recognition

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Shen, Zhuoxun

2009-11-01

For the requirement of many practical applications in the field of military, the research of 3D target recognition is active. The representation that captures the salient attributes of a 3D target independent of the viewing angle will be especially useful to the automatic 3D target recognition system. This paper presents a new approach of image generation based on Laser Detection and Ranging (LADAR) data. Range image of target is obtained by transformation of point cloud. In order to extract features of different ground vehicle targets and to recognize targets, zernike moment properties of typical ground vehicle targets are researched in this paper. A technique of support vector machine is applied to the classification and recognition of target. The new method of image generation and feature representation has been applied to the outdoor experiments. Through outdoor experiments, it can be proven that the method of image generation is stability, the moments are effective to be used as features for recognition, and the LADAR can be applied to the field of 3D target recognition.

Bi-fluorescence imaging for estimating accurately the nuclear condition of Rhizoctonia spp.

USDA-ARS?s Scientific Manuscript database

In the absence of perfect state, the number of nuclei in their vegetative hyphae is one of the anamorphic features that separate Rhizoctonia solani from other Rhizoctonia-like fungi. Anamorphs of Rhizoctonia solani are typically multinucleate while the other Rhizoctonia species are binucleate. Howev...

A typical band-shaped calcific keratopathy with keratocyte changes.

PubMed Central

Dark, A J; Proctor, J

1982-01-01

The clinical features of a patient with atypical band keratopathy are described. Histochemical and electron probe analyses indicate that the granular deposits in Bowman's layer contain calcium and phosphate. An unusual feature in this patient was the presence of severe keratocyte degeneration; its possible role in the pathogenesis of this condition is discussed. Exfoliation of the calcified Bowman's layer appears to have been the basis for severe attacks of recurrent ocular pain. Images PMID:7074004

The molecular mechanisms on glomangiopericytoma invasion

PubMed Central

2013-01-01

Purpose To observed the imaging and pathological features of the glomangiopericytoma. Experimental design In this paper we report a typical case of glomangiopericytoma arising in the skull base area and summarize the clinical manifestations, imaging and pathological features of such diseases. Results Immunohistochemical staining confirmed the tumor cells were strongly positive to Vim, SMA, MSA and negative to CD31, CD34. Partial cells were positive to FVIII. The imaging can’t confirm the diagnosis but indicate the the tumor has intact envelope.The cells in the tumor envelope is positive to Vim and negative SMA and FVIII. These findings were compatible with glomangiopericytoma and the cells in the tumor envelope is not glomangiopericytoma cells. Conclusion In view of the clinical and pathological features of the glomangiopericytoma, we believe that the surgery is the best treatment so far and the tumor can be resected completely. The above results can be preliminary reason to explain the low recurrence of such diseases. PMID:24074285

An Overview of data science uses in bioimage informatics.

PubMed

Chessel, Anatole

2017-02-15

This review aims at providing a practical overview of the use of statistical features and associated data science methods in bioimage informatics. To achieve a quantitative link between images and biological concepts, one typically replaces an object coming from an image (a segmented cell or intracellular object, a pattern of expression or localisation, even a whole image) by a vector of numbers. They range from carefully crafted biologically relevant measurements to features learnt through deep neural networks. This replacement allows for the use of practical algorithms for visualisation, comparison and inference, such as the ones from machine learning or multivariate statistics. While originating mainly, for biology, in high content screening, those methods are integral to the use of data science for the quantitative analysis of microscopy images to gain biological insight, and they are sure to gather more interest as the need to make sense of the increasing amount of acquired imaging data grows more pressing. Copyright © 2017 Elsevier Inc. All rights reserved.

Adaptive and robust statistical methods for processing near-field scanning microwave microscopy images.

PubMed

Coakley, K J; Imtiaz, A; Wallis, T M; Weber, J C; Berweger, S; Kabos, P

2015-03-01

Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave |S11| images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical. Published by Elsevier B.V.

Medusae Fossae

NASA Technical Reports Server (NTRS)

2002-01-01

[figure removed for brevity, see original site] (Released 31 July 2002) This image crosses the equator at about 155 W longitude and shows a sample of the middle member of the Medusae Fossae formation. The layers exposed in the southeast-facing scarp suggest that there is a fairly competent unit underlying the mesa in the center of the image. Dust-avalanches are apparent in the crater depression near the middle of the image. The mesa of Medusae Fossae material has the geomorphic signatures that are typical of the formation elsewhere on Mars, but the surface is probably heavily mantled with fine dust, masking the small-scale character of the unit. The close proximity of the Medusae Fossae unit to the Tharsis region may suggest that it is an ignimbrite or volcanic airfall deposit, but it's eroded character hasn't preserved the primary depositional features that would give away the secrets of formation. One of the most interesting feature in the image is the high-standing knob at the base of the scarp in the lower portion of the image. This knob or butte is high standing because it is composed of material that is not as easily eroded as the rest of the unit. There are a number of possible explanations for this feature, including volcano, inverted crater, or some localized process that caused once friable material to become cemented. Another interesting set of features are the long troughs on the slope in the lower portion of the image. The fact that the features keep the same width for the entire length suggests that these are not simple landslides.

Neurocysticercosis in Wisconsin: 3 cases and a review of the literature.

PubMed

Naddaf, Elie; Seeger, Susanne K; Stafstrom, Carl E

2014-04-01

Neurocysticercosis is the most common parasitic infection of the brain. Endemic in many regions of the world, neurocysticercosis is now showing up in nonendemic areas such as Wisconsin. We present 3 patients that illustrate features typical for neurocysticercosis in anon-endemic area, including immigrant/travel status, presentation with focal seizures, classic magnetic resonance imaging features of single enhancing lesions, and good response to treatment with anticonvulsants, anti-inflammatory agents, and cysticidal drugs. It behooves physicians involved in the care of at-risk populations to be aware of the clinical features, radiographic signs, diagnostic tests, and general principles for treating neurocysticercosis.

[Imaging of the elbow joint with focus MRI. Part 2: muscles, nerves and synovial membranes].

PubMed

Rehm, J; Zeifang, F; Weber, M-A

2014-03-01

This review article discusses the magnetic resonance imaging (MRI) features and pathological changes of muscles, nerves and the synovial lining of the elbow joint. Typical imaging findings are illustrated and discussed. In addition, the cross-sectional anatomy and anatomical variants, such as accessory muscles and plicae are discussed. Injuries of the muscles surrounding the elbow joint, as well as chronic irritation are particularly common in athletes. Morphological changes in MRI, for example tennis or golfer's elbow are typical and often groundbreaking. By adapting the examination sequences, imaging planes and slices, complete and incomplete tendon ruptures can be reliably diagnosed. Although the clinical and electrophysiological examinations form the basis for the diagnosis of peripheral neuropathies, MRI provides useful additional information about the precise localization due to its high resolution and good soft tissue contrast and helps to rule out differential diagnoses. Synovial diseases, such as inflammatory arthritis, proliferative diseases and also impinging plicae must be considered in the MRI diagnostics of the elbow joint.

Differentiation between benign and malignant palatal tumors using conventional MRI: a retrospective analysis of 130 cases.

PubMed

Zheng, Yingyan; Xiao, Zebin; Zhang, Hua; She, Dejun; Lin, Xuehua; Lin, Yu; Cao, Dairong

2018-04-01

To evaluate the discriminative value of conventional magnetic resonance imaging between benign and malignant palatal tumors. Conventional magnetic resonance imaging features of 130 patients with palatal tumors confirmed by histopathologic examination were retrospectively reviewed. Clinical data and imaging findings were assessed between benign and malignant tumors and between benign and low-grade malignant salivary gland tumors. The variables that were significant in differentiating benign from malignant lesions were further identified using logistic regression analysis. Moreover, imaging features of each common palatal histologic entity were statistically analyzed with the rest of the tumors to define their typical imaging features. Older age, partially defined and ill-defined margins, and absence of a capsule were highly suggestive of malignant palatal tumors, especially ill-defined margins (β = 6.400). The precision in determining malignant palatal tumors achieved a sensitivity of 92.8% and a specificity of 85.6%. In addition, irregular shape, ill-defined margins, lack of a capsule, perineural spread, and invasion of surrounding structures were more often associated with low-grade malignant salivary gland tumors. Conventional magnetic resonance imaging is useful for differentiating benign from malignant palatal tumors as well as benign salivary gland tumors from low-grade salivary gland malignancies. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Weakly supervised visual dictionary learning by harnessing image attributes.

PubMed

Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

2014-12-01

Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

Temporal assessment of radiomic features on clinical mammography in a high-risk population

NASA Astrophysics Data System (ADS)

Mendel, Kayla R.; Li, Hui; Lan, Li; Chan, Chun-Wai; King, Lauren M.; Tayob, Nabihah; Whitman, Gary; El-Zein, Randa; Bedrosian, Isabelle; Giger, Maryellen L.

2018-02-01

Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment, diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects (23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis, the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance evaluation served well in our investigation on the role of temporal radiomics in risk assessment.

Crossed asymmetry in Russell-Silver syndrome.

PubMed Central

Qazi, Q H; Kassner, E G; Ganapathy, C

1977-01-01

Since the initial report by Silver et al (1953), more than 50 examples of the Russell-Silver syndrome have been reported. Unilateral congenital asymmetry of the extremities has been considered one of the major features of this disorder (Silver, 1964). We recently observed a child with otherwise typical features of the Russell-Silver syndrome who had enlargement of the right hand and of the left lower extremity. We know of no other recorded example of crossed asymmetry in this clinical entity. Images PMID:839508

Wavelength feature mapping as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system

NASA Astrophysics Data System (ADS)

van der Meer, Freek; Kopačková, Veronika; Koucká, Lucie; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Bakker, Wim H.

2018-02-01

The final product of a geologic remote sensing data analysis using multi spectral and hyperspectral images is a mineral (abundance) map. Multispectral data, such as ASTER, Landsat, SPOT, Sentinel-2, typically allow to determine qualitative estimates of what minerals are in a pixel, while hyperspectral data allow to quantify this. As input to most image classification or spectral processing approach, endmembers are required. An alternative approach to classification is to derive absorption feature characteristics such as the wavelength position of the deepest absorption, depth of the absorption and symmetry of the absorption feature from hyperspectral data. Two approaches are presented, tested and compared in this paper: the 'Wavelength Mapper' and the 'QuanTools'. Although these algorithms use a different mathematical solution to derive absorption feature wavelength and depth, and use different image post-processing, the results are consistent, comparable and reproducible. The wavelength images can be directly linked to mineral type and abundance, but more importantly also to mineral chemical composition and subtle changes thereof. This in turn allows to interpret hyperspectral data in terms of mineral chemistry changes which is a proxy to pressure-temperature of formation of minerals. We show the case of the Rodalquilar epithermal system of the southern Spanish Gabo de Gata volcanic area using HyMAP airborne hyperspectral images.

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

PubMed

Bühnemann, Claudia; Li, Simon; Yu, Haiyue; Branford White, Harriet; Schäfer, Karl L; Llombart-Bosch, Antonio; Machado, Isidro; Picci, Piero; Hogendoorn, Pancras C W; Athanasou, Nicholas A; Noble, J Alison; Hassan, A Bassim

2014-01-01

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.

[Road Extraction in Remote Sensing Images Based on Spectral and Edge Analysis].

PubMed

Zhao, Wen-zhi; Luo, Li-qun; Guo, Zhou; Yue, Jun; Yu, Xue-ying; Liu, Hui; Wei, Jing

2015-10-01

Roads are typically man-made objects in urban areas. Road extraction from high-resolution images has important applications for urban planning and transportation development. However, due to the confusion of spectral characteristic, it is difficult to distinguish roads from other objects by merely using traditional classification methods that mainly depend on spectral information. Edge is an important feature for the identification of linear objects (e. g. , roads). The distribution patterns of edges vary greatly among different objects. It is crucial to merge edge statistical information into spectral ones. In this study, a new method that combines spectral information and edge statistical features has been proposed. First, edge detection is conducted by using self-adaptive mean-shift algorithm on the panchromatic band, which can greatly reduce pseudo-edges and noise effects. Then, edge statistical features are obtained from the edge statistical model, which measures the length and angle distribution of edges. Finally, by integrating the spectral and edge statistical features, SVM algorithm is used to classify the image and roads are ultimately extracted. A series of experiments are conducted and the results show that the overall accuracy of proposed method is 93% comparing with only 78% overall accuracy of the traditional. The results demonstrate that the proposed method is efficient and valuable for road extraction, especially on high-resolution images.

Rapid calibrated high-resolution hyperspectral imaging using tunable laser source

NASA Astrophysics Data System (ADS)

Nguyen, Lam K.; Margalith, Eli

2009-05-01

We present a novel hyperspectral imaging technique based on tunable laser technology. By replacing the broadband source and tunable filters of a typical NIR imaging instrument, several advantages are realized, including: high spectral resolution, highly variable field-of-views, fast scan-rates, high signal-to-noise ratio, and the ability to use optical fiber for efficient and flexible sample illumination. With this technique, high-resolution, calibrated hyperspectral images over the NIR range can be acquired in seconds. The performance of system features will be demonstrated on two example applications: detecting melamine contamination in wheat gluten and separating bovine protein from wheat protein in cattle feed.

Neuronal Substrates of Relapse to Cocaine-Seeking Behavior: Role of Prefrontal Cortex

ERIC Educational Resources Information Center

Rebec, George V.; Sun, WenLin

2005-01-01

The return to drug seeking, even after prolonged periods of abstinence, is a defining feature of cocaine addiction. The neural circuitry underlying relapse has been identified in neuropharmacological studies of experimental animals, typically rats, and supported in brain imaging studies of human addicts. Although the nucleus accumbens (NAcc),…

BP Piscium: its flaring disc imaged with SPHERE/ZIMPOL★

NASA Astrophysics Data System (ADS)

de Boer, J.; Girard, J. H.; Canovas, H.; Min, M.; Sitko, M.; Ginski, C.; Jeffers, S. V.; Mawet, D.; Milli, J.; Rodenhuis, M.; Snik, F.; Keller, C. U.

2017-03-01

Whether BP Piscium (BP Psc) is either a pre-main sequence T Tauri star at d ≈ 80 pc, or a post-main sequence G giant at d ≈ 300 pc is still not clear. As a first-ascent giant, it is the first to be observed with a molecular and dust disc. Alternatively, BP Psc would be among the nearest T Tauri stars with a protoplanetary disc (PPD). We investigate whether the disc geometry resembles typical PPDs, by comparing polarimetric images with radiative transfer models. Our Very Large Telescope/Spectro-Polarimetric High-contrast Exoplanet REsearch (SPHERE)/Zurich IMaging Polarimeter (ZIMPOL) observations allow us to perform polarimetric differential imaging, reference star differential imaging, and Richardson-Lucy deconvolution. We present the first visible light polarization and intensity images of the disc of BP Psc. Our deconvolution confirms the disc shape as detected before, mainly showing the southern side of the disc. In polarized intensity the disc is imaged at larger detail and also shows the northern side, giving it the typical shape of high-inclination flared discs. We explain the observed disc features by retrieving the large-scale geometry with MCMAX radiative transfer modelling, which yields a strongly flared model, atypical for discs of T Tauri stars.

Parahippocampal epilepsy with subtle dysplasia: A cause of "imaging negative" partial epilepsy.

PubMed

Pillay, Neelan; Fabinyi, Gavin C A; Myles, Terry S; Fitt, Gregory J; Berkovic, Samuel F; Jackson, Graeme D

2009-12-01

Lesion-negative refractory partial epilepsy is a major challenge in the assessment of patients for potential surgery. Finding a potential epileptogenic lesion simplifies assessment and is associated with good outcome. Here we describe imaging features of subtle parahippocampal dysplasia in five cases that were initially assessed as having imaging-negative frontal or temporal lobe epilepsy. We analyzed the clinical and imaging features of five patients with seizures from the parahippocampal region. Five patients had subtle but distinctive magnetic resonance imaging (MRI) abnormalities in the parahippocampal gyrus. This was a unilateral signal abnormality in the parahippocampal white matter extending into gray matter on heavily T(1)- and T(2)-weighted images with relative preservation of the gray-white matter boundary on T(1)-weighted volume sequences. Only one of these patients had typical electroclinical unilateral temporal lobe epilepsy (TLE); one mimicked frontal lobe epilepsy, two showed bitemporal seizures, and one had unlocalized partial seizures. All have had surgery; four are seizure-free (one has occasional auras only, follow-up 6 months to 10 years), and one has a >50% seizure reduction. Histopathologic evaluation suggested dysplastic features in the surgical specimens in all. In patients with lesion-negative partial epilepsy with frontal or temporal semiology, or in cases with apparent bitemporal seizures, subtle parahippocampal abnormalities should be carefully excluded. Recognizing the MRI findings of an abnormal parahippocampal gyrus can lead to successful surgery without invasive monitoring, despite apparently incongruent electroclinical features.

Tracing the beginning of crystallization of amorphous forsterite thin films using AFM and IR spectroscopy

NASA Astrophysics Data System (ADS)

Oehm, B.; Burchard, M.; Lattard, D.; Dohmen, R.; Chakraborty, S.

2009-12-01

Observations of accretion disks of Young Stellar Objects revealed dust of crystalline Mg-silicates, in particular of forsterite, which is assumed to result from high temperature annealing of amorphous cosmic dust particles. We are performing annealing experiments to obtain kinetic parameters of the crystallization that are necessary for the numerical modeling of accretion disks. We use thin films obtained by Pulsed Laser Deposition (PLD) on Si (111) wafers. The thin films are completely amorphous, chemically homogeneous (on the Mg2SiO4 composition) and with a continuous and flat surface. They are annealed for 1 to 260 h at 1073K in a vertical furnace and drop-quenched. To monitor the progress of crystallization, the samples are characterized by AFM and SEM imaging and IR spectroscopy. After 2.5 h of annealing AFM images reveal elliptical features, below 1 µm in diameter, with a central elevation and surrounded by a lowering of the surface which indicate material transport within the elliptical domains. These elliptical features most probably represent early nucleation sites in an amorphous matrix. The IR spectra still show the broad bands of Si-O stretching modes typical of amorphous silica without clear evidence for crystalline forsterite. After 6 h of annealing, AFM and SEM images show circular and square features both with a central elevation in the range of 80 to 120 nm. IR spectra show a few weak bands that can be assigned to crystalline forsterite (bending and stretching of tetrahedra). After 10 h of annealing planar faces appear in the former pyramidal features and the surrounding matrix evolves into domains with spherolitic appearance. IR spectra of these samples display typical bands of crystalline forsterite. With increasing annealing time AFM images picture the further growth of the planar faces towards idiomorphic crystals. SEM imaging shows surface roughening with increasing annealing time. The quantitative evaluation of the surface roughness of AFM images point to three evolutionary stages during annealing. The quantitative evaluation of IR spectra reveals that the forsterite bands continuously grow with increasing annealing time up to 64 h but that no significant change appears for longer run durations. AFM imaging proves to be a powerful tool to detect the very first signs of crystallization and to trace its further evolution.

Picosecond imaging of inertial confinement fusion plasmas using electron pulse-dilation

NASA Astrophysics Data System (ADS)

Hilsabeck, T. J.; Nagel, S. R.; Hares, J. D.; Kilkenny, J. D.; Bell, P. M.; Bradley, D. K.; Dymoke-Bradshaw, A. K. L.; Piston, K.; Chung, T. M.

2017-02-01

Laser driven inertial confinement fusion (ICF) plasmas typically have burn durations on the order of 100 ps. Time resolved imaging of the x-ray self emission during the hot spot formation is an important diagnostic tool which gives information on implosion symmetry, transient features and stagnation time. Traditional x-ray gated imagers for ICF use microchannel plate detectors to obtain gate widths of 40-100 ps. The development of electron pulse-dilation imaging has enabled a 10X improvement in temporal resolution over legacy instruments. In this technique, the incoming x-ray image is converted to electrons at a photocathode. The electrons are accelerated with a time-varying potential that leads to temporal expansion as the electron signal transits the tube. This expanded signal is recorded with a gated detector and the effective temporal resolution of the composite system can be as low as several picoseconds. An instrument based on this principle, known as the Dilation X-ray Imager (DIXI) has been constructed and fielded at the National Ignition Facility. Design features and experimental results from DIXI will be presented.

Initial Observations of Lunar Impact Melts and Ejecta Flows with the Mini-RF Radar

NASA Technical Reports Server (NTRS)

Carter, Lynn M.; Neish, Catherine D.; Bussey, D. B. J.; Spudis, Paul D.; Patterson, G. Wesley; Cahill, Joshua T.; Raney, R. Keith

2011-01-01

The Mini-RF radar on the Lunar Reconnaissance Orbiter's spacecraft has revealed a great variety of crater ejecta flow and impact melt deposits, some of which were not observed in prior radar imaging. The craters Tycho and Glushko have long melt flows that exhibit variations in radar backscatter and circular polarization ratio along the flow. Comparison with optical imaging reveals that these changes are caused by features commonly seen in terrestrial lava flows, such as rafted plates, pressure ridges, and ponding. Small (less than 20 km) sized craters also show a large variety of features, including melt flows and ponds. Two craters have flow features that may be ejecta flows caused by entrained debris flowing across the surface rather than by melted rock. The circular polarization ratios (CPRs) of the impact melt flows are typically very high; even ponded areas have CPR values between 0.7-1.0. This high CPR suggests that deposits that appear smooth in optical imagery may be rough at centimeter- and decimeter- scales. In some places, ponds and flows are visible with no easily discernable source crater. These melt deposits may have come from oblique impacts that are capable of ejecting melted material farther downrange. They may also be associated with older, nearby craters that no longer have a radar-bright proximal ejecta blanket. The observed morphology of the lunar crater flows has implications for similar features observed on Venus. In particular, changes in backscatter along many of the ejecta flows are probably caused by features typical of lava flows.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

PubMed

Yoo, Youngjin; Tang, Lisa Y W; Brosch, Tom; Li, David K B; Kolind, Shannon; Vavasour, Irene; Rauscher, Alexander; MacKay, Alex L; Traboulsee, Anthony; Tam, Roger C

2018-01-01

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t -test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Three-dimensional face model reproduction method using multiview images

NASA Astrophysics Data System (ADS)

Nagashima, Yoshio; Agawa, Hiroshi; Kishino, Fumio

1991-11-01

This paper describes a method of reproducing three-dimensional face models using multi-view images for a virtual space teleconferencing system that achieves a realistic visual presence for teleconferencing. The goal of this research, as an integral component of a virtual space teleconferencing system, is to generate a three-dimensional face model from facial images, synthesize images of the model virtually viewed from different angles, and with natural shadow to suit the lighting conditions of the virtual space. The proposed method is as follows: first, front and side view images of the human face are taken by TV cameras. The 3D data of facial feature points are obtained from front- and side-views by an image processing technique based on the color, shape, and correlation of face components. Using these 3D data, the prepared base face models, representing typical Japanese male and female faces, are modified to approximate the input facial image. The personal face model, representing the individual character, is then reproduced. Next, an oblique view image is taken by TV camera. The feature points of the oblique view image are extracted using the same image processing technique. A more precise personal model is reproduced by fitting the boundary of the personal face model to the boundary of the oblique view image. The modified boundary of the personal face model is determined by using face direction, namely rotation angle, which is detected based on the extracted feature points. After the 3D model is established, the new images are synthesized by mapping facial texture onto the model.

Comparing object recognition from binary and bipolar edge images for visual prostheses.

PubMed

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2016-11-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition.

Skipping the real world: Classification of PolSAR images without explicit feature extraction

NASA Astrophysics Data System (ADS)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

PubMed

Zhou, Yongxia; Yu, Fang; Duong, Timothy

2014-01-01

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.

Insights into multimodal imaging classification of ADHD

PubMed Central

Colby, John B.; Rudie, Jeffrey D.; Brown, Jesse A.; Douglas, Pamela K.; Cohen, Mark S.; Shehzad, Zarrar

2012-01-01

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD. PMID:22912605

Elevated thyroid peroxidase antibodies with encephalopathy in MELAS syndrome.

PubMed

Chan, Derrick W S; Lim, C C Tchoyoson; Tay, Stacey K H; Choong, Chew-Thye; Phuah, Huan Kee

2007-06-01

Both the syndrome of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS syndrome) and Hashimoto's encephalopathy can present with nonspecific encephalopathy. Hashimoto's encephalopathy is an association of steroid-responsive encephalopathy with elevated thyroid peroxidase antibodies. Steroid-responsive encephalopathy, however, is not characteristic of the MELAS syndrome, which typically presents with stroke-like episodes and lactic acidosis in cerebrospinal fluid and blood. Here, a patient is described with goiter, recurrent encephalopathy and elevated thyroid peroxidase antibodies who apparently responded to steroid therapy; however, magnetic resonance imaging was atypical for Hashimoto's encephalopathy, and she was diagnosed with MELAS syndrome. This syndrome can present with apparent steroid-responsive encephalopathy and elevated thyroid peroxidase antibodies, mimicking Hashimoto's encephalopathy, and should be suspected if lactic acidosis is present and typical features are detected on magnetic resonance imaging.

SU-E-I-91: Quantitative Assessment of Early Hepatocellular Carcinoma and Cavernous Hemangioma of Live Using In-Line Phase-Contrast X-Ray Imaging

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

Duan, J

Purpose: To investigate the potential utility of in-line phase-contrast imaging (ILPCI) technique with synchrotron radiation in detecting early hepatocellular carcinoma and cavernous hemangioma of live using in vitro model system. Methods: Without contrast agents, three typical early hepatocellular carcinoma specimens and three typical cavernous hemangioma of live specimens were imaged using ILPCI. To quantitatively discriminate early hepatocellular carcinoma tissues and cavernous hemangioma tissues, the projection images texture feature based on gray level co-occurrence matrix (GLCM) were extracted. The texture parameters of energy, inertia, entropy, correlation, sum average, sum entropy, difference average, difference entropy and inverse difference moment, were obtained respectively.more » Results: In the ILPCI planar images of early hepatocellular carcinoma specimens, vessel trees were clearly visualized on the micrometer scale. Obvious distortion deformation was presented, and the vessel mostly appeared as a ‘dry stick’. Liver textures appeared not regularly. In the ILPCI planar images of cavernous hemangioma of live specimens, typical vessels had not been found compared with the early hepatocellular carcinoma planar images. The planar images of cavernous hemangioma of live specimens clearly displayed the dilated hepatic sinusoids with the diameter of less than 100 microns, but all of them were overlapped with each other. The texture parameters of energy, inertia, entropy, correlation, sum average, sum entropy, and difference average, showed a statistically significant between the two types specimens image (P<0.01), except the texture parameters of difference entropy and inverse difference moment(P>0.01). Conclusion: The results indicate that there are obvious changes in morphological levels including vessel structures and liver textures. The study proves that this imaging technique has a potential value in evaluating early hepatocellular carcinoma and cavernous hemangioma of live.« less

The imaging features of the meniscal roots on isotropic 3D MRI in young asymptomatic volunteers.

PubMed

Wang, Ping; Zhang, Cheng-Zhou; Zhang, Di; Liu, Quan-Yuan; Zhong, Xiao-Fei; Yin, Zhi-Jie; Wang, Bin

2018-05-01

This study aimed to describe clearly the normal imaging features of the meniscal roots on the magnetic resonance imaging (MRI) with a 3-dimensional (3D) proton density-weighted (PDW) sequence at 3T. A total of 60 knees of 31 young asymptomatic volunteers were examined using a 3D MRI. The insertion patterns, constitution patterns, and MR signals of the meniscal roots were recorded. The anterior root of the medial meniscus (ARMM), the anterior root of the lateral meniscus (ARLM), and the posterior root of the medial meniscus (PRMM) had 1 insertion site, whereas the posterior root of the lateral meniscus (PRLM) can be divided into major and minor insertion sites. The ARLM and the PRMM usually consisted of multiple fiber bundles (≥3), whereas the ARMM and the PRLM often consisted of a single fiber bundle. The ARMM and the PRLM usually appeared as hypointense, whereas the ARLM and the PRMM typically exhibited mixed signals. The meniscal roots can be complex and diverse, and certain characteristics of them were observed on 3D MRI. Understanding the normal imaging features of the meniscal roots is extremely beneficial for further diagnosis of root tears.

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Imaging of cerebellopontine angle lesions: an update. Part 1: enhancing extra-axial lesions.

PubMed

Bonneville, Fabrice; Savatovsky, Julien; Chiras, Jacques

2007-10-01

Computed tomography (CT) and magnetic resonance (MR) imaging reliably demonstrate typical features of vestibular schwannomas or meningiomas in the vast majority of mass lesions in the cerebellopontine angle (CPA). However, a large variety of unusual lesions can also be encountered in the CPA. Covering the entire spectrum of lesions potentially found in the CPA, these articles explain the pertinent neuroimaging features that radiologists need to know to make clinically relevant diagnoses in these cases, including data from diffusion and perfusion-weighted imaging or MR spectroscopy, when available. A diagnostic algorithm based on the lesion's site of origin, shape and margins, density, signal intensity and contrast material uptake is also proposed. Part 1 describes the different enhancing extra-axial CPA masses primarily arising from the cerebellopontine cistern and its contents, including vestibular and non-vestibular schwannomas, meningioma, metastasis, aneurysm, tuberculosis and other miscellaneous meningeal lesions.

Nonpuerperal mastitis and subareolar abscess of the breast.

PubMed

Kasales, Claudia J; Han, Bing; Smith, J Stanley; Chetlen, Alison L; Kaneda, Heather J; Shereef, Serene

2014-02-01

The purpose of this article is to show radiologists how to readily recognize nonpuerperal subareolar abscess and its complications in order to help reduce the time to definitive therapy and improve patient care. To achieve this purpose, the various theories of pathogenesis and the associated histopathologic features are reviewed; the typical clinical characteristics are detailed in contrast to those seen in lactational abscess and inflammatory breast cancer; the common imaging findings are described with emphasis on the sonographic features; correlative pathologic findings are presented to reinforce the imaging findings as they pertain to disease origins; and the various treatment options are reviewed. Nonpuerperal subareolar mastitis and abscess is a benign breast entity often associated with prolonged morbidity. Through better understanding of the underlying disease process the imaging, physical, and clinical findings of this rare process can be more readily recognized and treatment options expedited, improving patient care.

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the random sampling method is used to conduct the field investigation, achieve an overall accuracy of 90.31 %, and the Kappa coefficient is 0.88. The classification method based on decision tree threshold values and rule set developed by the repository, outperforms the results obtained from the traditional methodology. Our decision tree repository and rule set based object-oriented classification technique was an effective method for producing comparable and consistency wetlands data set.

Automatic Contour Extraction of Facial Organs for Frontal Facial Images with Various Facial Expressions

NASA Astrophysics Data System (ADS)

Kobayashi, Hiroshi; Suzuki, Seiji; Takahashi, Hisanori; Tange, Akira; Kikuchi, Kohki

This study deals with a method to realize automatic contour extraction of facial features such as eyebrows, eyes and mouth for the time-wise frontal face with various facial expressions. Because Snakes which is one of the most famous methods used to extract contours, has several disadvantages, we propose a new method to overcome these issues. We define the elastic contour model in order to hold the contour shape and then determine the elastic energy acquired by the amount of modification of the elastic contour model. Also we utilize the image energy obtained by brightness differences of the control points on the elastic contour model. Applying the dynamic programming method, we determine the contour position where the total value of the elastic energy and the image energy becomes minimum. Employing 1/30s time-wise facial frontal images changing from neutral to one of six typical facial expressions obtained from 20 subjects, we have estimated our method and find it enables high accuracy automatic contour extraction of facial features.

MDI: integrity index of cytoskeletal fibers observed by AFM

NASA Astrophysics Data System (ADS)

Manghi, Massimo; Bruni, Luca; Croci, Simonetta

2016-06-01

The Modified Directional Index (MDI) is a form factor of the angular spectrum computed from the 2D Fourier transform of an image marking the prevalence of rectilinear features throughout the picture. We study some properties of the index and we apply it to AFM images of cell cytoskeleton regions featuring patterns of rectilinear nearly parallel actin filaments as in the case of microfilaments grouped in bundles. The analysis of AFM images through MDI calculation quantifies the fiber directionality changes which could be related to fiber damages. This parameter is applied to the images of Hs 578Bst cell line, non-tumoral and not immortalized human epithelial cell line, irradiated with X-rays at doses equivalent to typical radiotherapy treatment fractions. In the reported samples, we could conclude that the damages are mainly born to the membrane and not to the cytoskeleton. It could be interesting to test the parameter also using other kinds of chemical or physical agents.

Archaeological Feature Detection from Archive Aerial Photography with a Sfm-Mvs and Image Enhancement Pipeline

NASA Astrophysics Data System (ADS)

Peppa, M. V.; Mills, J. P.; Fieber, K. D.; Haynes, I.; Turner, S.; Turner, A.; Douglas, M.; Bryan, P. G.

2018-05-01

Understanding and protecting cultural heritage involves the detection and long-term documentation of archaeological remains alongside the spatio-temporal analysis of their landscape evolution. Archive aerial photography can illuminate traces of ancient features which typically appear with different brightness values from their surrounding environment, but are not always well defined. This research investigates the implementation of the Structure-from-Motion - Multi-View Stereo image matching approach with an image enhancement algorithm to derive three epochs of orthomosaics and digital surface models from visible and near infrared historic aerial photography. The enhancement algorithm uses decorrelation stretching to improve the contrast of the orthomosaics so as archaeological features are better detected. Results include 2D / 3D locations of detected archaeological traces stored into a geodatabase for further archaeological interpretation and correlation with benchmark observations. The study also discusses the merits and difficulties of the process involved. This research is based on a European-wide project, entitled "Cultural Heritage Through Time", and the case study research was carried out as a component of the project in the UK.

Cranio-orbital primary intraosseous haemangioma.

PubMed

Gupta, T; Rose, G E; Manisali, M; Minhas, P; Uddin, J M; Verity, D H

2013-11-01

Primary intraosseous haemangioma (IOH) is a rare benign neoplasm presenting in the fourth and fifth decades of life. The spine and skull are the most commonly involved, orbital involvement is extremely rare. We describe six patients with cranio-orbital IOH, the largest case series to date. Retrospective review of six patients with histologically confirmed primary IOH involving the orbit. Clinical characteristics, imaging features, approach to management, and histopathological findings are described. Five patients were male with a median age of 56. Pain and diplopia were the most common presenting features. A characteristic 'honeycomb' pattern on CT imaging was demonstrated in three of the cases. Complete surgical excision was performed in all cases with presurgical embolisation carried out in one case. In all the cases, histological studies identified cavernous vascular spaces within the bony tissue. These channels were lined by single layer of cytologically normal endothelial cells. IOCH of the cranio-orbital region is rare; in the absence of typical imaging features, the differential diagnosis includes chondroma, chondrosarcoma, bony metastasis, and lymphoma. Surgical excision may be necessary to exclude more sinister pathology. Intraoperative haemorrhage can be severe and may be reduced by preoperative embolisation.

Uranus' southern circulation revealed by Voyager 2: Unique characteristics

NASA Astrophysics Data System (ADS)

Karkoschka, Erich

2015-04-01

Revised calibration and processing of 1600 images of Uranus by Voyager 2 revealed dozens of discrete features south of -45° latitude, where only a single feature was known from Voyager images and none has been seen since. Tracking of these features over five weeks defined the southern rotational profile of Uranus with high accuracy and no significant gap. The profile has kinks unlike previous profiles and is strongly asymmetric with respect to the northern profile by Sromovsky et al. (Sromovsky, L.A., Fry, P.M., Hammel, H.B., de Pater, I., Rages, K.A. [2012]. Icarus 220, 694-712). The asymmetry is larger than that of all previous data on jovian planets. A spot that included the South Pole off-center rotated with a period of 12.24 h, 2 h outside the range of all previous observations of Uranus. The region between -68° and -59° latitude rotated almost like a solid body, with a shear that was about 30 times smaller than typical shears on Uranus. At lower latitudes, features were sheared into tightly wound spirals as Voyager watched. The zone at -84° latitude was exceptionally bland; reflectivity variations were only 18 ppm, consistent with a signal-to-noise ratio estimated at 55,000. The low noise was achieved by smoothing over dozens of pixels per image and averaging 1600 images. The presented data set in eight filters contains rich information about temporal evolution and spectral characteristics of features on Uranus that will be the basis for further analysis.

Symmetric corticobasal degeneration (S-CBD).

PubMed

Hassan, Anhar; Whitwell, Jennifer L; Boeve, Bradley F; Jack, Clifford R; Parisi, Joseph E; Dickson, Dennis W; Josephs, Keith A

2010-03-01

Corticobasal degeneration (CBD) is a neurodegenerative disease characterized pathologically by neuronal loss, gliosis and tau deposition in neocortex, basal ganglia and brainstem. Typical clinical presentation is known as corticobasal syndrome (CBS) and involves the core features of progressive asymmetric rigidity and apraxia, accompanied by other signs of cortical and extrapyramidal dysfunction. Asymmetry is also emphasized on neuroimaging. To describe a series of cases of CBD with symmetric clinical features and to compare clinical and imaging features of these symmetric CBD cases (S-CBD) to typical cases of CBS with CBD pathology. All cases of pathologically confirmed CBD from the Mayo Clinic Rochester database were identified. Clinical records were reviewed and quantitative volumetric analysis of symmetric atrophy on head MRI using atlas based parcellation was performed. Subjects were classified as S-CBD if no differences had been observed between right- and left-sided cortical or extrapyramidal signs or symptoms. S-CBD cases were compared to 10 randomly selected typical CBS cases. Five cases (2 female) met criteria for S-CBD. None had limb dystonia, myoclonus, apraxia or alien limb phenomena. S-CBD cases had significantly less asymmetric atrophy when compared with CBS cases (p=0.009); they were also younger at onset (median 61 versus 66 years, p<0.05) and death (67 versus 73 years, p<0.05). Family history was present in 40% of S-CBD cases. CBD can have a symmetric presentation, clinically and radiologically, in which typical features of CBS, such as limb apraxia, myoclonus, dystonia and alien limb phenomenon, may be absent. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Design of Content Based Image Retrieval Scheme for Diabetic Retinopathy Images using Harmony Search Algorithm.

PubMed

Sivakamasundari, J; Natarajan, V

2015-01-01

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Automated segmentation of blood vessel is vital for periodic screening and timely diagnosis. An attempt has been made to generate continuous retinal vasculature for the design of Content Based Image Retrieval (CBIR) application. The typical normal and abnormal retinal images are preprocessed to improve the vessel contrast. The blood vessels are segmented using evolutionary based Harmony Search Algorithm (HSA) combined with Otsu Multilevel Thresholding (MLT) method by best objective functions. The segmentation results are validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images of normal and DR affected retina and are analyzed. CBIR in medical image retrieval applications are used to assist physicians in clinical decision-support techniques and research fields. A CBIR system is developed using HSA based Otsu MLT segmentation technique and the features obtained from the segmented images. Similarity matching is carried out between the features of query and database images using Euclidean Distance measure. Similar images are ranked and retrieved. The retrieval performance of CBIR system is evaluated in terms of precision and recall. The CBIR systems developed using HSA based Otsu MLT and conventional Otsu MLT methods are compared. The retrieval performance such as precision and recall are found to be 96% and 58% for CBIR system using HSA based Otsu MLT segmentation. This automated CBIR system could be recommended for use in computer assisted diagnosis for diabetic retinopathy screening.

Imaging of benign and malignant cystic pancreatic lesions and a strategy for follow up

PubMed Central

Bhosale, Priya; Balachandran, Aparna; Tamm, Eric

2010-01-01

Cystic lesions in a variety of organs are being increasingly recognized as an incidental finding on cross-sectional imaging. These lesions can be benign, premalignant or malignant. When these cystic lesions are small it can be difficult to characterize them radiologically. However, with appropriate clinical history and knowledge of typical imaging features of cystic pancreatic lesions this can enable accurate diagnosis and thus guide appropriate treatment. In this review, we provide an overview of the most common types of cystic lesions and their appearance on computer tomography, magnetic resonance imaging and ultrasound. We will also discuss the follow up and management strategies of these cystic lesions. PMID:21160696

Identifying Conventionally Sub-Seismic Faults in Polygonal Fault Systems

NASA Astrophysics Data System (ADS)

Fry, C.; Dix, J.

2017-12-01

Polygonal Fault Systems (PFS) are prevalent in hydrocarbon basins globally and represent potential fluid pathways. However the characterization of these pathways is subject to the limitations of conventional 3D seismic imaging; only capable of resolving features on a decametre scale horizontally and metres scale vertically. While outcrop and core examples can identify smaller features, they are limited by the extent of the exposures. The disparity between these scales can allow for smaller faults to be lost in a resolution gap which could mean potential pathways are left unseen. Here the focus is upon PFS from within the London Clay, a common bedrock that is tunnelled into and bears construction foundations for much of London. It is a continuation of the Ieper Clay where PFS were first identified and is found to approach the seafloor within the Outer Thames Estuary. This allows for the direct analysis of PFS surface expressions, via the use of high resolution 1m bathymetric imaging in combination with high resolution seismic imaging. Through use of these datasets surface expressions of over 1500 faults within the London Clay have been identified, with the smallest fault measuring 12m and the largest at 612m in length. The displacements over these faults established from both bathymetric and seismic imaging ranges from 30cm to a couple of metres, scales that would typically be sub-seismic for conventional basin seismic imaging. The orientations and dimensions of the faults within this network have been directly compared to 3D seismic data of the Ieper Clay from the offshore Dutch sector where it exists approximately 1km below the seafloor. These have typical PFS attributes with lengths of hundreds of metres to kilometres and throws of tens of metres, a magnitude larger than those identified in the Outer Thames Estuary. The similar orientations and polygonal patterns within both locations indicates that the smaller faults exist within typical PFS structure but are sub-seismic in conventional imaging techniques. These unseen faults could create additional unseen pathways that impact construction in London via water ingress and influence fluid migration within hydrocarbon basins.

Mitochondrial Encephalomyopathy With Lactic Acidosis and Stroke-Like Episodes—MELAS Syndrome

PubMed Central

Henry, Caitlin; Patel, Neema; Shaffer, William; Murphy, Lillian; Park, Joe

2017-01-01

Background: Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome is a rare inherited disorder that results in waxing and waning nervous system and muscle dysfunction. MELAS syndrome may overlap with other neurologic disorders but shows distinctive imaging features. Case Report: We present the case of a 28-year-old female with atypical stroke-like symptoms, a strong family history of stroke-like symptoms, and a relapsing-remitting course for several years. We discuss the imaging features distinctive to the case, the mechanism of the disease, typical presentation, imaging diagnosis, and disease management. Conclusion: This case is a classic example of the relapse-remitting MELAS syndrome progression with episodic clinical flares and fluctuating patterns of stroke-like lesions on imaging. MELAS is an important diagnostic consideration when neuroimaging reveals a pattern of disappearing and relapsing cortical brain lesions that may occur in different areas of the brain and are not necessarily limited to discrete vascular territories. Future studies should investigate disease mechanisms at the cellular level and the value of advanced magnetic resonance imaging techniques for a targeted approach to therapy. PMID:29026367

Mitochondrial Encephalomyopathy With Lactic Acidosis and Stroke-Like Episodes-MELAS Syndrome.

PubMed

Henry, Caitlin; Patel, Neema; Shaffer, William; Murphy, Lillian; Park, Joe; Spieler, Bradley

2017-01-01

Mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome is a rare inherited disorder that results in waxing and waning nervous system and muscle dysfunction. MELAS syndrome may overlap with other neurologic disorders but shows distinctive imaging features. We present the case of a 28-year-old female with atypical stroke-like symptoms, a strong family history of stroke-like symptoms, and a relapsing-remitting course for several years. We discuss the imaging features distinctive to the case, the mechanism of the disease, typical presentation, imaging diagnosis, and disease management. This case is a classic example of the relapse-remitting MELAS syndrome progression with episodic clinical flares and fluctuating patterns of stroke-like lesions on imaging. MELAS is an important diagnostic consideration when neuroimaging reveals a pattern of disappearing and relapsing cortical brain lesions that may occur in different areas of the brain and are not necessarily limited to discrete vascular territories. Future studies should investigate disease mechanisms at the cellular level and the value of advanced magnetic resonance imaging techniques for a targeted approach to therapy.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

PubMed Central

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

PubMed

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Anaglyph Image Technology As a Visualization Tool for Teaching Geology of National Parks

NASA Astrophysics Data System (ADS)

Stoffer, P. W.; Phillips, E.; Messina, P.

2003-12-01

Anaglyphic stereo viewing technology emerged in the mid 1800's. Anaglyphs use offset images in contrasting colors (typically red and cyan) that when viewed through color filters produce a three-dimensional (3-D) image. Modern anaglyph image technology has become increasingly easy to use and relatively inexpensive using digital cameras, scanners, color printing, and common image manipulation software. Perhaps the primary drawbacks of anaglyph images include visualization problems with primary colors (such as flowers, bright clothing, or blue sky) and distortion factors in large depth-of-field images. However, anaglyphs are more versatile than polarization techniques since they can be printed, displayed on computer screens (such as on websites), or projected with a single projector (as slides or digital images), and red and cyan viewing glasses cost less than polarization glasses and other 3-D viewing alternatives. Anaglyph images are especially well suited for most natural landscapes, such as views dominated by natural earth tones (grays, browns, greens), and they work well for sepia and black and white images (making the conversion of historic stereo photography into anaglyphs easy). We used a simple stereo camera setup incorporating two digital cameras with a rigid base to photograph landscape features in national parks (including arches, caverns, cactus, forests, and coastlines). We also scanned historic stereographic images. Using common digital image manipulation software we created websites featuring anaglyphs of geologic features from national parks. We used the same images for popular 3-D poster displays at the U.S. Geological Survey Open House 2003 in Menlo Park, CA. Anaglyph photography could easily be used in combined educational outdoor activities and laboratory exercises.

Kinematic measurements of the vocal-fold displacement waveform in typical children and adult populations: quantification of high-speed endoscopic videos.

PubMed

Patel, Rita; Donohue, Kevin D; Unnikrishnan, Harikrishnan; Kryscio, Richard J

2015-04-01

This article presents a quantitative method for assessing instantaneous and average lateral vocal-fold motion from high-speed digital imaging, with a focus on developmental changes in vocal-fold kinematics during childhood. Vocal-fold vibrations were analyzed for 28 children (aged 5-11 years) and 28 adults (aged 21-45 years) without voice disorders. The following kinematic features were analyzed from the vocal-fold displacement waveforms: relative velocity-based features (normalized average and peak opening and closing velocities), relative acceleration-based features (normalized peak opening and closing accelerations), speed quotient, and normalized peak displacement. Children exhibited significantly larger normalized peak displacements, normalized average and peak opening velocities, normalized average and peak closing velocities, peak opening and closing accelerations, and speed quotient compared to adult women. Values of normalized average closing velocity and speed quotient were higher in children compared to adult men. When compared to adult men, developing children typically have higher estimates of kinematic features related to normalized displacement and its derivatives. In most cases, the kinematic features of children are closer to those of adult men than adult women. Even though boys experience greater changes in glottal length and pitch as they mature, results indicate that girls experience greater changes in kinematic features compared to boys.

Using Trained Pixel Classifiers to Select Images of Interest

NASA Technical Reports Server (NTRS)

Mazzoni, D.; Wagstaff, K.; Castano, R.

2004-01-01

We present a machine-learning-based approach to ranking images based on learned priorities. Unlike previous methods for image evaluation, which typically assess the value of each image based on the presence of predetermined specific features, this method involves using two levels of machine-learning classifiers: one level is used to classify each pixel as belonging to one of a group of rather generic classes, and another level is used to rank the images based on these pixel classifications, given some example rankings from a scientist as a guide. Initial results indicate that the technique works well, producing new rankings that match the scientist's rankings significantly better than would be expected by chance. The method is demonstrated for a set of images collected by a Mars field-test rover.

Real-time high dynamic range laser scanning microscopy

NASA Astrophysics Data System (ADS)

Vinegoni, C.; Leon Swisher, C.; Fumene Feruglio, P.; Giedt, R. J.; Rousso, D. L.; Stapleton, S.; Weissleder, R.

2016-04-01

In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging.

AAPM/RSNA physics tutorial for residents: physics of flat-panel fluoroscopy systems: Survey of modern fluoroscopy imaging: flat-panel detectors versus image intensifiers and more.

PubMed

Nickoloff, Edward Lee

2011-01-01

This article reviews the design and operation of both flat-panel detector (FPD) and image intensifier fluoroscopy systems. The different components of each imaging chain and their functions are explained and compared. FPD systems have multiple advantages such as a smaller size, extended dynamic range, no spatial distortion, and greater stability. However, FPD systems typically have the same spatial resolution for all fields of view (FOVs) and are prone to ghosting. Image intensifier systems have better spatial resolution with the use of smaller FOVs (magnification modes) and tend to be less expensive. However, the spatial resolution of image intensifier systems is limited by the television system to which they are coupled. Moreover, image intensifier systems are degraded by glare, vignetting, spatial distortions, and defocusing effects. FPD systems do not have these problems. Some recent innovations to fluoroscopy systems include automated filtration, pulsed fluoroscopy, automatic positioning, dose-area product meters, and improved automatic dose rate control programs. Operator-selectable features may affect both the patient radiation dose and image quality; these selectable features include dose level setting, the FOV employed, fluoroscopic pulse rates, geometric factors, display software settings, and methods to reduce the imaging time. © RSNA, 2011.

Multi-scale Gaussian representation and outline-learning based cell image segmentation.

PubMed

Farhan, Muhammad; Ruusuvuori, Pekka; Emmenlauer, Mario; Rämö, Pauli; Dehio, Christoph; Yli-Harja, Olli

2013-01-01

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

Multi-scale Gaussian representation and outline-learning based cell image segmentation

PubMed Central

2013-01-01

Background High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation. Methods We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information. Results and conclusions We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks. PMID:24267488

The drive for muscle leanness: a complex case with features of muscle dysmorphia and eating disorder not otherwise specified.

PubMed

Cafri, G; Blevins, N; Thompson, J K

2006-12-01

Muscle dysmorphia has been described as a subtype of body dysmorphic disorder in which an individual experiences severe body image disturbance related to muscularity. The current case is of a 20-year-old man who describes a history of muscle dysmorphia in which the nature of the body image concern is related to leanness (i.e., muscularity in the absence of body fat), as opposed to increasing muscle mass, which is how muscle dysmorphia has typically been characterized in the literature. The case illustrates the need to consider this additional facet of body image when diagnosing muscle dysmorphia.

Active Contours Driven by Multi-Feature Gaussian Distribution Fitting Energy with Application to Vessel Segmentation.

PubMed

Wang, Lei; Zhang, Huimao; He, Kan; Chang, Yan; Yang, Xiaodong

2015-01-01

Active contour models are of great importance for image segmentation and can extract smooth and closed boundary contours of the desired objects with promising results. However, they cannot work well in the presence of intensity inhomogeneity. Hence, a novel region-based active contour model is proposed by taking image intensities and 'vesselness values' from local phase-based vesselness enhancement into account simultaneously to define a novel multi-feature Gaussian distribution fitting energy in this paper. This energy is then incorporated into a level set formulation with a regularization term for accurate segmentations. Experimental results based on publicly available STructured Analysis of the Retina (STARE) demonstrate our model is more accurate than some existing typical methods and can successfully segment most small vessels with varying width.

On the use of INS to improve Feature Matching

NASA Astrophysics Data System (ADS)

Masiero, A.; Guarnieri, A.; Vettore, A.; Pirotti, F.

2014-11-01

The continuous technological improvement of mobile devices opens the frontiers of Mobile Mapping systems to very compact systems, i.e. a smartphone or a tablet. This motivates the development of efficient 3D reconstruction techniques based on the sensors typically embedded in such devices, i.e. imaging sensors, GPS and Inertial Navigation System (INS). Such methods usually exploits photogrammetry techniques (structure from motion) to provide an estimation of the geometry of the scene. Actually, 3D reconstruction techniques (e.g. structure from motion) rely on use of features properly matched in different images to compute the 3D positions of objects by means of triangulation. Hence, correct feature matching is of fundamental importance to ensure good quality 3D reconstructions. Matching methods are based on the appearance of features, that can change as a consequence of variations of camera position and orientation, and environment illumination. For this reason, several methods have been developed in recent years in order to provide feature descriptors robust (ideally invariant) to such variations, e.g. Scale-Invariant Feature Transform (SIFT), Affine SIFT, Hessian affine and Harris affine detectors, Maximally Stable Extremal Regions (MSER). This work deals with the integration of information provided by the INS in the feature matching procedure: a previously developed navigation algorithm is used to constantly estimate the device position and orientation. Then, such information is exploited to estimate the transformation of feature regions between two camera views. This allows to compare regions from different images but associated to the same feature as seen by the same point of view, hence significantly easing the comparison of feature characteristics and, consequently, improving matching. SIFT-like descriptors are used in order to ensure good matching results in presence of illumination variations and to compensate the approximations related to the estimation process.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

NASA Astrophysics Data System (ADS)

Proctor, D. D.

2006-07-01

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

Is Trypophobia a Phobia?

PubMed

Can, Wang; Zhuoran, Zhao; Zheng, Jin

2017-04-01

In the past 10 years, thousands of people have claimed to be affected by trypophobia, which is the fear of objects with small holes. Recent research suggests that people do not fear the holes; rather, images of clustered holes, which share basic visual characteristics with venomous organisms, lead to nonconscious fear. In the present study, both self-reported measures and the Preschool Single Category Implicit Association Test were adapted for use with preschoolers to investigate whether discomfort related to trypophobic stimuli was grounded in their visual features or based on a nonconsciously associated fear of venomous animals. The results indicated that trypophobic stimuli were associated with discomfort in children. This discomfort seemed to be related to the typical visual characteristics and pattern properties of trypophobic stimuli rather than to nonconscious associations with venomous animals. The association between trypophobic stimuli and venomous animals vanished when the typical visual characteristics of trypophobic features were removed from colored photos of venomous animals. Thus, the discomfort felt toward trypophobic images might be an instinctive response to their visual characteristics rather than the result of a learned but nonconscious association with venomous animals. Therefore, it is questionable whether it is justified to legitimize trypophobia.

Inflammatory myofibroblastic tumor: an entity of CT and MR imaging to differentiate from malignant tumors of the sinonasal cavity.

PubMed

Yan, Zhongyu; Wang, Yongzhe; Zhang, Zhengyu

2014-01-01

Inflammatory myofibroblastic tumor (IMT) is chronic inflammatory lesions of unknown origins. The preoperative diagnosis for tumors in the sinonasal cavity is difficult to distinguish between IMT and aggressive malignancy in most cases. The purpose of this study was to evaluate the imaging features of IMT distinguishing the 2 types of tumors. Computed tomography and magnetic resonance imaging were identified retrospectively with IMT in 14 cases and with aggressive malignancy in 38 cases in the sinonasal cavity proven by pathology. Imaging findings were evaluated, including the configuration, extent, margin, calcification, bone involvement, T1WI and T2WI signal intensity, and degree of enhancement. There was a significant difference between IMT and aggressive malignancy regarding the configuration, extension, calcification, bone change, signal intensity and homogeneous on T2-weighted imaging, and degree of enhancement (P < 0.05). Inflammatory myofibroblastic tumor and aggressive malignancy have some different imaging features that could be helpful in the differentiation between the lesions. Bone erosion with sclerosis, calcification when present, typically homogenous and never hyperintense of T2 appearance, and mild enhancement played an important role in differentiating sinonasal IMT from malignancies.

Interferometric synthetic aperture radar imagery of the Gulf Stream

NASA Technical Reports Server (NTRS)

Ainsworth, T. L.; Cannella, M. E.; Jansen, R. W.; Chubb, S. R.; Carande, R. E.; Foley, E. W.; Goldstein, R. M.; Valenzuela, G. R.

1993-01-01

The advent of interferometric synthetic aperture radar (INSAR) imagery brought to the ocean remote sensing field techniques used in radio astronomy. Whilst details of the interferometry differ between the two fields, the basic idea is the same: Use the phase information arising from positional differences of the radar receivers and/or transmitters to probe remote structures. The interferometric image is formed from two complex synthetic aperture radar (SAR) images. These two images are of the same area but separated in time. Typically the time between these images is very short -- approximately 50 msec for the L-band AIRSAR (Airborne SAR). During this short period the radar scatterers on the ocean surface do not have time to significantly decorrelate. Hence the two SAR images will have the same amplitude, since both obtain the radar backscatter from essentially the same object. Although the ocean surface structure does not significantly decorrelate in 50 msec, surface features do have time to move. It is precisely the translation of scattering features across the ocean surface which gives rise to phase differences between the two SAR images. This phase difference is directly proportional to the range velocity of surface scatterers. The constant of proportionality is dependent upon the interferometric mode of operation.

Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a Bayesian probabilistic framework

NASA Astrophysics Data System (ADS)

Jain, Ameet K.; Taylor, Russell H.

2004-04-01

The registration of preoperative CT to intra-operative reality systems is a crucial step in Computer Assisted Orthopedic Surgery (CAOS). The intra-operative sensors include 3D digitizers, fiducials, X-rays and Ultrasound (US). Although US has many advantages over others, tracked US for Orthopedic Surgery has been researched by only a few authors. An important factor limiting the accuracy of tracked US to CT registration (1-3mm) has been the difficulty in determining the exact location of the bone surfaces in the US images (the response could range from 2-4mm). Thus it is crucial to localize the bone surface accurately from these images. Moreover conventional US imaging systems are known to have certain inherent inaccuracies, mainly due to the fact that the imaging model is assumed planar. This creates the need to develop a bone segmentation framework that can couple information from various post-processed spatially separated US images (of the bone) to enhance the localization of the bone surface. In this paper we discuss the various reasons that cause inherent uncertainties in the bone surface localization (in B-mode US images) and suggest methods to account for these. We also develop a method for automatic bone surface detection. To do so, we account objectively for the high-level understanding of the various bone surface features visible in typical US images. A combination of these features would finally decide the surface position. We use a Bayesian probabilistic framework, which strikes a fair balance between high level understanding from features in an image and the low level number crunching of standard image processing techniques. It also provides us with a mathematical approach that facilitates combining multiple images to augment the bone surface estimate.

Unique Neural Characteristics of Atypical Lateralization of Language in Healthy Individuals

PubMed Central

Biduła, Szymon P.; Przybylski, Łukasz; Pawlak, Mikołaj A.; Króliczak, Gregory

2017-01-01

Using functional magnetic resonance imaging (fMRI) in 63 healthy participants, including left-handed and ambidextrous individuals, we tested how atypical lateralization of language—i. e., bilateral or right hemispheric language representation—differs from the typical left-hemisphere dominance. Although regardless of their handedness, all 11 participants from the atypical group engaged classical language centers, i.e., Broca's and Wernicke's areas, the right-hemisphere components of the default mode network (DMN), including the angular gyrus and middle temporal gyrus, were also critically involved during the verbal fluency task. Importantly, activity in these regions could not be explained in terms of mirroring the typical language pattern because left-hemisphere dominant individuals did not exhibit similar significant signal modulations. Moreover, when spatial extent of language-related activity across whole brain was considered, the bilateral language organization entailed more diffuse functional processing. Finally, we detected significant differences between the typical and atypical group in the resting-state connectivity at the global and local level. These findings suggest that the atypical lateralization of language has unique features, and is not a simple mirror image of the typical left hemispheric language representation. PMID:28983238

Comparing object recognition from binary and bipolar edge images for visual prostheses

PubMed Central

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2017-01-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition. PMID:28458481

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing.

PubMed

Einhäuser, Wolfgang; Nuthmann, Antje

2016-09-01

During natural scene viewing, humans typically attend and fixate selected locations for about 200-400 ms. Two variables characterize such "overt" attention: the probability of a location being fixated, and the fixation's duration. Both variables have been widely researched, but little is known about their relation. We use a two-step approach to investigate the relation between fixation probability and duration. In the first step, we use a large corpus of fixation data. We demonstrate that fixation probability (empirical salience) predicts fixation duration across different observers and tasks. Linear mixed-effects modeling shows that this relation is explained neither by joint dependencies on simple image features (luminance, contrast, edge density) nor by spatial biases (central bias). In the second step, we experimentally manipulate some of these features. We find that fixation probability from the corpus data still predicts fixation duration for this new set of experimental data. This holds even if stimuli are deprived of low-level images features, as long as higher level scene structure remains intact. Together, this shows a robust relation between fixation duration and probability, which does not depend on simple image features. Moreover, the study exemplifies the combination of empirical research on a large corpus of data with targeted experimental manipulations.

A computational study on convolutional feature combination strategies for grade classification in colon cancer using fluorescence microscopy data

NASA Astrophysics Data System (ADS)

Chowdhury, Aritra; Sevinsky, Christopher J.; Santamaria-Pang, Alberto; Yener, Bülent

2017-03-01

The cancer diagnostic workflow is typically performed by highly specialized and trained pathologists, for which analysis is expensive both in terms of time and money. This work focuses on grade classification in colon cancer. The analysis is performed over 3 protein markers; namely E-cadherin, beta actin and colagenIV. In addition, we also use a virtual Hematoxylin and Eosin (HE) stain. This study involves a comparison of various ways in which we can manipulate the information over the 4 different images of the tissue samples and come up with a coherent and unified response based on the data at our disposal. Pre- trained convolutional neural networks (CNNs) is the method of choice for feature extraction. The AlexNet architecture trained on the ImageNet database is used for this purpose. We extract a 4096 dimensional feature vector corresponding to the 6th layer in the network. Linear SVM is used to classify the data. The information from the 4 different images pertaining to a particular tissue sample; are combined using the following techniques: soft voting, hard voting, multiplication, addition, linear combination, concatenation and multi-channel feature extraction. We observe that we obtain better results in general than when we use a linear combination of the feature representations. We use 5-fold cross validation to perform the experiments. The best results are obtained when the various features are linearly combined together resulting in a mean accuracy of 91.27%.

Reducing Field Distortion in Magnetic Resonance Imaging

NASA Technical Reports Server (NTRS)

Eom, Byeong Ho; Penanen, Konstantin; Hahn, Inseob

2010-01-01

A concept for a magnetic resonance imaging (MRI) system that would utilize a relatively weak magnetic field provides for several design features that differ significantly from the corresponding features of conventional MRI systems. Notable among these features are a magnetic-field configuration that reduces (relative to the conventional configuration) distortion and blurring of the image, the use of a superconducting quantum interference device (SQUID) magnetometer as the detector, and an imaging procedure suited for the unconventional field configuration and sensor. In a typical application of MRI, a radio-frequency pulse is used to excite precession of the magnetic moments of protons in an applied magnetic field, and the decaying precession is detected for a short time following the pulse. The precession occurs at a resonance frequency proportional to the strengths of the magnetic field and the proton magnetic moment. The magnetic field is configured to vary with position in a known way; hence, by virtue of the aforesaid proportionality, the resonance frequency varies with position in a known way. In other words, position is encoded as resonance frequency. MRI using magnetic fields weaker than those of conventional MRI offers several advantages, including cheaper and smaller equipment, greater compatibility with metallic objects, and higher image quality because of low susceptibility distortion and enhanced spin-lattice-relaxation- time contrast. SQUID MRI is being developed into a practical MRI method for applied magnetic flux densities of the order of only 100 T

Image preprocessing study on KPCA-based face recognition

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Dehua

2015-12-01

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Clinical skin imaging using color spatial frequency domain imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Yang, Bin; Lesicko, John; Moy, Austin J.; Reichenberg, Jason; Tunnell, James W.

2016-02-01

Skin diseases are typically associated with underlying biochemical and structural changes compared with normal tissues, which alter the optical properties of the skin lesions, such as tissue absorption and scattering. Although widely used in dermatology clinics, conventional dermatoscopes don't have the ability to selectively image tissue absorption and scattering, which may limit its diagnostic power. Here we report a novel clinical skin imaging technique called color spatial frequency domain imaging (cSFDI) which enhances contrast by rendering color spatial frequency domain (SFD) image at high spatial frequency. Moreover, by tuning spatial frequency, we can obtain both absorption weighted and scattering weighted images. We developed a handheld imaging system specifically for clinical skin imaging. The flexible configuration of the system allows for better access to skin lesions in hard-to-reach regions. A total of 48 lesions from 31 patients were imaged under 470nm, 530nm and 655nm illumination at a spatial frequency of 0.6mm^(-1). The SFD reflectance images at 470nm, 530nm and 655nm were assigned to blue (B), green (G) and red (R) channels to render a color SFD image. Our results indicated that color SFD images at f=0.6mm-1 revealed properties that were not seen in standard color images. Structural features were enhanced and absorption features were reduced, which helped to identify the sources of the contrast. This imaging technique provides additional insights into skin lesions and may better assist clinical diagnosis.

Zernike phase contrast cryo-electron tomography of whole bacterial cells

PubMed Central

Guerrero-Ferreira, Ricardo C.; Wright, Elizabeth R.

2014-01-01

Cryo-electron tomography (cryo-ET) provides three-dimensional (3D) structural information of bacteria preserved in a native, frozen-hydrated state. The typical low contrast of tilt-series images, a result of both the need for a low electron dose and the use of conventional defocus phase-contrast imaging, is a challenge for high-quality tomograms. We show that Zernike phase-contrast imaging allows the electron dose to be reduced. This limits movement of gold fiducials during the tilt series, which leads to better alignment and a higher-resolution reconstruction. Contrast is also enhanced, improving visibility of weak features. The reduced electron dose also means that more images at more tilt angles could be recorded, further increasing resolution. PMID:24075950

ARCOCT: Automatic detection of lumen border in intravascular OCT images.

PubMed

Cheimariotis, Grigorios-Aris; Chatzizisis, Yiannis S; Koutkias, Vassilis G; Toutouzas, Konstantinos; Giannopoulos, Andreas; Riga, Maria; Chouvarda, Ioanna; Antoniadis, Antonios P; Doulaverakis, Charalambos; Tsamboulatidis, Ioannis; Kompatsiaris, Ioannis; Giannoglou, George D; Maglaveras, Nicos

2017-11-01

Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. ARCOCT allows accurate and fully-automated lumen border detection in OCT images. Copyright © 2017 Elsevier B.V. All rights reserved.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

PubMed Central

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning.

PubMed

Yu, Huan; Caldwell, Curtis; Mah, Katherine; Mozeg, Daniel

2009-03-01

Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered 18F-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (AZ) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The AZ of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET Coarseness, PET Contrast, and CT Coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC.

Real-time high dynamic range laser scanning microscopy

PubMed Central

Vinegoni, C.; Leon Swisher, C.; Fumene Feruglio, P.; Giedt, R. J.; Rousso, D. L.; Stapleton, S.; Weissleder, R.

2016-01-01

In conventional confocal/multiphoton fluorescence microscopy, images are typically acquired under ideal settings and after extensive optimization of parameters for a given structure or feature, often resulting in information loss from other image attributes. To overcome the problem of selective data display, we developed a new method that extends the imaging dynamic range in optical microscopy and improves the signal-to-noise ratio. Here we demonstrate how real-time and sequential high dynamic range microscopy facilitates automated three-dimensional neural segmentation. We address reconstruction and segmentation performance on samples with different size, anatomy and complexity. Finally, in vivo real-time high dynamic range imaging is also demonstrated, making the technique particularly relevant for longitudinal imaging in the presence of physiological motion and/or for quantification of in vivo fast tracer kinetics during functional imaging. PMID:27032979

Utility of fat-suppressed sequences in differentiation of aggressive vs typical asymptomatic haemangioma of the spine.

PubMed

Nabavizadeh, Seyed Ali; Mamourian, Alexander; Schmitt, James E; Cloran, Francis; Vossough, Arastoo; Pukenas, Bryan; Loevner, Laurie A; Mohan, Suyash

2016-01-01

While haemangiomas are common benign vascular lesions involving the spine, some behave in an aggressive fashion. We investigated the utility of fat-suppressed sequences to differentiate between benign and aggressive vertebral haemangiomas. Patients with the diagnosis of aggressive vertebral haemangioma and available short tau inversion-recovery or T2 fat saturation sequence were included in the study. 11 patients with typical asymptomatic vertebral body haemangiomas were selected as the control group. Region of interest signal intensity (SI) analysis of the entire haemangioma as well as the portion of each haemangioma with highest signal on fat-saturation sequences was performed and normalized to a reference normal vertebral body. A total of 8 patients with aggressive vertebral haemangioma and 11 patients with asymptomatic typical vertebral haemangioma were included. There was a significant difference between total normalized mean SI ratio (3.14 vs 1.48, p = 0.0002), total normalized maximum SI ratio (5.72 vs 2.55, p = 0.0003), brightest normalized mean SI ratio (4.28 vs 1.72, p < 0.0001) and brightest normalized maximum SI ratio (5.25 vs 2.45, p = 0.0003). Multiple measures were able to discriminate between groups with high sensitivity (>88%) and specificity (>82%). In addition to the conventional imaging features such as vertebral expansion and presence of extravertebral component, quantitative evaluation of fat-suppression sequences is also another imaging feature that can differentiate aggressive haemangioma and typical asymptomatic haemangioma. The use of quantitative fat-suppressed MRI in vertebral haemangiomas is demonstrated. Quantitative fat-suppressed MRI can have a role in confirming the diagnosis of aggressive haemangiomas. In addition, this application can be further investigated in future studies to predict aggressiveness of vertebral haemangiomas in early stages.

Extramedullary haematopoiesis: radiological imaging features.

PubMed

Roberts, A S; Shetty, A S; Mellnick, V M; Pickhardt, P J; Bhalla, S; Menias, C O

2016-09-01

Extramedullary haematopoiesis (EMH) is defined as the production of blood cells outside of the bone marrow, which occurs when there is inadequate production of blood cells. The most common causes of EMH are myelofibrosis, diffuse osseous metastatic disease replacing the bone marrow, leukaemia, sickle cell disease, and thalassemia. The purpose of this article is to review the common and uncommon imaging appearances of EMH by anatomical compartment. In the thorax, EMH most commonly presents as paravertebral fat-containing masses, and typically does not present a diagnostic dilemma; however, EMH in the abdomen most commonly manifests as hepatosplenomegaly with or without focal soft-tissue masses in the liver, spleen, perirenal space, and in the peritoneum. Hepatosplenomegaly, a non-specific feature, most often occurs without an associated focal mass, which makes suggestion of EMH difficult. EMH manifesting as visceral soft-tissue masses often requires biopsy as the differential diagnosis can include lymphoma, metastatic disease, and sarcoma. Many of these soft-tissue masses do not contain adipose elements, making the diagnosis of EMH difficult. Clinical history is crucial, as EMH would likely not otherwise be in the differential in patients with non-specific abdominal masses. Careful biopsy planning is necessary when EMH is a diagnostic consideration, given the propensity for haemorrhage. Understanding the typical imaging appearances of EMH based on its site of manifestation can help the radiologist when encountered with a finding that is diagnostic for EMH, and can help the radiologist suggest the need and plan appropriately for image-guided biopsy. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Differentiation of arterioles from venules in mouse histology images using machine learning

NASA Astrophysics Data System (ADS)

Elkerton, J. S.; Xu, Yiwen; Pickering, J. G.; Ward, Aaron D.

2016-03-01

Analysis and morphological comparison of arteriolar and venular networks are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained for smooth muscle α-actin. Classifiers trained on texture and morphologic features by supervised machine learning provided excellent classification accuracy for differentiation of arterioles and venules, achieving an area under the receiver operating characteristic curve of 0.90 and balanced false-positive and false-negative rates. Feature selection was consistent across cross-validation iterations, and a small set of three features was required to achieve the reported performance, suggesting potential generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample, and paves the way for high-throughput analysis the arteriolar and venular networks in the mouse.

Mammographic texture synthesis using genetic programming and clustered lumpy background

NASA Astrophysics Data System (ADS)

Castella, Cyril; Kinkel, Karen; Descombes, François; Eckstein, Miguel P.; Sottas, Pierre-Edouard; Verdun, Francis R.; Bochud, François O.

2006-03-01

In this work we investigated the digital synthesis of images which mimic real textures observed in mammograms. Such images could be produced in an unlimited number with tunable statistical properties in order to study human performance and model observer performance in perception experiments. We used the previously developed clustered lumpy background (CLB) technique and optimized its parameters with a genetic algorithm (GA). In order to maximize the realism of the textures, we combined the GA objective approach with psychophysical experiments involving the judgments of radiologists. Thirty-six statistical features were computed and averaged, over 1000 real mammograms regions of interest. The same features were measured for the synthetic textures, and the Mahalanobis distance was used to quantify the similarity of the features between the real and synthetic textures. The similarity, as measured by the Mahalanobis distance, was used as GA fitness function for evolving the free CLB parameters. In the psychophysical approach, experienced radiologists were asked to qualify the realism of synthetic images by considering typical structures that are expected to be found on real mammograms: glandular and fatty areas, and fiber crossings. Results show that CLB images found via optimization with GA are significantly closer to real mammograms than previously published images. Moreover, the psychophysical experiments confirm that all the above mentioned structures are reproduced well on the generated images. This means that we can generate an arbitrary large database of textures mimicking mammograms with traceable statistical properties.

Optical imaging of airglow structure in equatorial plasma bubbles at radio scintillation scales

NASA Astrophysics Data System (ADS)

Holmes, J. M.; Pedersen, T.; Parris, R. T.; Stephens, B.; Caton, R. G.; Dao, E. V.; Kratochvil, S.; Morton, Y.; Xu, D.; Jiao, Y.; Taylor, S.; Carrano, C. S.

2015-12-01

Imagery of optical emissions from F-region plasma is one of the few means available todetermine plasma density structure in two dimensions. However, the smallest spatial scalesobservable with this technique are typically limited not by magnification of the lens or resolutionof the detector but rather by the optical throughput of the system, which drives the integrationtime, which in turn causes smearing of the features that are typically moving at speeds of 100m/s or more. In this paper we present high spatio-temporal imagery of equatorial plasma bubbles(EPBs) from an imaging system called the Large Aperture Ionospheric Structure Imager(LAISI), which was specifically designed to capture short-integration, high-resolution images ofF-region recombination airglow at λ557.7 nm. The imager features 8-inch diameter entranceoptics comprised of a unique F/0.87 lens, combined with a monolithic 8-inch diameterinterference filter and a 2x2-inch CCD detector. The LAISI field of view is approximately 30degrees. Filtered all-sky images at common airglow wavelengths are combined with magneticfield-aligned LAISI images, GNSS scintillation, and VHF scintillation data obtained atAscension Island (7.98S, 14.41W geographic). A custom-built, multi-constellation GNSS datacollection system was employed that sampled GPS L1, L2C, L5, GLONASS L1 and L2, BeidouB1, and Galileo E1 and E5a signals. Sophisticated processing software was able to maintainlock of all signals during strong scintillation, providing unprecedented spatial observability ofL band scintillation. The smallest-resolvable scale sizes above the noise floor in the EPBs, as viewed byLAISI, are illustrated for integration times of 1, 5 and 10 seconds, with concurrentzonal irregularity drift speeds from both spaced-receiver VHF measurements and single-stationGNSS measurements of S4 and σφ. These observable optical scale sizes are placed in thecontext of those that give rise to radio scintillation in VHF and L band signals.

Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features

PubMed Central

Corredor, Germán; Whitney, Jon; Arias, Viviana; Madabhushi, Anant; Romero, Eduardo

2017-01-01

Abstract. Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in whole slide images. Handcrafted low- and mid-level features are computed from area, color, and spatial nuclei distributions. High-level information is implicitly captured from the recorded navigations of pathologists while exploring whole slide images during diagnostic tasks. This model was validated by predicting the presence of cancer in a set of unseen fields of view. The available database was composed of 24 cases of basal-cell carcinoma, from which 17 served to estimate the model parameters and the remaining 7 comprised the evaluation set. A total of 274 fields of view of size 1024×1024  pixels were extracted from the evaluation set. Then 176 patches from this set were used to train a support vector machine classifier to predict the presence of cancer on a patch-by-patch basis while the remaining 98 image patches were used for independent testing, ensuring that the training and test sets do not comprise patches from the same patient. A baseline model (M_ex) estimated the cancer likelihood for each of the image patches. M_ex uses the same visual features as M_im, but its weights are estimated from nuclei manually labeled as cancerous or noncancerous by a pathologist. M_im achieved an accuracy of 74.49% and an F-measure of 80.31%, while M_ex yielded corresponding accuracy and F-measures of 73.47% and 77.97%, respectively. PMID:28382314

Varying face occlusion detection and iterative recovery for face recognition

NASA Astrophysics Data System (ADS)

Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Mei

2017-05-01

In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

Restoration Of MEX SRC Images For Improved Topography: A New Image Product

NASA Astrophysics Data System (ADS)

Duxbury, T. C.

2012-12-01

Surface topography is an important constraint when investigating the evolution of solar system bodies. Topography is typically obtained from stereo photogrammetric or photometric (shape from shading) analyses of overlapping / stereo images and from laser / radar altimetry data. The ESA Mars Express Mission [1] carries a Super Resolution Channel (SRC) as part of the High Resolution Stereo Camera (HRSC) [2]. The SRC can build up overlapping / stereo coverage of Mars, Phobos and Deimos by viewing the surfaces from different orbits. The derivation of high precision topography data from the SRC raw images is degraded because the camera is out of focus. The point spread function (PSF) is multi-peaked, covering tens of pixels. After registering and co-adding hundreds of star images, an accurate SRC PSF was reconstructed and is being used to restore the SRC images to near blur free quality. The restored images offer a factor of about 3 in improved geometric accuracy as well as identifying the smallest of features to significantly improve the stereo photogrammetric accuracy in producing digital elevation models. The difference between blurred and restored images provides a new derived image product that can provide improved feature recognition to increase spatial resolution and topographic accuracy of derived elevation models. Acknowledgements: This research was funded by the NASA Mars Express Participating Scientist Program. [1] Chicarro, et al., ESA SP 1291(2009) [2] Neukum, et al., ESA SP 1291 (2009). A raw SRC image (h4235.003) of a Martian crater within Gale crater (the MSL landing site) is shown in the upper left and the restored image is shown in the lower left. A raw image (h0715.004) of Phobos is shown in the upper right and the difference between the raw and restored images, a new derived image data product, is shown in the lower right. The lower images, resulting from an image restoration process, significantly improve feature recognition for improved derived topographic accuracy.

Kinematic Measurements of the Vocal-Fold Displacement Waveform in Typical Children and Adult Populations: Quantification of High-Speed Endoscopic Videos

PubMed Central

Donohue, Kevin D.; Unnikrishnan, Harikrishnan; Kryscio, Richard J.

2015-01-01

Purpose This article presents a quantitative method for assessing instantaneous and average lateral vocal-fold motion from high-speed digital imaging, with a focus on developmental changes in vocal-fold kinematics during childhood. Method Vocal-fold vibrations were analyzed for 28 children (aged 5–11 years) and 28 adults (aged 21–45 years) without voice disorders. The following kinematic features were analyzed from the vocal-fold displacement waveforms: relative velocity-based features (normalized average and peak opening and closing velocities), relative acceleration-based features (normalized peak opening and closing accelerations), speed quotient, and normalized peak displacement. Results Children exhibited significantly larger normalized peak displacements, normalized average and peak opening velocities, normalized average and peak closing velocities, peak opening and closing accelerations, and speed quotient compared to adult women. Values of normalized average closing velocity and speed quotient were higher in children compared to adult men. Conclusions When compared to adult men, developing children typically have higher estimates of kinematic features related to normalized displacement and its derivatives. In most cases, the kinematic features of children are closer to those of adult men than adult women. Even though boys experience greater changes in glottal length and pitch as they mature, results indicate that girls experience greater changes in kinematic features compared to boys. PMID:25652615

Branchial cleft anomalies: a pictorial review of embryological development and spectrum of imaging findings.

PubMed

Adams, Ashok; Mankad, Kshitij; Offiah, Curtis; Childs, Lucy

2016-02-01

The branchial arches are the embryological precursors of the face, neck and pharynx. Anomalies of the branchial arches are the second most common congenital lesions of the head and neck in children, with second branchial arch anomalies by far the most common. Clinically, these congenital anomalies may present as cysts, sinus tracts, fistulae or cartilaginous remnants with typical clinical and radiological findings. We review the normal embryological development of the branchial arches and the anatomical structures of the head and neck that derive from each arch. The typical clinical and radiological appearances of both common and uncommon branchial arch abnormalities are discussed with an emphasis on branchial cleft anomalies. • Anomalies of the branchial arches usually present as cysts, sinuses or fistulae. • Second branchial arch anomalies account for approximately 95 % of cases. • There are no pathognomonic imaging features so diagnosis depends on a high index of suspicion and knowledge of typical locations. • Persistent cysts, fistulae or recurrent localised infection may be due to branchial arch anomalies. • Surgical excision of the cyst or tract is the most common curative option.

Evaluation of different distortion correction methods and interpolation techniques for an automated classification of celiac disease☆

PubMed Central

Gadermayr, M.; Liedlgruber, M.; Uhl, A.; Vécsei, A.

2013-01-01

Due to the optics used in endoscopes, a typical degradation observed in endoscopic images are barrel-type distortions. In this work we investigate the impact of methods used to correct such distortions in images on the classification accuracy in the context of automated celiac disease classification. For this purpose we compare various different distortion correction methods and apply them to endoscopic images, which are subsequently classified. Since the interpolation used in such methods is also assumed to have an influence on the resulting classification accuracies, we also investigate different interpolation methods and their impact on the classification performance. In order to be able to make solid statements about the benefit of distortion correction we use various different feature extraction methods used to obtain features for the classification. Our experiments show that it is not possible to make a clear statement about the usefulness of distortion correction methods in the context of an automated diagnosis of celiac disease. This is mainly due to the fact that an eventual benefit of distortion correction highly depends on the feature extraction method used for the classification. PMID:23981585

Cranio-orbital primary intraosseous haemangioma

PubMed Central

Gupta, T; Rose, G E; Manisali, M; Minhas, P; Uddin, J M; Verity, D H

2013-01-01

Purpose Primary intraosseous haemangioma (IOH) is a rare benign neoplasm presenting in the fourth and fifth decades of life. The spine and skull are the most commonly involved, orbital involvement is extremely rare. We describe six patients with cranio-orbital IOH, the largest case series to date. Patients and methods Retrospective review of six patients with histologically confirmed primary IOH involving the orbit. Clinical characteristics, imaging features, approach to management, and histopathological findings are described. Results Five patients were male with a median age of 56. Pain and diplopia were the most common presenting features. A characteristic ‘honeycomb' pattern on CT imaging was demonstrated in three of the cases. Complete surgical excision was performed in all cases with presurgical embolisation carried out in one case. In all the cases, histological studies identified cavernous vascular spaces within the bony tissue. These channels were lined by single layer of cytologically normal endothelial cells. Discussion IOCH of the cranio-orbital region is rare; in the absence of typical imaging features, the differential diagnosis includes chondroma, chondrosarcoma, bony metastasis, and lymphoma. Surgical excision may be necessary to exclude more sinister pathology. Intraoperative haemorrhage can be severe and may be reduced by preoperative embolisation. PMID:23989119

MR imaging findings of adenomyosis: correlation with histopathologic features and diagnostic pitfalls.

PubMed

Tamai, Ken; Togashi, Kaori; Ito, Tsuyoshi; Morisawa, Nobuko; Fujiwara, Toshitaka; Koyama, Takashi

2005-01-01

Adenomyosis is a nonneoplastic condition, characterized by benign invasion of ectopic endometrium into the myometrium with hyperplasia of adjacent smooth muscle. The common symptoms include dysmenorrhea, menorrhagia, and abnormal uterine bleeding, but these do not allow diagnosis. Therefore, imaging plays an important role because establishment of the correct preoperative diagnosis is critical to avoid unnecessary intervention. Magnetic resonance (MR) imaging is a highly accurate noninvasive modality for diagnosis of adenomyosis, differentiation of adenomyosis from other gynecologic disorders, and planning of appropriate treatment. Although the typical MR imaging findings are well established, adenomyosis actually varies widely in terms of histopathologic features (adenomyosis with sparse glands), growth patterns (polypoid adenomyoma, adenomyotic cyst, and miniature uterus), responses to hormonal activity (tamoxifen, decidual changes), and responses to treatment (gonadotropin-releasing hormone agonist). The MR imaging findings of adenomyosis occasionally mimic those of uterine malignancy or ovarian cancer. Furthermore, malignancy occasionally develops in otherwise benign adenomyosis. Pitfalls in diagnosis of adenomyosis include myometrial contractions, leiomyoma, adenomatoid tumor, metastases, endometrial carcinoma, and endometrial stromal sarcoma. Knowledge of the various appearances of adenomyosis and the possible pitfalls in differential diagnosis help guide the determination of appropriate treatment options. (c) RSNA, 2005.

Imaging of autoimmune encephalitis--Relevance for clinical practice and hippocampal function.

PubMed

Heine, J; Prüss, H; Bartsch, T; Ploner, C J; Paul, F; Finke, C

2015-11-19

The field of autoimmune encephalitides associated with antibodies targeting cell-surface antigens is rapidly expanding and new antibodies are discovered frequently. Typical clinical presentations include cognitive deficits, psychiatric symptoms, movement disorders and seizures and the majority of patients respond well to immunotherapy. Pathophysiological mechanisms and clinical features are increasingly recognized and indicate hippocampal dysfunction in most of these syndromes. Here, we review the neuroimaging characteristics of autoimmune encephalitides, including N-methyl-d-aspartate (NMDA) receptor, leucine-rich glioma inactivated 1 (LGI1), contactin-associated protein-like 2 (CASPR2) encephalitis as well as more recently discovered and less frequent forms such as dipeptidyl-peptidase-like protein 6 (DPPX) or glycine receptor encephalitis. We summarize findings of routine magnetic resonance imaging (MRI) investigations as well as (18)F-fluoro-2-deoxy-d-glucose (FDG)-positron emission tomography (PET) and single photon emission tomography (SPECT) imaging and relate these observations to clinical features and disease outcome. We furthermore review results of advanced imaging analyses such as diffusion tensor imaging, volumetric analyses and resting-state functional MRI. Finally, we discuss contributions of these neuroimaging observations to the understanding of the pathophysiology of autoimmune encephalitides. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Automatic evaluation of skin histopathological images for melanocytic features

NASA Astrophysics Data System (ADS)

Koosha, Mohaddeseh; Hoseini Alinodehi, S. Pourya; Nicolescu, Mircea; Safaei Naraghi, Zahra

2017-03-01

Successfully detecting melanocyte cells in the skin epidermis has great significance in skin histopathology. Because of the existence of cells with similar appearance to melanocytes in hematoxylin and eosin (HE) images of the epidermis, detecting melanocytes becomes a challenging task. This paper proposes a novel technique for the detection of melanocytes in HE images of the epidermis, based on the melanocyte color features, in the HSI color domain. Initially, an effective soft morphological filter is applied to the HE images in the HSI color domain to remove noise. Then a novel threshold-based technique is applied to distinguish the candidate melanocytes' nuclei. Similarly, the method is applied to find the candidate surrounding halos of the melanocytes. The candidate nuclei are associated with their surrounding halos using the suggested logical and statistical inferences. Finally, a fuzzy inference system is proposed, based on the HSI color information of a typical melanocyte in the epidermis, to calculate the similarity ratio of each candidate cell to a melanocyte. As our review on the literature shows, this is the first method evaluating epidermis cells for melanocyte similarity ratio. Experimental results on various images with different zooming factors show that the proposed method improves the results of previous works.

Application of MOS-1 MESSR image to the investigation of wetlands in Poyang Lake

NASA Astrophysics Data System (ADS)

Chen, Shuisen; Li, Yan

1998-08-01

The lake beach and grass moor land is a kind of typical wetlands. The area varies greatly with season in Poyang Lake region. Moreover, the field investigation of wetlands is almost impossible as geographical features and difficulties in transportation. The notes address the potential role of remote sensing in the surveying of the lake beach and grass moor land. In particular, the notes reflect the characteristics relationships between MOS-1 MESSR image and the wetlands. The application results show that MOS-1 MESSR image is effective in surveying the wetland area variation and distribution (lake, river, grass moor, mud flat, sand beach, etc.). detecting lake base shape, and analyzing eco-environment surrounded.

Neuron’s eye view: Inferring features of complex stimuli from neural responses

PubMed Central

Chen, Xin; Beck, Jeffrey M.

2017-01-01

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world—contrast and luminance for vision, pitch and intensity for sound—and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set. PMID:28827790

Clinical and Imaging Findings in Childhood Posterior Reversible Encephalopathy Syndrome

PubMed Central

GUNGOR, Serdal; KILIC, Betul; TABEL, Yilmaz; SELIMOGLU, Ayse; OZGEN, Unsal; YILMAZ, Sezai

2018-01-01

Objective Posterior reversible encephalopathy syndrome (PRES) is characterized by typical radiologic findings in the posterior regions of the cerebral hemispheres and cerebellum. The symptoms include headache, nausea, vomiting, visual disturbances, focal neurologic deficits, and seizures. The aim of this study is to evaluate the clinical and radiological features of PRES in children and to emphasize the recognition of atypical features. Materials & Methods We retrospectively examined 23 children with PRES from Mar 2010-Apr 2015 in Inonu University Turgut Ozal Medical Center in Turkey. We compared the clinical features and cranial MRI findings between underlying diseases of PRES. Results The most common precipitating factors were hypertension (78.2%) and medications, namely immunosuppressive and antineoplastic agents (60.8%). Manifestations included mental changes (100%), seizures (95.6%), headache (60.8%), and visual disturbances (21.7%) of mean 3.6 (range 1-10) days' duration. Cranial magnetic resonance imaging (MRI) showed bilateral occipital lesions in all patients, associated in 82.6% with less typical distribution of lesions in frontal, temporal or parietal lobes, cerebellum, corpus callosum, basal ganglia, thalamus, and brain stem. Frontal involvement was predominant, observed in 56.5% of patients. Clinical recovery was followed by radiologic resolution in all patients. Conclusion PRES is often unsuspected by the clinician, thus radiologists may be the first to suggest this diagnosis on an MRI obtained for seizures or encephalopathy. Atypical MRI finding is seen quite often. Rapid diagnosis and treatment are required to avoid a devastating outcome. PMID:29379559

Post-pancreatitis Fat Necrosis Mimicking Carcinomatosis.

PubMed

Smith, Joshua P; Arnoletti, J Pablo; Varadarajulu, Shyam; Morgan, Desiree E

2008-01-01

Acute pancreatitis can result in retroperitoneal fat necrosis, typically occurring in the peripancreatic region, with extension into the transverse mesocolon, omentum and mesenteric root. When evaluated with contrast enhanced computed tomography (CECT), acute peripancreatic post necrotic collections typically become lower in attenuation over time, and often appear as homogeneous fluid collections. Saponification as a complication of fat necrosis in patients with acute pancreatitis is a well recognized clinical entity. While retroperitonal fat necrosis is commonly seen on CECT, saponification is not a prominent imaging feature. We present a case of acute pancreatitis complicated by extensive saponification of fat throughout the retroperitoneum and peritoneal lining, mimicking carcinomatosis.

The early-stage diagnosis of albinic embryos by applying optical coherence tomography

NASA Astrophysics Data System (ADS)

Yang, Bor-Wen; Wang, Shih-Yuan; Wang, Yu-Yen; Cai, Jyun-Jhang; Chang, Chung-Hao

2013-09-01

Albinism is a kind of congenital disease of abnormal metabolism. Poecilia reticulata (guppy fish) is chosen as the model to study the development of albinic embryos as it is albinic, ovoviviparous and with short life period. This study proposed an imaging method for penetrative embryo investigation using optical coherence tomography. By imaging through guppy mother’s reproduction purse, we found the embryo’s eyes were the early-developed albinism features. As human’s ocular albinism typically appear at about four weeks old, it is the time to determine if an embryo will grow into an albino.

First Images from HERO: A Hard-X-Ray Focusing Telescope

NASA Technical Reports Server (NTRS)

Ramsey, Brian D.; Alexander, Cheryl D.; Apple, Jeff A.; Benson, Carl M.; Dietz, Kurtis L.; Elsner, Ronald F.; Engelhaupt, Darell E.; Ghosh, Kajal K.; Kolodziejczak, Jeffery J.; ODell, Stephen L.;

Yardangs near Olympus Mons

NASA Image and Video Library

2002-12-16

In this region of the Olympus Mons aureole, located to the SW of the volcano, the surface has been eroded by the wind into linear landforms called yardangs. These ridges generally point in direction of the prevailing winds that carved them, in this case winds from the southeast. Yardangs typically occur on surfaces that are easily erodable, such as wind-blown dust or volcanic ash. The northeast - southwest trending ridges and valleys in the northwest corner of the image are typical of the Olympus Mons aureole. The varying concentration and shape of the yardangs in this area may be controlled by the motion of winds around these topographic features. Some crater outlines are visible near the top of this image. The rims of these craters appear to have been stripped away - indicating that the wind erosion is younger than these craters. There are two round knobs in the image, one on the bottom on the right side of the image and another about midway down on the left. These may be inverted craters, formed because the impacts caused materials underneath the crater to become harder to erode than the surrounding materials. http://photojournal.jpl.nasa.gov/catalog/PIA04036

Quantitative Magnetic Resonance Diffusion-Weighted Imaging Evaluation of the Supratentorial Brain Regions in Patients Diagnosed with Brainstem Variant of Posterior Reversible Encephalopathy Syndrome: A Preliminary Study.

PubMed

Chen, Tai-Yuan; Wu, Te-Chang; Ko, Ching-Chung; Feng, I-Jung; Tsui, Yu-Kun; Lin, Chien-Jen; Chen, Jeon-Hor; Lin, Ching-Po

2017-07-01

Posterior reversible encephalopathy syndrome (PRES) is a clinicoradiologic entity with several causes, characterized by rapid onset of symptoms and typical neuroimaging features, which usually resolve if promptly recognized and treated. Brainstem variant of PRES presents with vasogenic edema in brainstem regions on magnetic resonance (MR) images and there is sparing of the supratentorial regions. Because PRES is usually caused by a hypertensive crisis, which would likely have a systemic effect and global manifestations on the brain tissue, we thus proposed that some microscopic abnormalities of the supratentorial regions could be detected with diffusion-weighted imaging (DWI) using apparent diffusion coefficient (ADC) analysis in brainstem variant of PRES and hypothesized that "normal-looking" supratentorial regions will increase water diffusion. We retrospectively identified patients with PRES who underwent brain magnetic resonance imaging studies. We identified 11 brainstem variants of PRES patients, who formed the study cohort, and 11 typical PRES patients and 20 normal control subjects as the comparison cohorts for this study. Nineteen regions of interest were drawn and systematically placed. The mean ADC values were measured and compared among these 3 groups. ADC values of the typical PRES group were consistently elevated compared with those in normal control subjects. ADC values of the brainstem variant group were consistently elevated compared with those in normal control subjects. ADC values of the typical PRES group and brainstem variant group did not differ significantly, except for the pons area. Quantitative MR DWI may aid in the evaluation of supratentorial microscopic abnormalities in brainstem variant of PRES patients. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Application of a neural network for reflectance spectrum classification

NASA Astrophysics Data System (ADS)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

GENIE: a hybrid genetic algorithm for feature classification in multispectral images

NASA Astrophysics Data System (ADS)

Perkins, Simon J.; Theiler, James P.; Brumby, Steven P.; Harvey, Neal R.; Porter, Reid B.; Szymanski, John J.; Bloch, Jeffrey J.

2000-10-01

We consider the problem of pixel-by-pixel classification of a multi- spectral image using supervised learning. Conventional spuervised classification techniques such as maximum likelihood classification and less conventional ones s uch as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see why: the color of a pixel provides a nice, bounded, fixed dimensional space in which these classifiers work well. It is often the case however, that spectral information alone is not sufficient to correctly classify a pixel. Maybe spatial neighborhood information is required as well. Or maybe the raw spectral components do not themselves make for easy classification, but some arithmetic combination of them would. In either of these cases we have the problem of selecting suitable spatial, spectral or spatio-spectral features that allow the classifier to do its job well. The number of all possible such features is extremely large. How can we select a suitable subset? We have developed GENIE, a hybrid learning system that combines a genetic algorithm that searches a space of image processing operations for a set that can produce suitable feature planes, and a more conventional classifier which uses those feature planes to output a final classification. In this paper we show that the use of a hybrid GA provides significant advantages over using either a GA alone or more conventional classification methods alone. We present results using high-resolution IKONOS data, looking for regions of burned forest and for roads.

ASI aurora search: an attempt of intelligent image processing for circular fisheye lens.

PubMed

Yang, Xi; Gao, Xinbo; Song, Bin; Wang, Nannan; Yang, Dong

2018-04-02

The circular fisheye lens exhibits an approximately 180° angular field-of-view (FOV), which is much larger than that of an ordinary lens. Thus, images captured with a circular fisheye lens are distributed non-uniformly with spherical deformation. Along with the fast development of deep neural networks for normal images, how to apply it to achieve intelligent image processing for a circular fisheye lens is a new task of significant importance. In this paper, we take the aurora images captured with all-sky-imagers (ASI) as a typical example. By analyzing the imaging principle of ASI and the magnetic characteristics of the aurora, a deformed region division (DRD) scheme is proposed to replace the region proposals network (RPN) in the advanced mask regional convolutional neural network (Mask R-CNN) framework. Thus, each image can be regarded as a "bag" of deformed regions represented with CNN features. After clustering all CNN features to generate a vocabulary, each deformed region is quantified to its nearest center for indexing. On the stage of an online search, a similarity score is computed by measuring the distances between regions in the query image and all regions in the data set, and the image with the highest value is outputted as the top rank search result. Experimental results show that the proposed method greatly improves the search accuracy and efficiency, demonstrating that it is a valuable attempt of intelligent image processing for circular fisheye lenses.

A survey of camera error sources in machine vision systems

NASA Astrophysics Data System (ADS)

Jatko, W. B.

In machine vision applications, such as an automated inspection line, television cameras are commonly used to record scene intensity in a computer memory or frame buffer. Scene data from the image sensor can then be analyzed with a wide variety of feature-detection techniques. Many algorithms found in textbooks on image processing make the implicit simplifying assumption of an ideal input image with clearly defined edges and uniform illumination. The ideal image model is helpful to aid the student in understanding the principles of operation, but when these algorithms are blindly applied to real-world images the results can be unsatisfactory. This paper examines some common measurement errors found in camera sensors and their underlying causes, and possible methods of error compensation. The role of the camera in a typical image-processing system is discussed, with emphasis on the origination of signal distortions. The effects of such things as lighting, optics, and sensor characteristics are considered.

Forensic detection of noise addition in digital images

NASA Astrophysics Data System (ADS)

Cao, Gang; Zhao, Yao; Ni, Rongrong; Ou, Bo; Wang, Yongbin

2014-03-01

We proposed a technique to detect the global addition of noise to a digital image. As an anti-forensics tool, noise addition is typically used to disguise the visual traces of image tampering or to remove the statistical artifacts left behind by other operations. As such, the blind detection of noise addition has become imperative as well as beneficial to authenticate the image content and recover the image processing history, which is the goal of general forensics techniques. Specifically, the special image blocks, including constant and strip ones, are used to construct the features for identifying noise addition manipulation. The influence of noising on blockwise pixel value distribution is formulated and analyzed formally. The methodology of detectability recognition followed by binary decision is proposed to ensure the applicability and reliability of noising detection. Extensive experimental results demonstrate the efficacy of our proposed noising detector.

Image quality guided approach for adaptive modelling of biometric intra-class variations

NASA Astrophysics Data System (ADS)

Abboud, Ali J.; Jassim, Sabah A.

2010-04-01

The high intra-class variability of acquired biometric data can be attributed to several factors such as quality of acquisition sensor (e.g. thermal), environmental (e.g. lighting), behavioural (e.g. change face pose). Such large fuzziness of biometric data can cause a big difference between an acquired and stored biometric data that will eventually lead to reduced performance. Many systems store multiple templates in order to account for such variations in the biometric data during enrolment stage. The number and typicality of these templates are the most important factors that affect system performance than other factors. In this paper, a novel offline approach is proposed for systematic modelling of intra-class variability and typicality in biometric data by regularly selecting new templates from a set of available biometric images. Our proposed technique is a two stage algorithm whereby in the first stage image samples are clustered in terms of their image quality profile vectors, rather than their biometric feature vectors, and in the second stage a per cluster template is selected from a small number of samples in each clusters to create an ultimate template sets. These experiments have been conducted on five face image databases and their results will demonstrate the effectiveness of proposed quality guided approach.

High-resolution coherent backscatter interferometric radar images of equatorial spread F using Capon's method

NASA Astrophysics Data System (ADS)

Rodrigues, Fabiano S.; de Paula, Eurico R.; Zewdie, Gebreab K.

2017-03-01

We present results of Capon's method for estimation of in-beam images of ionospheric scattering structures observed by a small, low-power coherent backscatter interferometer. The radar interferometer operated in the equatorial site of São Luís, Brazil (2.59° S, 44.21° W, -2.35° dip latitude). We show numerical simulations that evaluate the performance of the Capon method for typical F region measurement conditions. Numerical simulations show that, despite the short baselines of the São Luís radar, the Capon technique is capable of distinguishing localized features with kilometric scale sizes (in the zonal direction) at F region heights. Following the simulations, we applied the Capon algorithm to actual measurements made by the São Luís interferometer during a typical equatorial spread F (ESF) event. As indicated by the simulations, the Capon method produced images that were better resolved than those produced by the Fourier method. The Capon images show narrow (a few kilometers wide) scattering channels associated with ESF plumes and scattering regions spaced by only a few tens of kilometers in the zonal direction. The images are also capable of resolving bifurcations and the C shape of scattering structures.

Comprehension of concrete and abstract words in semantic dementia

PubMed Central

Jefferies, Elizabeth; Patterson, Karalyn; Jones, Roy W.; Lambon Ralph, Matthew A.

2009-01-01

The vast majority of brain-injured patients with semantic impairment have better comprehension of concrete than abstract words. In contrast, several patients with semantic dementia (SD), who show circumscribed atrophy of the anterior temporal lobes bilaterally, have been reported to show reverse imageability effects, i.e., relative preservation of abstract knowledge. Although these reports largely concern individual patients, some researchers have recently proposed that superior comprehension of abstract concepts is a characteristic feature of SD. This would imply that the anterior temporal lobes are particularly crucial for processing sensory aspects of semantic knowledge, which are associated with concrete not abstract concepts. However, functional neuroimaging studies of healthy participants do not unequivocally predict reverse imageability effects in SD because the temporal poles sometimes show greater activation for more abstract concepts. We examined a case-series of eleven SD patients on a synonym judgement test that orthogonally varied the frequency and imageability of the items. All patients had higher success rates for more imageable as well as more frequent words, suggesting that (a) the anterior temporal lobes underpin semantic knowledge for both concrete and abstract concepts, (b) more imageable items – perhaps due to their richer multimodal representations – are typically more robust in the face of global semantic degradation and (c) reverse imageability effects are not a characteristic feature of SD. PMID:19586212

Time-reversal imaging for classification of submerged elastic targets via Gibbs sampling and the Relevance Vector Machine.

PubMed

Dasgupta, Nilanjan; Carin, Lawrence

2005-04-01

Time-reversal imaging (TRI) is analogous to matched-field processing, although TRI is typically very wideband and is appropriate for subsequent target classification (in addition to localization). Time-reversal techniques, as applied to acoustic target classification, are highly sensitive to channel mismatch. Hence, it is crucial to estimate the channel parameters before time-reversal imaging is performed. The channel-parameter statistics are estimated here by applying a geoacoustic inversion technique based on Gibbs sampling. The maximum a posteriori (MAP) estimate of the channel parameters are then used to perform time-reversal imaging. Time-reversal implementation requires a fast forward model, implemented here by a normal-mode framework. In addition to imaging, extraction of features from the time-reversed images is explored, with these applied to subsequent target classification. The classification of time-reversed signatures is performed by the relevance vector machine (RVM). The efficacy of the technique is analyzed on simulated in-channel data generated by a free-field finite element method (FEM) code, in conjunction with a channel propagation model, wherein the final classification performance is demonstrated to be relatively insensitive to the associated channel parameters. The underlying theory of Gibbs sampling and TRI are presented along with the feature extraction and target classification via the RVM.

Flame colour characterization in the visible and infrared spectrum using a digital camera and image processing

NASA Astrophysics Data System (ADS)

Huang, Hua-Wei; Zhang, Yang

2008-08-01

An attempt has been made to characterize the colour spectrum of methane flame under various burning conditions using RGB and HSV colour models instead of resolving the real physical spectrum. The results demonstrate that each type of flame has its own characteristic distribution in both the RGB and HSV space. It has also been observed that the averaged B and G values in the RGB model represent well the CH* and C*2 emission of methane premixed flame. Theses features may be utilized for flame measurement and monitoring. The great advantage of using a conventional camera for monitoring flame properties based on the colour spectrum is that it is readily available, easy to interface with a computer, cost effective and has certain spatial resolution. Furthermore, it has been demonstrated that a conventional digital camera is able to image flame not only in the visible spectrum but also in the infrared. This feature is useful in avoiding the problem of image saturation typically encountered in capturing the very bright sooty flames. As a result, further digital imaging processing and quantitative information extraction is possible. It has been identified that an infrared image also has its own distribution in both the RGB and HSV colour space in comparison with a flame image in the visible spectrum.

Influence of seismic diffraction for high-resolution imaging: applications in offshore Malaysia

NASA Astrophysics Data System (ADS)

Bashir, Yasir; Ghosh, Deva Prasad; Sum, Chow Weng

2018-04-01

Small-scale geological discontinuities are not easy to detect and image in seismic data, as these features represent themselves as diffracted rather than reflected waves. However, the combined reflected and diffracted image contains full wave information and is of great value to an interpreter, for instance enabling the identification of faults, fractures, and surfaces in built-up carbonate. Although diffraction imaging has a resolution below the typical seismic wavelength, if the wavelength is much smaller than the width of the discontinuity then interference effects can be ignored, as they would not play a role in generating the seismic diffractions. In this paper, by means of synthetic examples and real data, the potential of diffraction separation for high-resolution seismic imaging is revealed and choosing the best method for preserving diffraction are discussed. We illustrate the accuracy of separating diffractions using the plane-wave destruction (PWD) and dip frequency filtering (DFF) techniques on data from the Sarawak Basin, a carbonate field. PWD is able to preserve the diffraction more intelligently than DFF, which is proven in the results by the model and real data. The final results illustrate the effectiveness of diffraction separation and possible imaging for high-resolution seismic data of small but significant geological features.

Comparison of texture synthesis methods for content generation in ultrasound simulation for training

NASA Astrophysics Data System (ADS)

Mattausch, Oliver; Ren, Elizabeth; Bajka, Michael; Vanhoey, Kenneth; Goksel, Orcun

2017-03-01

Navigation and interpretation of ultrasound (US) images require substantial expertise, the training of which can be aided by virtual-reality simulators. However, a major challenge in creating plausible simulated US images is the generation of realistic ultrasound speckle. Since typical ultrasound speckle exhibits many properties of Markov Random Fields, it is conceivable to use texture synthesis for generating plausible US appearance. In this work, we investigate popular classes of texture synthesis methods for generating realistic US content. In a user study, we evaluate their performance for reproducing homogeneous tissue regions in B-mode US images from small image samples of similar tissue and report the best-performing synthesis methods. We further show that regression trees can be used on speckle texture features to learn a predictor for US realism.

Zernike phase contrast cryo-electron tomography of whole bacterial cells.

PubMed

Guerrero-Ferreira, Ricardo C; Wright, Elizabeth R

2014-01-01

Cryo-electron tomography (cryo-ET) provides three-dimensional (3D) structural information of bacteria preserved in a native, frozen-hydrated state. The typical low contrast of tilt-series images, a result of both the need for a low electron dose and the use of conventional defocus phase-contrast imaging, is a challenge for high-quality tomograms. We show that Zernike phase-contrast imaging allows the electron dose to be reduced. This limits movement of gold fiducials during the tilt series, which leads to better alignment and a higher-resolution reconstruction. Contrast is also enhanced, improving visibility of weak features. The reduced electron dose also means that more images at more tilt angles could be recorded, further increasing resolution. Copyright © 2013 Elsevier Inc. All rights reserved.

Cross Sectional Imaging of Solitary Lesions of the Neurocranium.

PubMed

Schäfer, Max-Ludwig; Koch, Arend; Streitparth, Florian; Wiener, Edzard

2017-12-01

Background  Although a wide range of processes along the neurocranium are of a benign nature, there are often difficulties in the differential diagnosis. Method  In the review CT/MRI scans of the head were evaluated retrospectively regarding solitary lesions along the neurocranium. The majority of the lesions were histologically proven. Results  The purpose of the review is to present typical pathologies of the neurocranium and provide a systematic overview based on 12 entities, their locations, prevalence and radiological characteristics. Conclusion  Processes, which primarily originate from the neurocranium have to be differentiated from secondary processes infiltrating the neurocranium. For this important diagnostic feature, MRI is typically essential, while the definitive diagnosis is often made on the basis of the medical history and the typical appearance on computer tomography. Key Points   · There are often difficulties in the precise differential diagnosis of solitary lesions along the neurocranium. Typical solitary pathologies of the neurocranium based on 12 entities were presented. Both magnetic resonance imaging and computed tomography are often essential for an exact differential diagnosis.. Citation Format · Schäfer M, Koch A, Streitparth F et al. Cross Sectional Diagnosis of Solitary Lesions of the Neurocranium. Fortschr Röntgenstr 2017; 189: 1135 - 1144. © Georg Thieme Verlag KG Stuttgart · New York.

Hyperbolic metamaterials: Novel physics and applications

NASA Astrophysics Data System (ADS)

Smolyaninov, Igor I.; Smolyaninova, Vera N.

2017-10-01

Hyperbolic metamaterials were originally introduced to overcome the diffraction limit of optical imaging. Soon thereafter it was realized that hyperbolic metamaterials demonstrate a number of novel phenomena resulting from the broadband singular behavior of their density of photonic states. These novel phenomena and applications include super resolution imaging, new stealth technologies, enhanced quantum-electrodynamic effects, thermal hyperconductivity, superconductivity, and interesting gravitation theory analogues. Here we briefly review typical material systems, which exhibit hyperbolic behavior and outline important novel applications of hyperbolic metamaterials. In particular, we will describe recent imaging experiments with plasmonic metamaterials and novel VCSEL geometries, in which the Bragg mirrors may be engineered in such a way that they exhibit hyperbolic metamaterial properties in the long wavelength infrared range, so that they may be used to efficiently remove excess heat from the laser cavity. We will also discuss potential applications of three-dimensional self-assembled photonic hypercrystals, which are based on cobalt ferrofluids in external magnetic field. This system bypasses 3D nanofabrication issues, which typically limit metamaterial applications. Photonic hypercrystals combine the most interesting features of hyperbolic metamaterials and photonic crystals.

Corticobasal degeneration with olivopontocerebellar atrophy and TDP-43 pathology: an unusual clinicopathologic variant of CBD

PubMed Central

Kouri, Naomi; Oshima, Kenichi; Takahashi, Makio; Murray, Melissa E.; Ahmed, Zeshan; Parisi, Joseph E.; Yen, Shu-Hui C.; Dickson, Dennis W.

2013-01-01

CBD is a disorder affecting cognition and movement due to a progressive neurodegeneration associated with distinctive neuropathologic features, including abnormal phosphorylated tau protein in neurons and glia in cortex, basal ganglia, diencephalon and brainstem, as well as ballooned neurons and astrocytic plaques. We identified three cases of CBD with olivopontocerebellar atrophy (CBD-OPCA) that did not have α-synuclein-positive glial cytoplasmic inclusions of multiple system atrophy (MSA). Two patients had clinical features suggestive of progressive supranuclear palsy (PSP), and the third case had cerebellar ataxia thought to be due to idiopathic OPCA. Neuropathologic features of CBD-OPCA are compared to typical CBD, as well as MSA and PSP. CBD-OPCA and MSA had marked neuronal loss in pontine nuclei, inferior olivary nucleus, and Purkinje cell layer. Neuronal loss and grumose degeneration in the cerebellar dentate nucleus was comparable in CBD-OPCA and PSP. Image analysis of tau pathology showed greater infratentorial tau burden, especially in pontine base, in CBD-OPCA compared with typical CBD. Additionally, CBD-OPCA had TDP-43 immunoreactive neuronal and glial cytoplasmic inclusions and threads throughout the basal ganglia and in olivopontocerebellar system. CBD-OPCA met neuropathologic research diagnostic criteria for CBD and shared tau biochemical characteristics with typical CBD. These results suggest that CBD-OPCA is a distinct clinicopathologic variant of CBD with olivopontocerebellar TDP-43 pathology. PMID:23371366

Corticobasal degeneration with olivopontocerebellar atrophy and TDP-43 pathology: an unusual clinicopathologic variant of CBD.

PubMed

Kouri, Naomi; Oshima, Kenichi; Takahashi, Makio; Murray, Melissa E; Ahmed, Zeshan; Parisi, Joseph E; Yen, Shu-Hui C; Dickson, Dennis W

2013-05-01

Corticobasal degeneration (CBD) is a disorder affecting cognition and movement due to a progressive neurodegeneration associated with distinctive neuropathologic features, including abnormal phosphorylated tau protein in neurons and glia in cortex, basal ganglia, diencephalon, and brainstem, as well as ballooned neurons and astrocytic plaques. We identified three cases of CBD with olivopontocerebellar atrophy (CBD-OPCA) that did not have α-synuclein-positive glial cytoplasmic inclusions of multiple system atrophy (MSA). Two patients had clinical features suggestive of progressive supranuclear palsy (PSP), and the third case had cerebellar ataxia thought to be due to idiopathic OPCA. Neuropathologic features of CBD-OPCA are compared to typical CBD, as well as MSA and PSP. CBD-OPCA and MSA had marked neuronal loss in pontine nuclei, inferior olivary nucleus, and Purkinje cell layer. Neuronal loss and grumose degeneration in the cerebellar dentate nucleus were comparable in CBD-OPCA and PSP. Image analysis of tau pathology showed greater infratentorial tau burden, especially in pontine base, in CBD-OPCA compared with typical CBD. In addition, CBD-OPCA had TDP-43 immunoreactive neuronal and glial cytoplasmic inclusions and threads throughout the basal ganglia and in olivopontocerebellar system. CBD-OPCA met neuropathologic research diagnostic criteria for CBD and shared tau biochemical characteristics with typical CBD. These results suggest that CBD-OPCA is a distinct clinicopathologic variant of CBD with olivopontocerebellar TDP-43 pathology.

Coronal Bright Points Associated with Minifilament Eruptions

NASA Astrophysics Data System (ADS)

Hong, Junchao; Jiang, Yunchun; Yang, Jiayan; Bi, Yi; Li, Haidong; Yang, Bo; Yang, Dan

2014-12-01

Coronal bright points (CBPs) are small-scale, long-lived coronal brightenings that always correspond to photospheric network magnetic features of opposite polarity. In this paper, we subjectively adopt 30 CBPs in a coronal hole to study their eruptive behavior using data from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory. About one-quarter to one-third of the CBPs in the coronal hole go through one or more minifilament eruption(s) (MFE(s)) throughout their lifetimes. The MFEs occur in temporal association with the brightness maxima of CBPs and possibly result from the convergence and cancellation of underlying magnetic dipoles. Two examples of CBPs with MFEs are analyzed in detail, where minifilaments appear as dark features of a cool channel that divide the CBPs along the neutral lines of the dipoles beneath. The MFEs show the typical rising movements of filaments and mass ejections with brightenings at CBPs, similar to large-scale filament eruptions. Via differential emission measure analysis, it is found that CBPs are heated dramatically by their MFEs and the ejected plasmas in the MFEs have average temperatures close to the pre-eruption BP plasmas and electron densities typically near 109 cm-3. These new observational results indicate that CBPs are more complex in dynamical evolution and magnetic structure than previously thought.

Location and Geologic Setting for the Three U.S. Mars Landers

NASA Technical Reports Server (NTRS)

Parker, T. J.; Kirk, R. L.

1999-01-01

Super resolution of the horizon at both Viking landing sites has revealed "new" features we use for triangulation, similar to the approach used during the Mars Pathfinder Mission. We propose alternative landing site locations for both landers for which we believe the confidence is very high. Super resolution of VL-1 images also reveals some of the drift material at the site to consist of gravel-size deposits. Since our proposed location for VL-2 is NOT on the Mie ejecta blanket, the blocky surface around the lander may represent the meter-scale texture of "smooth palins" in the region. The Viking Lander panchromatic images typically offer more repeat coverage than does the IMP on Mars Pathfinder, due to the longer duration of these landed missions. Sub-pixel offsets, necessary for super resolution to work, appear to be attributable to thermal effects on the lander and settling of the lander over time. Due to the greater repeat coverage (particularly in the near and mid-fields) and all-panchromatic images, the gain in resolution by super resolution processing is better for Viking than it is with most IMP image sequences. This enhances the study of textural details near the lander and enables the identification rock and surface textures at greater distances from the lander. Discernment of stereo in super resolution im-ages is possible to great distances from the lander, but is limited by the non-rotating baseline between the two cameras and the shorter height of the cameras above the ground compared to IMP. With super resolution, details of horizon features, such as blockiness and crater rim shapes, may be better correlated with Orbiter images. A number of horizon features - craters and ridges - were identified at VL-1 during the misison, and a few hils and subtle ridges were identified at VL-2. We have added a few "new" horizon features for triangulation at the VL-2 landing site in Utopia Planitia. These features were used for independent triangulation with features visible in Viking Orbiter and MGS MOC images, though the actual location of VL-1 lies in a data dropout in the MOC image of the area. Additional information is contained in the original extended abstract.

Multi-dimension feature fusion for action recognition

NASA Astrophysics Data System (ADS)

Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

2018-04-01

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

PubMed

Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

2014-01-01

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

MRI for the detection of calcific features of vertebral haemangioma.

PubMed

Bender, Y Y; Böker, S M; Diederichs, G; Walter, T; Wagner, M; Fallenberg, E; Liebig, T; Rickert, M; Hamm, B; Makowski, M R

2017-08-01

To evaluate the diagnostic performance of susceptibility-weighted-magnetic-resonance imaging (SW-MRI) for the detection of vertebral haemangiomas (VHs) compared to T1/T2-weighted MRI sequences, radiographs, and computed tomography (CT). The study was approved by the local ethics review board. An SW-MRI sequence was added to the clinical spine imaging protocol. The image-based diagnosis of 56 VHs in 46 patients was established using T1/T2 MRI in combination with radiography/CT as the reference standard. VHs were assessed based on T1/T2-weighted MRI images alone and in combination with SW-MRI, while radiographs/CT images were excluded from the analysis. Fifty-one of 56 VHs could be identified on T1/T2 MRI images alone, if radiographs/CT images were excluded from analysis. In five cases (9.1%), additional radiographs/CT images were required for the imaging-based diagnosis. If T1/T2 and SW-MRI images were used in combination, all VHs could be diagnosed, without the need for radiography/CT. Size measurements revealed a close correlation between CT and SW-MRI (R 2 =0.94; p<0.05). This study demonstrates that SW-MRI enables reliable detection of the typical calcified features of VHs. This is of importance for routine MRI of the spine, as the use of additional CT/radiography can be minimized. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

A systematic approach to vertebral hemangioma.

PubMed

Gaudino, Simona; Martucci, Matia; Colantonio, Raffaella; Lozupone, Emilio; Visconti, Emiliano; Leone, Antonio; Colosimo, Cesare

2015-01-01

Vertebral hemangiomas (VHs) are a frequent and often incidental finding on computed tomography (CT) and magnetic resonance (MR) imaging of the spine. When their imaging appearance is "typical" (coarsened vertical trabeculae on radiographic and CT images, hyperintensity on T1- and T2-weighted MR images), the radiological diagnosis is straightforward. Nonetheless, VHs might also display an "atypical" appearance on MR imaging because of their histological features (amount of fat, vessels, and interstitial edema). Although the majority of VHs are asymptomatic and quiescent lesions, they can exhibit active behaviors, including growing quickly, extending beyond the vertebral body, and invading the paravertebral and/or epidural space with possible compression of the spinal cord and/or nerve roots ("aggressive" VHs). These "atypical" and "aggressive" VHs are a radiological challenge since they can mimic primary bony malignancies or metastases. CT plays a central role in the workup of atypical VHs, being the most appropriate imaging modality to highlight the polka-dot appearance that is representative of them. When aggressive VHs are suspected, both CT and MR are needed. MR is the best imaging modality to characterize the epidural and/or soft-tissue component, helping in the differential diagnosis. Angiography is a useful imaging adjunct for evaluating and even treating aggressive VHs. The primary objectives of this review article are to summarize the clinical, pathological, and imaging features of VHs, as well as the treatment options, and to provide a practical guide for the differential diagnosis, focusing on the rationale assessment of the findings from radiography, CT, and MR imaging.

The effect of graphics on environmental health risk beliefs, emotions, behavioral intentions, and recall.

PubMed

Severtson, Dolores J; Henriques, Jeffrey B

2009-11-01

Lay people have difficulty understanding the meaning of environmental health risk information. Visual images can use features that leverage visual perception capabilities and semiotic conventions to promote meaningful comprehension. Such evidence-based features were employed to develop two images of a color-coded visual scale to convey drinking water test results. The effect of these images and a typical alphanumeric (AN) lab report were explored in a repeated measures randomized trial among 261 undergraduates. Outcome measures included risk beliefs, emotions, personal safety threshold, mitigation intentions, the durability of beliefs and intentions over time, and test result recall. The plain image conveyed the strongest risk message overall, likely due to increased visual salience. The more detailed graded image conveyed a stronger message than the AN format only for females. Images only prompted meaningful risk reduction intentions among participants with optimistically biased safety threshold beliefs. Fuzzy trace theory supported some findings as follow. Images appeared to promote the consolidation of beliefs over time from an initial meaning of safety to an integrated meaning of safety and health risk; emotion potentially shaped this process. Although the AN report fostered more accurate recall, images were related to more appropriate beliefs and intentions at both time points. Findings hinted at the potential for images to prompt appropriate beliefs independent of accurate factual knowledge. Overall, results indicate that images facilitated meaningful comprehension of environmental health risk information and suggest foci for further research.

Abnormal brain magnetic resonance imaging in two patients with Smith-Magenis syndrome.

PubMed

Maya, Idit; Vinkler, Chana; Konen, Osnat; Kornreich, Liora; Steinberg, Tamar; Yeshaya, Josepha; Latarowski, Victoria; Shohat, Mordechai; Lev, Dorit; Baris, Hagit N

2014-08-01

Smith-Magenis syndrome (SMS) is a clinically recognizable contiguous gene syndrome ascribed to an interstitial deletion in chromosome 17p11.2. Seventy percent of SMS patients have a common deletion interval spanning 3.5 megabases (Mb). Clinical features of SMS include characteristic mild dysmorphic features, ocular anomalies, short stature, brachydactyly, and hypotonia. SMS patients have a unique neurobehavioral phenotype that includes intellectual disability, self-injurious behavior and severe sleep disturbance. Little has been reported in the medical literature about anatomical brain anomalies in patients with SMS. Here we describe two patients with SMS caused by the common deletion in 17p11.2 diagnosed using chromosomal microarray (CMA). Both patients had a typical clinical presentation and abnormal brain magnetic resonance imaging (MRI) findings. One patient had subependymal periventricular gray matter heterotopia, and the second had a thin corpus callosum, a thin brain stem and hypoplasia of the cerebellar vermis. This report discusses the possible abnormal MRI images in SMS and reviews the literature on brain malformations in SMS. Finally, although structural brain malformations in SMS patients are not a common feature, we suggest baseline routine brain imaging in patients with SMS in particular, and in patients with chromosomal microdeletion/microduplication syndromes in general. Structural brain malformations in these patients may affect the decision-making process regarding their management. © 2014 Wiley Periodicals, Inc.

Probing Hypergiant Mass Loss with Adaptive Optics Imaging and Polarimetry in the Infrared: MMT-Pol and LMIRCam Observations of IRC +10420 and VY Canis Majoris

NASA Astrophysics Data System (ADS)

Shenoy, Dinesh P.; Jones, Terry J.; Packham, Chris; Lopez-Rodriguez, Enrique

2015-07-01

We present 2-5 μm adaptive optics (AO) imaging and polarimetry of the famous hypergiant stars IRC +10420 and VY Canis Majoris. The imaging polarimetry of IRC +10420 with MMT-Pol at 2.2 μ {m} resolves nebular emission with intrinsic polarization of 30%, with a high surface brightness indicating optically thick scattering. The relatively uniform distribution of this polarized emission both radially and azimuthally around the star confirms previous studies that place the scattering dust largely in the plane of the sky. Using constraints on scattered light consistent with the polarimetry at 2.2 μ {m}, extrapolation to wavelengths in the 3-5 μm band predicts a scattered light component significantly below the nebular flux that is observed in our Large Binocular Telescope/LMIRCam 3-5 μm AO imaging. Under the assumption this excess emission is thermal, we find a color temperature of ˜500 K is required, well in excess of the emissivity-modified equilibrium temperature for typical astrophysical dust. The nebular features of VY CMa are found to be highly polarized (up to 60%) at 1.3 μm, again with optically thick scattering required to reproduce the observed surface brightness. This star’s peculiar nebular feature dubbed the “Southwest Clump” is clearly detected in the 3.1 μm polarimetry as well, which, unlike IRC +10420, is consistent with scattered light alone. The high intrinsic polarizations of both hypergiants’ nebulae are compatible with optically thick scattering for typical dust around evolved dusty stars, where the depolarizing effect of multiple scatters is mitigated by the grains’ low albedos. Observations reported here were obtained at the MMT Observatory, a joint facility of the Smithsonian Institution and the University of Arizona.

Anatomical background noise power spectrum in differential phase contrast breast images

NASA Astrophysics Data System (ADS)

Garrett, John; Ge, Yongshuai; Li, Ke; Chen, Guang-Hong

2015-03-01

In x-ray breast imaging, the anatomical noise background of the breast has a significant impact on the detection of lesions and other features of interest. This anatomical noise is typically characterized by a parameter, β, which describes a power law dependence of anatomical noise on spatial frequency (the shape of the anatomical noise power spectrum). Large values of β have been shown to reduce human detection performance, and in conventional mammography typical values of β are around 3.2. Recently, x-ray differential phase contrast (DPC) and the associated dark field imaging methods have received considerable attention as possible supplements to absorption imaging for breast cancer diagnosis. However, the impact of these additional contrast mechanisms on lesion detection is not yet well understood. In order to better understand the utility of these new methods, we measured the β indices for absorption, DPC, and dark field images in 15 cadaver breast specimens using a benchtop DPC imaging system. We found that the measured β value for absorption was consistent with the literature for mammographic acquisitions (β = 3.61±0.49), but that both DPC and dark field images had much lower values of β (β = 2.54±0.75 for DPC and β = 1.44±0.49 for dark field). In addition, visual inspection showed greatly reduced anatomical background in both DPC and dark field images. These promising results suggest that DPC and dark field imaging may help provide improved lesion detection in breast imaging, particularly for those patients with dense breasts, in whom anatomical noise is a major limiting factor in identifying malignancies.

Free-Form Region Description with Second-Order Pooling.

PubMed

Carreira, João; Caseiro, Rui; Batista, Jorge; Sminchisescu, Cristian

2015-06-01

Semantic segmentation and object detection are nowadays dominated by methods operating on regions obtained as a result of a bottom-up grouping process (segmentation) but use feature extractors developed for recognition on fixed-form (e.g. rectangular) patches, with full images as a special case. This is most likely suboptimal. In this paper we focus on feature extraction and description over free-form regions and study the relationship with their fixed-form counterparts. Our main contributions are novel pooling techniques that capture the second-order statistics of local descriptors inside such free-form regions. We introduce second-order generalizations of average and max-pooling that together with appropriate non-linearities, derived from the mathematical structure of their embedding space, lead to state-of-the-art recognition performance in semantic segmentation experiments without any type of local feature coding. In contrast, we show that codebook-based local feature coding is more important when feature extraction is constrained to operate over regions that include both foreground and large portions of the background, as typical in image classification settings, whereas for high-accuracy localization setups, second-order pooling over free-form regions produces results superior to those of the winning systems in the contemporary semantic segmentation challenges, with models that are much faster in both training and testing.

Fast processing of microscopic images using object-based extended depth of field.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

Image quality evaluation of full reference algorithm

NASA Astrophysics Data System (ADS)

He, Nannan; Xie, Kai; Li, Tong; Ye, Yushan

2018-03-01

Image quality evaluation is a classic research topic, the goal is to design the algorithm, given the subjective feelings consistent with the evaluation value. This paper mainly introduces several typical reference methods of Mean Squared Error(MSE), Peak Signal to Noise Rate(PSNR), Structural Similarity Image Metric(SSIM) and feature similarity(FSIM) of objective evaluation methods. The different evaluation methods are tested by Matlab, and the advantages and disadvantages of these methods are obtained by analyzing and comparing them.MSE and PSNR are simple, but they are not considered to introduce HVS characteristics into image quality evaluation. The evaluation result is not ideal. SSIM has a good correlation and simple calculation ,because it is considered to the human visual effect into image quality evaluation,However the SSIM method is based on a hypothesis,The evaluation result is limited. The FSIM method can be used for test of gray image and color image test, and the result is better. Experimental results show that the new image quality evaluation algorithm based on FSIM is more accurate.

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

tranSMART-XNAT Connector tranSMART-XNAT connector-image selection based on clinical phenotypes and genetic profiles.

PubMed

He, Sijin; Yong, May; Matthews, Paul M; Guo, Yike

2017-03-01

TranSMART has a wide range of functionalities for translational research and a large user community, but it does not support imaging data. In this context, imaging data typically includes 2D or 3D sets of magnitude data and metadata information. Imaging data may summarise complex feature descriptions in a less biased fashion than user defined plain texts and numeric numbers. Imaging data also is contextualised by other data sets and may be analysed jointly with other data that can explain features or their variation. Here we describe the tranSMART-XNAT Connector we have developed. This connector consists of components for data capture, organisation and analysis. Data capture is responsible for imaging capture either from PACS system or directly from an MRI scanner, or from raw data files. Data are organised in a similar fashion as tranSMART and are stored in a format that allows direct analysis within tranSMART. The connector enables selection and download of DICOM images and associated resources using subjects' clinical phenotypic and genotypic criteria. tranSMART-XNAT connector is written in Java/Groovy/Grails. It is maintained and available for download at https://github.com/sh107/transmart-xnat-connector.git. sijin@ebi.ac.uk. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

SPG11 Presenting with Tremor

PubMed Central

Schneider, Susanne A.; Mummery, Catherine J.; Mehrabian, Mohadeseh; Houlden, Henry; Bain, Peter G.

2012-01-01

Background Hereditary spastic paraplegias (HSPs) are a clinically and genetically heterogeneous group of neurological diseases, which typically present with progressive lower extremity weakness and spasticity causing progressive walking difficulties. Complicating neurological or extraneurological features may be present. Case Report We describe a 19-year-old male who was referred because of an action tremor of the hands; he later developed walking difficulties. Callosal atrophy was present on his cerebral magnetic resonance imaging scan, prompting genetic testing for SPG11, which revealed homozygous mutations. Discussion The clinical features, differential diagnosis and management of SPG11, the most common form of autosomal recessive complicated HSP with a thin corpus callosum are discussed. PMID:23439843

Investigating Mars: Kaiser Crater Dunes

NASA Image and Video Library

2018-02-02

This is a false color image of Kaiser Crater. In this combination of filters "blue" typically means basaltic sand. This VIS image crosses 3/4 of the crater and demonstrates how extensive the dunes are on the floor of Kaiser Crater. Kaiser Crater is located in the southern hemisphere in the Noachis region west of Hellas Planitia. Kaiser Crater is just one of several large craters with extensive dune fields on the crater floor. Other nearby dune filled craters are Proctor, Russell, and Rabe. Kaiser Crater is 207 km (129 miles) in diameter. The dunes are located in the southern part of the crater floor. The THEMIS VIS camera contains 5 filters. The data from different filters can be combined in multiple ways to create a false color image. These false color images may reveal subtle variations of the surface not easily identified in a single band image. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 71,000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 66602 Latitude: -47.0551 Longitude: 19.446 Instrument: VIS Captured: 2016-12-18 21:42 https://photojournal.jpl.nasa.gov/catalog/PIA22265

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

Automatic streak endpoint localization from the cornerness metric

NASA Astrophysics Data System (ADS)

Sease, Brad; Flewelling, Brien; Black, Jonathan

2017-05-01

Streaked point sources are a common occurrence when imaging unresolved space objects from both ground- and space-based platforms. Effective localization of streak endpoints is a key component of traditional techniques in space situational awareness related to orbit estimation and attitude determination. To further that goal, this paper derives a general detection and localization method for streak endpoints based on the cornerness metric. Corners detection involves searching an image for strong bi-directional gradients. These locations typically correspond to robust structural features in an image. In the case of unresolved imagery, regions with a high cornerness score correspond directly to the endpoints of streaks. This paper explores three approaches for global extraction of streak endpoints and applies them to an attitude and rate estimation routine.

A fast image simulation algorithm for scanning transmission electron microscopy.

PubMed

Ophus, Colin

2017-01-01

Image simulation for scanning transmission electron microscopy at atomic resolution for samples with realistic dimensions can require very large computation times using existing simulation algorithms. We present a new algorithm named PRISM that combines features of the two most commonly used algorithms, namely the Bloch wave and multislice methods. PRISM uses a Fourier interpolation factor f that has typical values of 4-20 for atomic resolution simulations. We show that in many cases PRISM can provide a speedup that scales with f 4 compared to multislice simulations, with a negligible loss of accuracy. We demonstrate the usefulness of this method with large-scale scanning transmission electron microscopy image simulations of a crystalline nanoparticle on an amorphous carbon substrate.

A fast image simulation algorithm for scanning transmission electron microscopy

DOE PAGES

Ophus, Colin

2017-05-10

Image simulation for scanning transmission electron microscopy at atomic resolution for samples with realistic dimensions can require very large computation times using existing simulation algorithms. Here, we present a new algorithm named PRISM that combines features of the two most commonly used algorithms, namely the Bloch wave and multislice methods. PRISM uses a Fourier interpolation factor f that has typical values of 4-20 for atomic resolution simulations. We show that in many cases PRISM can provide a speedup that scales with f 4 compared to multislice simulations, with a negligible loss of accuracy. We demonstrate the usefulness of this methodmore » with large-scale scanning transmission electron microscopy image simulations of a crystalline nanoparticle on an amorphous carbon substrate.« less

Linking brain, mind and behavior.

PubMed

Makeig, Scott; Gramann, Klaus; Jung, Tzyy-Ping; Sejnowski, Terrence J; Poizner, Howard

2009-08-01

Cortical brain areas and dynamics evolved to organize motor behavior in our three-dimensional environment also support more general human cognitive processes. Yet traditional brain imaging paradigms typically allow and record only minimal participant behavior, then reduce the recorded data to single map features of averaged responses. To more fully investigate the complex links between distributed brain dynamics and motivated natural behavior, we propose the development of wearable mobile brain/body imaging (MoBI) systems that continuously capture the wearer's high-density electrical brain and muscle signals, three-dimensional body movements, audiovisual scene and point of regard, plus new data-driven analysis methods to model their interrelationships. The new imaging modality should allow new insights into how spatially distributed brain dynamics support natural human cognition and agency.

Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Litjens, G. J. S.; Elliott, R.; Shih, N.; Feldman, M.; Barentsz, J. O.; Hulsbergen-van de Kaa, C. A.; Kovacs, I.; Huisman, H. J.; Madabhushi, A.

2014-03-01

Learning how to separate benign confounders from prostate cancer is important because the imaging characteristics of these confounders are poorly understood. Furthermore, the typical representations of the MRI parameters might not be enough to allow discrimination. The diagnostic uncertainty this causes leads to a lower diagnostic accuracy. In this paper a new cascaded classifier is introduced to separate prostate cancer and benign confounders on MRI in conjunction with specific computer-extracted features to distinguish each of the benign classes (benign prostatic hyperplasia (BPH), inflammation, atrophy or prostatic intra-epithelial neoplasia (PIN). In this study we tried to (1) calculate different mathematical representations of the MRI parameters which more clearly express subtle differences between different classes, (2) learn which of the MRI image features will allow to distinguish specific benign confounders from prostate cancer, and (2) find the combination of computer-extracted MRI features to best discriminate cancer from the confounding classes using a cascaded classifier. One of the most important requirements for identifying MRI signatures for adenocarcinoma, BPH, atrophy, inflammation, and PIN is accurate mapping of the location and spatial extent of the confounder and cancer categories from ex vivo histopathology to MRI. Towards this end we employed an annotated prostatectomy data set of 31 patients, all of whom underwent a multi-parametric 3 Tesla MRI prior to radical prostatectomy. The prostatectomy slides were carefully co-registered to the corresponding MRI slices using an elastic registration technique. We extracted texture features from the T2-weighted imaging, pharmacokinetic features from the dynamic contrast enhanced imaging and diffusion features from the diffusion-weighted imaging for each of the confounder classes and prostate cancer. These features were selected because they form the mainstay of clinical diagnosis. Relevant features for each of the classes were selected using maximum relevance minimum redundancy feature selection, allowing us to perform classifier independent feature selection. The selected features were then incorporated in a cascading classifier, which can focus on easier sub-tasks at each stage, leaving the more difficult classification tasks for later stages. Results show that distinct features are relevant for each of the benign classes, for example the fraction of extra-vascular, extra-cellular space in a voxel is a clear discriminator for inflammation. Furthermore, the cascaded classifier outperforms both multi-class and one-shot classifiers in overall accuracy for discriminating confounders from cancer: 0.76 versus 0.71 and 0.62.

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features

PubMed Central

Liu, Zhiya; Song, Xiaohong; Seger, Carol A.

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting. PMID:26274332

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features.

PubMed

Liu, Zhiya; Song, Xiaohong; Seger, Carol A

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting.

Reduced isothermal feature set for long wave infrared (LWIR) face recognition

NASA Astrophysics Data System (ADS)

Donoso, Ramiro; San Martín, Cesar; Hermosilla, Gabriel

2017-06-01

In this paper, we introduce a new concept in the thermal face recognition area: isothermal features. This consists of a feature vector built from a thermal signature that depends on the emission of the skin of the person and its temperature. A thermal signature is the appearance of the face to infrared sensors and is unique to each person. The infrared face is decomposed into isothermal regions that present the thermal features of the face. Each isothermal region is modeled as circles within a center representing the pixel of the image, and the feature vector is composed of a maximum radius of the circles at the isothermal region. This feature vector corresponds to the thermal signature of a person. The face recognition process is built using a modification of the Expectation Maximization (EM) algorithm in conjunction with a proposed probabilistic index to the classification process. Results obtained using an infrared database are compared with typical state-of-the-art techniques showing better performance, especially in uncontrolled acquisition conditions scenarios.

Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection

PubMed Central

Giakoumis, Dimitris; Drosou, Anastasios; Cipresso, Pietro; Tzovaras, Dimitrios; Hassapis, George; Gaggioli, Andrea; Riva, Giuseppe

2012-01-01

This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing. PMID:23028461

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

PubMed Central

Muhammad, Khan; Baik, Sung Wook

2017-01-01

In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods. PMID:28859140

Adenomyosis: from the sign to the diagnosis. Imaging, diagnostic pitfalls and differential diagnosis: a pictorial review.

PubMed

Valentini, A L; Speca, S; Gui, B; Soglia, G; Soglia, B G; Miccò, M; Bonomo, L

2011-12-01

Adenomyosis is a pathological gynaecological condition characterised by benign invasion of the endometrium into the myometrium. It is often misdiagnosed, or is not easily recognised, although it is responsible for disabling symptoms such as menorrhagia, abnormal uterine bleeding, dysmenorrhoea and infertility in premenopausal women. The aim of this pictorial review is to analyse the features of adenomyosis by illustrating the most usual and typical imaging patterns, along with the unusual appearances, seen in a vast array of gynaecological imaging modalities. The different findings of focal and diffuse adenomyosis along with the diagnostic limitations of ultrasound, hysterosalpingography and magnetic resonance imaging are described, as are the pitfalls and differential diagnosis with other pathological conditions that are often misdiagnosed as adenomyosis. The role of the different imaging modalities in planning appropriate treatment and their usefulness in monitoring therapy are also discussed.

Toward Dysfunctional Connectivity: A Review of Neuroimaging Findings in Pediatric Major Depressive Disorder

PubMed Central

Hulvershorn, Leslie; Cullen, Kathryn; Anand, Amit

2011-01-01

Child and adolescent psychiatric neuroimaging research typically lags behind similar advances in adult disorders. While the pediatric depression imaging literature is less developed, a recent surge in interest has created the need for a synthetic review of this work. Major findings from pediatric volumetric and functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and resting state functional connectivity studies converge to implicate a corticolimbic network of key areas that work together to mediate the task of emotion regulation. Imaging the brain of children and adolescents with unipolar depression began with volumetric studies of isolated brain regions that served to identify key prefrontal, cingulate and limbic nodes of depression-related circuitry elucidated from more recent advances in DTI and functional connectivity imaging. Systematic review of these studies preliminarily suggests developmental differences between findings in youth and adults, including prodromal neurobiological features, along with some continuity across development. PMID:21901425

Peroneal tendon pathology: Pre- and post-operative high resolution US and MR imaging.

PubMed

Kumar, Yogesh; Alian, Ali; Ahlawat, Shivani; Wukich, Dane K; Chhabra, Avneesh

2017-07-01

Peroneal tendon pathology is an important cause of lateral ankle pain and instability. Typical peroneal tendon disorders include tendinitis, tenosynovitis, partial and full thickness tendon tears, peroneal retinacular injuries, and tendon subluxations and dislocations. Surgery is usually indicated when conservative treatment fails. Familiarity with the peroneal tendon surgeries and expected postoperative imaging findings is essential for accurate assessment and to avoid diagnostic pitfalls. Cross-sectional imaging, especially ultrasound and MRI provide accurate pre-operative and post-operative evaluation of the peroneal tendon pathology. In this review article, the normal anatomy, clinical presentation, imaging features, pitfalls and commonly performed surgical treatments for peroneal tendon abnormalities will be reviewed. The role of dynamic ultrasound and kinematic MRI for the evaluation of peroneal tendons will be discussed. Normal and abnormal postsurgical imaging appearances will be illustrated. Copyright © 2017 Elsevier B.V. All rights reserved.

Filter Design and Performance Evaluation for Fingerprint Image Segmentation

PubMed Central

Thai, Duy Hoang; Huckemann, Stephan; Gottschlich, Carsten

2016-01-01

Fingerprint recognition plays an important role in many commercial applications and is used by millions of people every day, e.g. for unlocking mobile phones. Fingerprint image segmentation is typically the first processing step of most fingerprint algorithms and it divides an image into foreground, the region of interest, and background. Two types of error can occur during this step which both have a negative impact on the recognition performance: ‘true’ foreground can be labeled as background and features like minutiae can be lost, or conversely ‘true’ background can be misclassified as foreground and spurious features can be introduced. The contribution of this paper is threefold: firstly, we propose a novel factorized directional bandpass (FDB) segmentation method for texture extraction based on the directional Hilbert transform of a Butterworth bandpass (DHBB) filter interwoven with soft-thresholding. Secondly, we provide a manually marked ground truth segmentation for 10560 images as an evaluation benchmark. Thirdly, we conduct a systematic performance comparison between the FDB method and four of the most often cited fingerprint segmentation algorithms showing that the FDB segmentation method clearly outperforms these four widely used methods. The benchmark and the implementation of the FDB method are made publicly available. PMID:27171150

Automatic and adaptive heterogeneous refractive index compensation for light-sheet microscopy.

PubMed

Ryan, Duncan P; Gould, Elizabeth A; Seedorf, Gregory J; Masihzadeh, Omid; Abman, Steven H; Vijayaraghavan, Sukumar; Macklin, Wendy B; Restrepo, Diego; Shepherd, Douglas P

2017-09-20

Optical tissue clearing has revolutionized researchers' ability to perform fluorescent measurements of molecules, cells, and structures within intact tissue. One common complication to all optically cleared tissue is a spatially heterogeneous refractive index, leading to light scattering and first-order defocus. We designed C-DSLM (cleared tissue digital scanned light-sheet microscopy) as a low-cost method intended to automatically generate in-focus images of cleared tissue. We demonstrate the flexibility and power of C-DSLM by quantifying fluorescent features in tissue from multiple animal models using refractive index matched and mismatched microscope objectives. This includes a unique measurement of myelin tracks within intact tissue using an endogenous fluorescent reporter where typical clearing approaches render such structures difficult to image. For all measurements, we provide independent verification using standard serial tissue sectioning and quantification methods. Paired with advancements in volumetric image processing, C-DSLM provides a robust methodology to quantify sub-micron features within large tissue sections.Optical clearing of tissue has enabled optical imaging deeper into tissue due to significantly reduced light scattering. Here, Ryan et al. tackle first-order defocus, an artefact of a non-uniform refractive index, extending light-sheet microscopy to partially cleared samples.

Regional information guidance system based on hypermedia concept

NASA Astrophysics Data System (ADS)

Matoba, Hiroshi; Hara, Yoshinori; Kasahara, Yutako

1990-08-01

A regional information guidance system has been developed on an image workstation. Two main features of this system are hypermedia data structure and friendly visual interface realized by the full-color frame memory system. As the hypermedia data structure manages regional information such as maps, pictures and explanations of points of interest, users can retrieve those information one by one, next to next according to their interest change. For example, users can retrieve explanation of a picture through the link between pictures and text explanations. Users can also traverse from one document to another by using keywords as cross reference indices. The second feature is to utilize a full-color, high resolution and wide space frame memory for visual interface design. This frame memory system enables real-time operation of image data and natural scene representation. The system also provides half tone representing function which enables fade-in/out presentations. This fade-in/out functions used in displaying and erasing menu and image data, makes visual interface soft for human eyes. The system we have developed is a typical example of multimedia applications. We expect the image workstation will play an important role as a platform for multimedia applications.

Brain tumor classification using AFM in combination with data mining techniques.

PubMed

Huml, Marlene; Silye, René; Zauner, Gerald; Hutterer, Stephan; Schilcher, Kurt

2013-01-01

Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

Intramuscular Lipoma: A Review of the Literature

PubMed Central

McTighe, Shane; Chernev, Ivan

2014-01-01

Lipomas are the most common type of soft tissue mesenchymal tumors. They are typically located subcutaneously and consist of mature fatty tissue. When they occur under the enclosing fascia, they are called deep-seated lipomas. Infrequently, lipomas can arise inside the muscle and are called intramuscular lipomas. Intramuscular lipomas have been commonly investigated and categorized in the same group as other deep-seated and superficial lipomatous lesions. Their clinical, histological and imaging characteristics may resemble well-differentiated liposarcomas, further adding to the difficulties in the differential diagnosis. This article summarizes the available literature and describes the typical epidemiological, pathological and clinical features of intramuscular lipomas, as well as delineating their treatment and prognosis. PMID:25568733

IgG4-Related Disease Simulating Carcinoma Colon With Diffuse Peritoneal Carcinomatosis on 18F-FDG PET/CT.

PubMed

Vadi, Shelvin Kumar; Parihar, Ashwin Singh; Kumar, Rajender; Singh, Harmandeep; Mittal, Bhagwant Rai; Bal, Amanjit; Sinha, Saroj Kumar

2018-05-14

IgG4-related disease (IgG4-RD) continues to be a diagnostic challenge and a great mimicker of malignancies. We report here a case of young man who presented with subacute intestinal obstruction with initial imaging and clinical features suggestive of carcinoma colon. 18F-FDG PET/CT showed diffuse peritoneal carcinomatosis pattern typically seen with abdominal malignancies. However, the histopathology and the raised IgG4 levels diagnosed it to be IgG4-RD. Although 18F-FDG PET/CT has typical patterns corresponding to the multisystemic involvement of IgG4-RD, the index case did not show any such findings.

Typical Neural Representations of Action Verbs Develop without Vision

PubMed Central

Caramazza, A.; Pascual-Leone, A.; Saxe, R.

2012-01-01

Many empiricist theories hold that concepts are composed of sensory–motor primitives. For example, the meaning of the word “run” is in part a visual image of running. If action concepts are partly visual, then the concepts of congenitally blind individuals should be altered in that they lack these visual features. We compared semantic judgments and neural activity during action verb comprehension in congenitally blind and sighted individuals. Participants made similarity judgments about pairs of nouns and verbs that varied in the visual motion they conveyed. Blind adults showed the same pattern of similarity judgments as sighted adults. We identified the left middle temporal gyrus (lMTG) brain region that putatively stores visual–motion features relevant to action verbs. The functional profile and location of this region was identical in sighted and congenitally blind individuals. Furthermore, the lMTG was more active for all verbs than nouns, irrespective of visual–motion features. We conclude that the lMTG contains abstract representations of verb meanings rather than visual–motion images. Our data suggest that conceptual brain regions are not altered by the sensory modality of learning. PMID:21653285

[Destructive mastoiditis with thrombosis of the sigmoid sinus in a 8 year-old child presenting with concomitant chicken pox].

PubMed

Bogomil'skiĭ, M R; Polunin, M M; Ivanenko, A M; Poliakov, A A

2014-01-01

The specific clinical feature of mastoidities that developed in a patient presenting with chicken pox was the rapid progress in temporal bone destruction with partial thrombosis of the sigmoid sinusis in the absence of typical manifestations of mastoiditis. The pronounced destructive changes found in a series of CT images were regarded as the indications for urgent antromastoidotomy with the puncture of the sigmoid sinusis.

Classification of motor intent in transradial amputees using sonomyography and spatio-temporal image analysis

NASA Astrophysics Data System (ADS)

Hariharan, Harishwaran; Aklaghi, Nima; Baker, Clayton A.; Rangwala, Huzefa; Kosecka, Jana; Sikdar, Siddhartha

2016-04-01

In spite of major advances in biomechanical design of upper extremity prosthetics, these devices continue to lack intuitive control. Conventional myoelectric control strategies typically utilize electromyography (EMG) signal amplitude sensed from forearm muscles. EMG has limited specificity in resolving deep muscle activity and poor signal-to-noise ratio. We have been investigating alternative control strategies that rely on real-time ultrasound imaging that can overcome many of the limitations of EMG. In this work, we present an ultrasound image sequence classification method that utilizes spatiotemporal features to describe muscle activity and classify motor intent. Ultrasound images of the forearm muscles were obtained from able-bodied subjects and a trans-radial amputee while they attempted different hand movements. A grid-based approach is used to test the feasibility of using spatio-temporal features by classifying hand motions performed by the subjects. Using the leave-one-out cross validation on image sequences acquired from able-bodied subjects, we observe that the grid-based approach is able to discern four hand motions with 95.31% accuracy. In case of the trans-radial amputee, we are able to discern three hand motions with 80% accuracy. In a second set of experiments, we study classification accuracy by extracting spatio-temporal sub-sequences the depict activity due to the motion of local anatomical interfaces. Short time and space limited cuboidal sequences are initially extracted and assigned an optical flow behavior label, based on a response function. The image space is clustered based on the location of cuboids and features calculated from the cuboids in each cluster. Using sequences of known motions, we extract feature vectors that describe said motion. A K-nearest neighbor classifier is designed for classification experiments. Using the leave-one-out cross validation on image sequences for an amputee subject, we demonstrate that the classifier is able to discern three important hand motions with an accuracy of 93.33% accuracy, 91-100% precision and 80-100% recall rate. We anticipate that ultrasound imaging based methods will address some limitations of conventional myoelectric sensing, while adding advantages inherent to ultrasound imaging.

Remote Sensing Image Analysis Without Expert Knowledge - A Web-Based Classification Tool On Top of Taverna Workflow Management System

NASA Astrophysics Data System (ADS)

Selsam, Peter; Schwartze, Christian

2016-10-01

Providing software solutions via internet has been known for quite some time and is now an increasing trend marketed as "software as a service". A lot of business units accept the new methods and streamlined IT strategies by offering web-based infrastructures for external software usage - but geospatial applications featuring very specialized services or functionalities on demand are still rare. Originally applied in desktop environments, the ILMSimage tool for remote sensing image analysis and classification was modified in its communicating structures and enabled for running on a high-power server and benefiting from Tavema software. On top, a GIS-like and web-based user interface guides the user through the different steps in ILMSimage. ILMSimage combines object oriented image segmentation with pattern recognition features. Basic image elements form a construction set to model for large image objects with diverse and complex appearance. There is no need for the user to set up detailed object definitions. Training is done by delineating one or more typical examples (templates) of the desired object using a simple vector polygon. The template can be large and does not need to be homogeneous. The template is completely independent from the segmentation. The object definition is done completely by the software.

Earth Observation taken by the Expedition 20 crew

NASA Image and Video Library

2009-07-15

ISS020-E-021140 (15 July 2009) --- Teide Volcano on the Canary Islands of Spain is featured in this image photographed by an Expedition 20 crew member on the International Space Station. This detailed photograph features two stratovolcanoes ? Pico de Teide and Pico Viejo ? located on Tenerife Island, part of the Canary Islands of Spain. Stratovolcanoes are steep-sided; typically conical structures formed by interlayered lavas and fragmented rock material from explosive eruptions. Pico de Teide has a relatively sharp peak, whereas an explosion crater forms the summit of Pico Viejo. The two stratovolcanoes formed within an even larger volcanic structure known as the Las Ca?adas caldera ? a large collapse depression typically formed when a major eruption completely empties the underlying magma chamber of a volcano. The last eruption of Teide occurred in 1909. NASA scientists point out sinuous flow levees marking individual lava flows. The scientists consider the flow levees as perhaps the most striking volcanic features visible in the image. Flow levees are formed when the outer edges of a channelized lava flow cool and harden while the still-molten interior continues to flow downhill ? numerous examples radiate outwards from the peaks of both Pico de Teide and Pico Viejo. Brown to tan overlapping lava flows and domes are visible to the east-southeast of the Teide stratovolcano. Increased seismicity, carbon dioxide emissions, and fumarolic activity within the Las Ca?adas caldera and along the northwestern flanks of the volcano were observed in 2004. Monitoring of the volcano to detect renewal of activity is ongoing.

Fretted Terrain Valleys

NASA Technical Reports Server (NTRS)

2004-01-01

30 October 2004 This Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image shows shallow tributary valleys in the Ismenius Lacus fretted terrain region of northern Arabia Terra. These valleys exhibit a variety of typical fretted terrain valley wall and floor textures, including a lineated, pitted material somewhat reminiscent of the surface of a brain. Origins for these features are still being debated within the Mars science community; there are no clear analogs to these landforms on Earth. This image is located near 39.9oN, 332.1oW. The picture covers an area about 3 km (1.9 mi) wide. Sunlight illuminates the scene from the lower left.

An efficient approach to imaging underground hydraulic networks

NASA Astrophysics Data System (ADS)

Kumar, Mohi

2012-07-01

To better locate natural resources, treat pollution, and monitor underground networks associated with geothermal plants, nuclear waste repositories, and carbon dioxide sequestration sites, scientists need to be able to accurately characterize and image fluid seepage pathways below ground. With these images, scientists can gain knowledge of soil moisture content, the porosity of geologic formations, concentrations and locations of dissolved pollutants, and the locations of oil fields or buried liquid contaminants. Creating images of the unknown hydraulic environments underfoot is a difficult task that has typically relied on broad extrapolations from characteristics and tests of rock units penetrated by sparsely positioned boreholes. Such methods, however, cannot identify small-scale features and are very expensive to reproduce over a broad area. Further, the techniques through which information is extrapolated rely on clunky and mathematically complex statistical approaches requiring large amounts of computational power.

Diagnosis of a sigmoid volvulus in pregnancy: ultrasonography and magnetic resonance imaging findings

PubMed Central

Palmucci, Stefano; Lanza, Maria Letizia; Gulino, Fabrizio; Scilletta, Beniamino; Ettorre, Giovanni Carlo

2014-01-01

Sigmoid volvulus complicating pregnancy is a rare, non-obstetric cause of abdominal pain that requires prompt surgical intervention (decompression) to avoid intestinal ischemia and perforation. We report the case of a 31-week pregnant woman with abdominal pain and subsequent development of constipation. Preoperative diagnosis was achieved using magnetic resonance imaging and ultrasonography: the large bowel distension and a typical whirl sign - near a sigmoid colon transition point - suggested the diagnosis of sigmoid volvulus. The decision to refer the patient for emergency laparotomy was adopted without any ionizing radiation exposure, and the pre-operative diagnosis was confirmed after surgery. Imaging features of sigmoid volvulus and differential diagnosis from other non-obstetric abdominal emergencies in pregnancy are discussed in our report, with special emphasis on the diagnostic capabilities of ultrasonography and magnetic resonance imaging. PMID:24967020

Diagnosis of a sigmoid volvulus in pregnancy: ultrasonography and magnetic resonance imaging findings.

PubMed

Palmucci, Stefano; Lanza, Maria Letizia; Gulino, Fabrizio; Scilletta, Beniamino; Ettorre, Giovanni Carlo

2014-02-01

Sigmoid volvulus complicating pregnancy is a rare, non-obstetric cause of abdominal pain that requires prompt surgical intervention (decompression) to avoid intestinal ischemia and perforation. We report the case of a 31-week pregnant woman with abdominal pain and subsequent development of constipation. Preoperative diagnosis was achieved using magnetic resonance imaging and ultrasonography: the large bowel distension and a typical whirl sign - near a sigmoid colon transition point - suggested the diagnosis of sigmoid volvulus. The decision to refer the patient for emergency laparotomy was adopted without any ionizing radiation exposure, and the pre-operative diagnosis was confirmed after surgery. Imaging features of sigmoid volvulus and differential diagnosis from other non-obstetric abdominal emergencies in pregnancy are discussed in our report, with special emphasis on the diagnostic capabilities of ultrasonography and magnetic resonance imaging.

Investigating Mars: Rabe Crater

NASA Image and Video Library

2017-12-20

This is a false color image of Rabe Crater. In this combination of filters "blue" typically means basaltic sand. Rabe Crater is 108 km (67 miles) across. Craters of similar size often have flat floors. Rabe Crater has some areas of flat floor, but also has a large complex pit occupying a substantial part of the floor. The interior fill of the crater is thought to be layered sediments created by wind and or water action. The pit is eroded into this material. The eroded materials appear to have stayed within the crater forming a large sand sheet with surface dune forms as well as individual dunes where the crater floor is visible. The dunes also appear to be moving from the upper floor level into the pit. The THEMIS VIS camera contains 5 filters. The data from different filters can be combined in multiple ways to create a false color image. These false color images may reveal subtle variations of the surface not easily identified in a single band image. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 52231 Latitude: -43.6665 Longitude: 34.2627 Instrument: VIS Captured: 2013-09-22 14:29 https://photojournal.jpl.nasa.gov/catalog/PIA22146

Investigating Mars: Rabe Crater

NASA Image and Video Library

2017-12-22

This is a false color image of Rabe Crater. In this combination of filters "blue" typically means basaltic sand. Rabe Crater is 108 km (67 miles) across. Craters of similar size often have flat floors. Rabe Crater has some areas of flat floor, but also has a large complex pit occupying a substantial part of the floor. The interior fill of the crater is thought to be layered sediments created by wind and or water action. The pit is eroded into this material. The eroded materials appear to have stayed within the crater forming a large sand sheet with surface dune forms as well as individual dunes where the crater floor is visible. The dunes also appear to be moving from the upper floor level into the pit. The THEMIS VIS camera contains 5 filters. The data from different filters can be combined in multiple ways to create a false color image. These false color images may reveal subtle variations of the surface not easily identified in a single band image. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 67144 Latitude: -43.5512 Longitude: 34.5951 Instrument: VIS Captured: 2017-02-01 12:57 https://photojournal.jpl.nasa.gov/catalog/PIA22148

Investigating Mars: Rabe Crater

NASA Image and Video Library

2017-12-19

This is a false color image of Rabe Crater. In this combination of filters "blue" typically means basaltic sand. Rabe Crater is 108 km (67 miles) across. Craters of similar size often have flat floors. Rabe Crater has some areas of flat floor, but also has a large complex pit occupying a substantial part of the floor. The interior fill of the crater is thought to be layered sediments created by wind and or water action. The pit is eroded into this material. The eroded materials appear to have stayed within the crater forming a large sand sheet with surface dune forms as well as individual dunes where the crater floor is visible. The dunes also appear to be moving from the upper floor level into the pit. The THEMIS VIS camera contains 5 filters. The data from different filters can be combined in multiple ways to create a false color image. These false color images may reveal subtle variations of the surface not easily identified in a single band image. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 51157 Latitude: -43.6787 Longitude: 34.3985 Instrument: VIS Captured: 2013-06-26 05:33 https://photojournal.jpl.nasa.gov/catalog/PIA22145

True color scanning laser ophthalmoscopy and optical coherence tomography handheld probe

PubMed Central

LaRocca, Francesco; Nankivil, Derek; Farsiu, Sina; Izatt, Joseph A.

2014-01-01

Scanning laser ophthalmoscopes (SLOs) are able to achieve superior contrast and axial sectioning capability compared to fundus photography. However, SLOs typically use monochromatic illumination and are thus unable to extract color information of the retina. Previous color SLO imaging techniques utilized multiple lasers or narrow band sources for illumination, which allowed for multiple color but not “true color” imaging as done in fundus photography. We describe the first “true color” SLO, handheld color SLO, and combined color SLO integrated with a spectral domain optical coherence tomography (OCT) system. To achieve accurate color imaging, the SLO was calibrated with a color test target and utilized an achromatizing lens when imaging the retina to correct for the eye’s longitudinal chromatic aberration. Color SLO and OCT images from volunteers were then acquired simultaneously with a combined power under the ANSI limit. Images from this system were then compared with those from commercially available SLOs featuring multiple narrow-band color imaging. PMID:25401032

Simulating Photo-Refraction Images of Keratoconus and Near-Sightedness Eyes

NASA Astrophysics Data System (ADS)

Baker, Kevin; Lewis, James W. L.; Chen, Ying-Ling

2004-11-01

Keratoconus is an abnormal condition of the eye resulting from cone-shaped features on the cornea that degrade the quality of vision. These corneal features result from thinning and subsequent bulging due to intraocular pressure. The abnormal corneal curvature increases the refractive power asymmetrically and can be misdiagnosed by examiners as astigmatism and nearsightedness. Since corrective treatment is possible, early detection of this condition is desirable. Photo-refraction (PR) detects the retinal irradiance reflected from a single light source and is an inexpensive method used to identify refractive errors. For near- (far-) sighted eye, a crescent appears on the same (opposite) side of the light source. The capability of a PR device to detect keratoconus and to differentiate this condition from myopia was investigated. Using a commercial optical program, synthetic eye models were constructed for both near-sighted and keratoconus eyes. PR images of various eye conditions were calculated. The keratoconus cone shapes were modeled with typical published cone locations and sizes. The results indicate significant differences between the images of keratoconus and near-sighted eyes.

Upper crustal structure of central Java, Indonesia, from transdimensional seismic ambient noise tomography

NASA Astrophysics Data System (ADS)

Zulfakriza, Z.; Saygin, E.; Cummins, P. R.; Widiyantoro, S.; Nugraha, A. D.; Lühr, B.-G.; Bodin, T.

2014-04-01

Delineating the crustal structure of central Java is crucial for understanding its complex tectonic setting. However, seismic imaging of the strong heterogeneity typical of such a tectonically active region can be challenging, particularly in the upper crust where velocity contrasts are strongest and steep body wave ray paths provide poor resolution. To overcome these difficulties, we apply the technique of ambient noise tomography (ANT) to data collected during the Merapi Amphibious Experiment (MERAMEX), which covered central Java with a temporary deployment of over 120 seismometers during 2004 May-October. More than 5000 Rayleigh wave Green's functions were extracted by cross-correlating the noise simultaneously recorded at available station pairs. We applied a fully non-linear 2-D Bayesian probabilistic inversion technique to the retrieved traveltimes. Features in the derived tomographic images correlate well with previous studies, and some shallow structures that were not evident in previous studies are clearly imaged with ANT. The Kendeng Basin and several active volcanoes appear with very low group velocities, and anomalies with relatively high velocities can be interpreted in terms of crustal sutures and/or surface geological features.

Characteristic optical coherence tomography findings in patients with primary vitreoretinal lymphoma: a novel aid to early diagnosis.

PubMed

Barry, Robert J; Tasiopoulou, Anastasia; Murray, Philip I; Patel, Praveen J; Sagoo, Mandeep S; Denniston, Alastair K; Keane, Pearse A

2018-01-06

The diagnosis of primary vitreoretinal lymphoma (PVRL) poses significant difficulties; presenting features are non-specific and confirmation usually necessitates invasive vitreoretinal biopsy. Diagnosis is often delayed, resulting in increased morbidity and mortality. Non-invasive imaging modalities such as spectral domain optical coherence tomography (SD-OCT) offer simple and rapid aids to diagnosis. We present characteristic SD-OCT images of patients with biopsy-positive PVRL and propose a number of typical features, which we believe are useful in identifying these lesions at an early stage. Medical records of all patients attending Moorfields Eye Hospital between April 2010 and April 2016 with biopsy-positive PVRL were reviewed. Pretreatment SD-OCT images were collected for all eyes and were reviewed independently by two researchers for features suggestive of PVRL. Pretreatment SD-OCT images of 32 eyes of 22 patients with biopsy-proven PVRL were reviewed. Observed features included hyper-reflective subretinal infiltrates (17/32), hyper-reflective infiltration in inner retinal layers (6/32), retinal pigment epithelium (RPE) undulation (5/32), clumps of vitreous cells (5/32) and sub-RPE deposits (3/32). Of these, the hyper-reflective subretinal infiltrates have an appearance unique to PVRL, with features not seen in other diseases. We have identified a range of SD-OCT features, which we believe to be consistent with a diagnosis of PVRL. We propose that the observation of hyper-reflective subretinal infiltrates as described is highly suggestive of PVRL. This case series further demonstrates the utility of SD-OCT as a non-invasive and rapid aid to diagnosis, which may improve both visual outcomes and survival of patients with intraocular malignancies such as PVRL. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study

NASA Astrophysics Data System (ADS)

Oweis, Khalid J.; Berl, Madison M.; Gaillard, William D.; Duke, Elizabeth S.; Blackstone, Kaitlin; Loew, Murray H.; Zara, Jason M.

2010-03-01

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p<0.05) between patient and control groups

The structure and rainfall features of Tropical Cyclone Rammasun (2002)

NASA Astrophysics Data System (ADS)

Ma, Leiming; Duan, Yihong; Zhu, Yongti

2004-12-01

Tropical Rainfall Measuring Mission (TRMM) data [TRMM Microwave Imager/Precipitation Radar/Visible and Infrared Scanner (TMI/PR/VIRS)] and a numerical model are used to investigate the structure and rainfall features of Tropical Cyclone (TC) Rammasun (2002). Based on the analysis of TRMM data, which are diagnosed together with NCEP/AVN [Aviation (global model)] analysis data, some typical features of TC structure and rainfall are preliminary discovered. Since the limitations of TRMM data are considered for their time resolution and coverage, the world observed by TRMM at several moments cannot be taken as the representation of the whole period of the TC lifecycle, therefore the picture should be reproduced by a numerical model of high quality. To better understand the structure and rainfall features of TC Rammasun, a numerical simulation is carried out with mesoscale model MM5 in which the validations have been made with the data of TRMM and NCEP/AVN analysis.

Fault-tolerant feature-based estimation of space debris rotational motion during active removal missions

NASA Astrophysics Data System (ADS)

Biondi, Gabriele; Mauro, Stefano; Pastorelli, Stefano; Sorli, Massimo

2018-05-01

One of the key functionalities required by an Active Debris Removal mission is the assessment of the target kinematics and inertial properties. Passive sensors, such as stereo cameras, are often included in the onboard instrumentation of a chaser spacecraft for capturing sequential photographs and for tracking features of the target surface. A plenty of methods, based on Kalman filtering, are available for the estimation of the target's state from feature positions; however, to guarantee the filter convergence, they typically require continuity of measurements and the capability of tracking a fixed set of pre-defined features of the object. These requirements clash with the actual tracking conditions: failures in feature detection often occur and the assumption of having some a-priori knowledge about the shape of the target could be restrictive in certain cases. The aim of the presented work is to propose a fault-tolerant alternative method for estimating the angular velocity and the relative magnitudes of the principal moments of inertia of the target. Raw data regarding the positions of the tracked features are processed to evaluate corrupted values of a 3-dimentional parameter which entirely describes the finite screw motion of the debris and which primarily is invariant on the particular set of considered features of the object. Missing values of the parameter are completely restored exploiting the typical periodicity of the rotational motion of an uncontrolled satellite: compressed sensing techniques, typically adopted for recovering images or for prognostic applications, are herein used in a completely original fashion for retrieving a kinematic signal that appears sparse in the frequency domain. Due to its invariance about the features, no assumptions are needed about the target's shape and continuity of the tracking. The obtained signal is useful for the indirect evaluation of an attitude signal that feeds an unscented Kalman filter for the estimation of the global rotational state of the target. The results of the computer simulations showed a good robustness of the method and its potential applicability for general motion conditions of the target.

Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy

NASA Astrophysics Data System (ADS)

Yi, Faliu; Moon, Inkyu; Lee, Yeon H.

2015-01-01

Counting morphologically normal cells in human red blood cells (RBCs) is extremely beneficial in the health care field. We propose a three-dimensional (3-D) classification method of automatically determining the morphologically normal RBCs in the phase image of multiple human RBCs that are obtained by off-axis digital holographic microscopy (DHM). The RBC holograms are first recorded by DHM, and then the phase images of multiple RBCs are reconstructed by a computational numerical algorithm. To design the classifier, the three typical RBC shapes, which are stomatocyte, discocyte, and echinocyte, are used for training and testing. Nonmain or abnormal RBC shapes different from the three normal shapes are defined as the fourth category. Ten features, including projected surface area, average phase value, mean corpuscular hemoglobin, perimeter, mean corpuscular hemoglobin surface density, circularity, mean phase of center part, sphericity coefficient, elongation, and pallor, are extracted from each RBC after segmenting the reconstructed phase images by using a watershed transform algorithm. Moreover, four additional properties, such as projected surface area, perimeter, average phase value, and elongation, are measured from the inner part of each cell, which can give significant information beyond the previous 10 features for the separation of the RBC groups; these are verified in the experiment by the statistical method of Hotelling's T-square test. We also apply the principal component analysis algorithm to reduce the dimension number of variables and establish the Gaussian mixture densities using the projected data with the first eight principal components. Consequently, the Gaussian mixtures are used to design the discriminant functions based on Bayesian decision theory. To improve the performance of the Bayes classifier and the accuracy of estimation of its error rate, the leaving-one-out technique is applied. Experimental results show that the proposed method can yield good results for calculating the percentage of each typical normal RBC shape in a reconstructed phase image of multiple RBCs that will be favorable to the analysis of RBC-related diseases. In addition, we show that the discrimination performance for the counting of normal shapes of RBCs can be improved by using 3-D features of an RBC.

Perinatal findings of Seckel syndrome: a case report of a fetus showing primordial dwarfism and severe microcephaly.

PubMed

Takikawa, Keiko Miyachi; Kikuchi, Akihiko; Yokoyama, Akiko; Ono, Kyoko; Iwasawa, Yuki; Sunagawa, Sorahiro; Takagi, Kimiyo; Kawame, Hiroshi; Nakamura, Tomohiko

2008-01-01

Seckel syndrome is a rare form of primordial dwarfism and most of the previous reports have been limited to postnatal findings. We report on a fetus showing severe microcephaly, intrauterine growth restriction and a few gyri with shallow sulci on the fetal brain suggesting cortical dysplasia, followed by ultrasound and magnetic resonance imaging in the prenatal period. Cardiotocograph revealed a reassuring fetal status throughout the whole pregnancy period. A male infant weighing 1,556 g was delivered at 39 weeks' gestation, and a diagnosis of Seckel syndrome was made based on postnatal typical findings. Although previous reports on prenatal findings of Seckel syndrome are quite limited, we think that our case presents typical features of a fetus affected by this syndrome. When prenatal ultrasound shows severe microcephaly and intrauterine growth restriction, this rare syndrome should be included in the differential diagnosis. Moreover, magnetic resonance imaging of the affected fetal brain provides further diagnostic clues. Copyright 2008 S. Karger AG, Basel.

On the Viability of Using Autonomous Three-Component Nodal Geophones to Calculate Teleseismic Ps Receiver Functions with an Application to the Old Faithful Hydrothermal System and the Cascadia Subduction Zone

NASA Astrophysics Data System (ADS)

Ward, K. M.; Lin, F. C.

2017-12-01

Recent advances in seismic data-acquisition technology paired with an increasing interest from the academic passive source seismological community have opened up new scientific targets and imaging possibilities, often referred to as Large-N experiments (large number of instruments). The success of these and other deployments has motivated individual researchers, as well as the larger seismological community, to invest in the next generation of nodal geophones. Although the new instruments have battery life and bandwidth limitations compared to broadband instruments, the relatively low deployment and procurement cost of these new nodal geophones provides an additional novel tool for researchers. Here, we explore the viability of using autonomous three-component nodal geophones to calculate teleseismic Ps receiver functions by comparison of co-located broadband stations and highlight some potential advantages with a dense nodal array deployed around the Upper Geyser basin in Yellowstone National Park. Two key findings from this example include (1) very dense nodal arrays can be used to image small-scale features in the shallow crust that typical broadband station spacing would alias, and (2) nodal arrays with a larger footprint could be used to image deeper features with greater or equal detail as typical broadband deployments but at a reduced deployment cost. The success of the previous example has motivated a larger 2-D line across the Cascadia subduction zone. In the summer of 2017, we deployed 174 nodal geophones with an average site spacing of 750 m. Synthetic tests with dense station spacing ( 1 km) reveal subtler features of the system that is consistent with our preliminary receiver function results from our Cascadia deployment. With the increasing availability of nodal geophones to individual researchers and the successful demonstration that nodal geophones are a viable instrument for receiver function studies, numerous scientific targets can be investigated at reduced costs or in expanded detail.

Utility of fat-suppressed sequences in differentiation of aggressive vs typical asymptomatic haemangioma of the spine

PubMed Central

Nabavizadeh, Seyed Ali; Mamourian, Alexander; Schmitt, James E; Cloran, Francis; Vossough, Arastoo; Pukenas, Bryan; Loevner, Laurie A

2016-01-01

Objective: While haemangiomas are common benign vascular lesions involving the spine, some behave in an aggressive fashion. We investigated the utility of fat-suppressed sequences to differentiate between benign and aggressive vertebral haemangiomas. Methods: Patients with the diagnosis of aggressive vertebral haemangioma and available short tau inversion-recovery or T2 fat saturation sequence were included in the study. 11 patients with typical asymptomatic vertebral body haemangiomas were selected as the control group. Region of interest signal intensity (SI) analysis of the entire haemangioma as well as the portion of each haemangioma with highest signal on fat-saturation sequences was performed and normalized to a reference normal vertebral body. Results: A total of 8 patients with aggressive vertebral haemangioma and 11 patients with asymptomatic typical vertebral haemangioma were included. There was a significant difference between total normalized mean SI ratio (3.14 vs 1.48, p = 0.0002), total normalized maximum SI ratio (5.72 vs 2.55, p = 0.0003), brightest normalized mean SI ratio (4.28 vs 1.72, p < 0.0001) and brightest normalized maximum SI ratio (5.25 vs 2.45, p = 0.0003). Multiple measures were able to discriminate between groups with high sensitivity (>88%) and specificity (>82%). Conclusion: In addition to the conventional imaging features such as vertebral expansion and presence of extravertebral component, quantitative evaluation of fat-suppression sequences is also another imaging feature that can differentiate aggressive haemangioma and typical asymptomatic haemangioma. Advances in knowledge: The use of quantitative fat-suppressed MRI in vertebral haemangiomas is demonstrated. Quantitative fat-suppressed MRI can have a role in confirming the diagnosis of aggressive haemangiomas. In addition, this application can be further investigated in future studies to predict aggressiveness of vertebral haemangiomas in early stages. PMID:26511277

Introducing keytagging, a novel technique for the protection of medical image-based tests.

PubMed

Rubio, Óscar J; Alesanco, Álvaro; García, José

2015-08-01

This paper introduces keytagging, a novel technique to protect medical image-based tests by implementing image authentication, integrity control and location of tampered areas, private captioning with role-based access control, traceability and copyright protection. It relies on the association of tags (binary data strings) to stable, semistable or volatile features of the image, whose access keys (called keytags) depend on both the image and the tag content. Unlike watermarking, this technique can associate information to the most stable features of the image without distortion. Thus, this method preserves the clinical content of the image without the need for assessment, prevents eavesdropping and collusion attacks, and obtains a substantial capacity-robustness tradeoff with simple operations. The evaluation of this technique, involving images of different sizes from various acquisition modalities and image modifications that are typical in the medical context, demonstrates that all the aforementioned security measures can be implemented simultaneously and that the algorithm presents good scalability. In addition to this, keytags can be protected with standard Cryptographic Message Syntax and the keytagging process can be easily combined with JPEG2000 compression since both share the same wavelet transform. This reduces the delays for associating keytags and retrieving the corresponding tags to implement the aforementioned measures to only ≃30 and ≃90ms respectively. As a result, keytags can be seamlessly integrated within DICOM, reducing delays and bandwidth when the image test is updated and shared in secure architectures where different users cooperate, e.g. physicians who interpret the test, clinicians caring for the patient and researchers. Copyright © 2015 Elsevier Inc. All rights reserved.

High contrast imaging through adaptive transmittance control in the focal plane

NASA Astrophysics Data System (ADS)

Dhadwal, Harbans S.; Rastegar, Jahangir; Feng, Dake

2016-05-01

High contrast imaging, in the presence of a bright background, is a challenging problem encountered in diverse applications ranging from the daily chore of driving into a sun-drenched scene to in vivo use of biomedical imaging in various types of keyhole surgeries. Imaging in the presence of bright sources saturates the vision system, resulting in loss of scene fidelity, corresponding to low image contrast and reduced resolution. The problem is exacerbated in retro-reflective imaging systems where the light sources illuminating the object are unavoidably strong, typically masking the object features. This manuscript presents a novel theoretical framework, based on nonlinear analysis and adaptive focal plane transmittance, to selectively remove object domain sources of background light from the image plane, resulting in local and global increases in image contrast. The background signal can either be of a global specular nature, giving rise to parallel illumination from the entire object surface or can be represented by a mosaic of randomly orientated, small specular surfaces. The latter is more representative of real world practical imaging systems. Thus, the background signal comprises of groups of oblique rays corresponding to distributions of the mosaic surfaces. Through the imaging system, light from group of like surfaces, converges to a localized spot in the focal plane of the lens and then diverges to cast a localized bright spot in the image plane. Thus, transmittance of a spatial light modulator, positioned in the focal plane, can be adaptively controlled to block a particular source of background light. Consequently, the image plane intensity is entirely due to the object features. Experimental image data is presented to verify the efficacy of the methodology.

Reliable structural information from multiscale decomposition with the Mellor-Brady filter

NASA Astrophysics Data System (ADS)

Szilágyi, Tünde; Brady, Michael

2009-08-01

Image-based medical diagnosis typically relies on the (poorly reproducible) subjective classification of textures in order to differentiate between diseased and healthy pathology. Clinicians claim that significant benefits would arise from quantitative measures to inform clinical decision making. The first step in generating such measures is to extract local image descriptors - from noise corrupted and often spatially and temporally coarse resolution medical signals - that are invariant to illumination, translation, scale and rotation of the features. The Dual-Tree Complex Wavelet Transform (DT-CWT) provides a wavelet multiresolution analysis (WMRA) tool e.g. in 2D with good properties, but has limited rotational selectivity. Also, it requires computationally-intensive steering due to the inherently 1D operations performed. The monogenic signal, which is defined in n >= 2D with the Riesz transform gives excellent orientation information without the need for steering. Recent work has suggested the Monogenic Riesz-Laplace wavelet transform as a possible tool for integrating these two concepts into a coherent mathematical framework. We have found that the proposed construction suffers from a lack of rotational invariance and is not optimal for retrieving local image descriptors. In this paper we show: 1. Local frequency and local phase from the monogenic signal are not equivalent, especially in the phase congruency model of a "feature", and so they are not interchangeable for medical image applications. 2. The accuracy of local phase computation may be improved by estimating the denoising parameters while maximizing a new measure of "featureness".

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Coronal bright points associated with minifilament eruptions

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

Hong, Junchao; Jiang, Yunchun; Yang, Jiayan

2014-12-01

Coronal bright points (CBPs) are small-scale, long-lived coronal brightenings that always correspond to photospheric network magnetic features of opposite polarity. In this paper, we subjectively adopt 30 CBPs in a coronal hole to study their eruptive behavior using data from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory. About one-quarter to one-third of the CBPs in the coronal hole go through one or more minifilament eruption(s) (MFE(s)) throughout their lifetimes. The MFEs occur in temporal association with the brightness maxima of CBPs and possibly result from the convergence and cancellationmore » of underlying magnetic dipoles. Two examples of CBPs with MFEs are analyzed in detail, where minifilaments appear as dark features of a cool channel that divide the CBPs along the neutral lines of the dipoles beneath. The MFEs show the typical rising movements of filaments and mass ejections with brightenings at CBPs, similar to large-scale filament eruptions. Via differential emission measure analysis, it is found that CBPs are heated dramatically by their MFEs and the ejected plasmas in the MFEs have average temperatures close to the pre-eruption BP plasmas and electron densities typically near 10{sup 9} cm{sup –3}. These new observational results indicate that CBPs are more complex in dynamical evolution and magnetic structure than previously thought.« less

Super-resolution imaging of multiple cells by optimized flat-field epi-illumination

NASA Astrophysics Data System (ADS)

Douglass, Kyle M.; Sieben, Christian; Archetti, Anna; Lambert, Ambroise; Manley, Suliana

2016-11-01

Biological processes are inherently multi-scale, and supramolecular complexes at the nanoscale determine changes at the cellular scale and beyond. Single-molecule localization microscopy (SMLM) techniques have been established as important tools for studying cellular features with resolutions of the order of around 10 nm. However, in their current form these modalities are limited by a highly constrained field of view (FOV) and field-dependent image resolution. Here, we develop a low-cost microlens array (MLA)-based epi-illumination system—flat illumination for field-independent imaging (FIFI)—that can efficiently and homogeneously perform simultaneous imaging of multiple cells with nanoscale resolution. The optical principle of FIFI, which is an extension of the Köhler integrator, is further elucidated and modelled with a new, free simulation package. We demonstrate FIFI's capabilities by imaging multiple COS-7 and bacteria cells in 100 × 100 μm2 SMLM images—more than quadrupling the size of a typical FOV and producing near-gigapixel-sized images of uniformly high quality.

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques.

PubMed

Bigler, Erin D

2015-09-01

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Understanding refraction contrast using a comparison of absorption and refraction computed tomographic techniques

NASA Astrophysics Data System (ADS)

Wiebe, S.; Rhoades, G.; Wei, Z.; Rosenberg, A.; Belev, G.; Chapman, D.

2013-05-01

Refraction x-ray contrast is an imaging modality used primarily in a research setting at synchrotron facilities, which have a biomedical imaging research program. The most common method for exploiting refraction contrast is by using a technique called Diffraction Enhanced Imaging (DEI). The DEI apparatus allows the detection of refraction between two materials and produces a unique ''edge enhanced'' contrast appearance, very different from the traditional absorption x-ray imaging used in clinical radiology. In this paper we aim to explain the features of x-ray refraction contrast as a typical clinical radiologist would understand. Then a discussion regarding what needs to be considered in the interpretation of the refraction image takes place. Finally we present a discussion about the limitations of planar refraction imaging and the potential of DEI Computed Tomography. This is an original work that has not been submitted to any other source for publication. The authors have no commercial interests or conflicts of interest to disclose.

Spread spectrum image watermarking based on perceptual quality metric.

PubMed

Zhang, Fan; Liu, Wenyu; Lin, Weisi; Ngan, King Ngi

2011-11-01

Efficient image watermarking calls for full exploitation of the perceptual distortion constraint. Second-order statistics of visual stimuli are regarded as critical features for perception. This paper proposes a second-order statistics (SOS)-based image quality metric, which considers the texture masking effect and the contrast sensitivity in Karhunen-Loève transform domain. Compared with the state-of-the-art metrics, the quality prediction by SOS better correlates with several subjectively rated image databases, in which the images are impaired by the typical coding and watermarking artifacts. With the explicit metric definition, spread spectrum watermarking is posed as an optimization problem: we search for a watermark to minimize the distortion of the watermarked image and to maximize the correlation between the watermark pattern and the spread spectrum carrier. The simple metric guarantees the optimal watermark a closed-form solution and a fast implementation. The experiments show that the proposed watermarking scheme can take full advantage of the distortion constraint and improve the robustness in return.

CLASSIFYING MEDICAL IMAGES USING MORPHOLOGICAL APPEARANCE MANIFOLDS.

PubMed

Varol, Erdem; Gaonkar, Bilwaj; Davatzikos, Christos

2013-12-31

Input features for medical image classification algorithms are extracted from raw images using a series of pre processing steps. One common preprocessing step in computational neuroanatomy and functional brain mapping is the nonlinear registration of raw images to a common template space. Typically, the registration methods used are parametric and their output varies greatly with changes in parameters. Most results reported previously perform registration using a fixed parameter setting and use the results as input to the subsequent classification step. The variation in registration results due to choice of parameters thus translates to variation of performance of the classifiers that depend on the registration step for input. Analogous issues have been investigated in the computer vision literature, where image appearance varies with pose and illumination, thereby making classification vulnerable to these confounding parameters. The proposed methodology addresses this issue by sampling image appearances as registration parameters vary, and shows that better classification accuracies can be obtained this way, compared to the conventional approach.

Atypical progression of multiple myeloma with extensive extramedullary disease.

PubMed Central

Jowitt, S N; Jacobs, A; Batman, P A; Sapherson, D A

1994-01-01

Multiple myeloma is a neoplastic disorder caused by the proliferation of a transformed B lymphoid progenitor cell that gives rise to a clone of immunoglobulin-secreting cells. Other plasma cell tumours include solitary plasmacytoma of bone (SPB) and extramedullary plasmacytomas (EMP). Despite an apparent common origin there exist pathological and clinical differences between these neoplasms and the association between them is not completely understood. A case of IgG multiple myeloma that presented with typical clinical and laboratory features, including a bone marrow infiltrated by well differentiated plasma cells, is reported. The tumour had an unusual evolution, with the development of extensive extramedullary disease while maintaining mature histological features. Images PMID:8163701

Radiological features of primitive neuroectodermal tumors in intra-abdominal and retroperitoneal regions: A series of 18 cases

PubMed Central

Zhang, Youming; Xiao, Desheng; Yin, Hongling; Long, Xueying; Li, Li; Zai, Hongyan; Chen, Minfeng; Li, Wenzheng; Sun, Lunquan

2017-01-01

Objectives To characterize the imaging and clinicopathological features of primitive neuroectodermal tumors (PNETs) arising in intra-abdominal and retroperitoneal regions. Methods Eighteen patients with histopathologically proven intra-abdominal and retroperitoneal PNET were enrolled; computed tomography was performed for all cases, and magnetic resonance imaging was performed for a single case. Typical computed tomography and magnetic resonance imaging findings, including morphology, texture and enhancement features, as well as clinicopathological characteristics and prognosis data were retrospectively analyzed. Results Of eighteen PNET patients, fifteen were male and three were female, with a median age of 36 years (range, 2–65 years). The onset of symptoms was most often nonspecific and insidious. The mean tumor diameter was 7.2 cm (range, 3.0–12.1 cm), with necrosis in fifteen cases, cystic changes in eight, partition structure in five, calcification in five, hemorrhage in two, and mural nodules in one. Contrast enhanced computed tomography showed multiple tiny feeding arteries within the masses in six cases, resulting in a crab-like appearance, and mild ring enhancement pattern in five cases. Eleven cases showed surrounding invasion and metastasis. Of the eighteen PNET cases, nine cases showed smooth, well-defined margins, and nine cases had irregular, ill-defined margins. A median survival was 10.0±1.6 months. However, chemotherapy had efficacy on patients even those with advanced disease. Conclusions Primary intra-abdominal and retroperitoneal PNETs are rare, and imaging features documented here may help the diagnosis of this severe disease. Notably, two signs present in retroperitoneal PNET tumors, including a mild ring enhancement pattern and a crab-like appearance of the tiny feeding arteries, may have the potential to help us improve the ability to make a relatively reliable diagnosis. PMID:28319177

Utilization of DIRSIG in support of real-time infrared scene generation

NASA Astrophysics Data System (ADS)

Sanders, Jeffrey S.; Brown, Scott D.

2000-07-01

Real-time infrared scene generation for hardware-in-the-loop has been a traditionally difficult challenge. Infrared scenes are usually generated using commercial hardware that was not designed to properly handle the thermal and environmental physics involved. Real-time infrared scenes typically lack details that are included in scenes rendered in no-real- time by ray-tracing programs such as the Digital Imaging and Remote Sensing Scene Generation (DIRSIG) program. However, executing DIRSIG in real-time while retaining all the physics is beyond current computational capabilities for many applications. DIRSIG is a first principles-based synthetic image generation model that produces multi- or hyper-spectral images in the 0.3 to 20 micron region of the electromagnetic spectrum. The DIRSIG model is an integrated collection of independent first principles based on sub-models, each of which works in conjunction to produce radiance field images with high radiometric fidelity. DIRSIG uses the MODTRAN radiation propagation model for exo-atmospheric irradiance, emitted and scattered radiances (upwelled and downwelled) and path transmission predictions. This radiometry submodel utilizes bidirectional reflectance data, accounts for specular and diffuse background contributions, and features path length dependent extinction and emission for transmissive bodies (plumes, clouds, etc.) which may be present in any target, background or solar path. This detailed environmental modeling greatly enhances the number of rendered features and hence, the fidelity of a rendered scene. While DIRSIG itself cannot currently be executed in real-time, its outputs can be used to provide scene inputs for real-time scene generators. These inputs can incorporate significant features such as target to background thermal interactions, static background object thermal shadowing, and partially transmissive countermeasures. All of these features represent significant improvements over the current state of the art in real-time IR scene generation.

Using spectrotemporal indices to improve the fruit-tree crop classification accuracy

NASA Astrophysics Data System (ADS)

Peña, M. A.; Liao, R.; Brenning, A.

2017-06-01

This study assesses the potential of spectrotemporal indices derived from satellite image time series (SITS) to improve the classification accuracy of fruit-tree crops. Six major fruit-tree crop types in the Aconcagua Valley, Chile, were classified by applying various linear discriminant analysis (LDA) techniques on a Landsat-8 time series of nine images corresponding to the 2014-15 growing season. As features we not only used the complete spectral resolution of the SITS, but also all possible normalized difference indices (NDIs) that can be constructed from any two bands of the time series, a novel approach to derive features from SITS. Due to the high dimensionality of this "enhanced" feature set we used the lasso and ridge penalized variants of LDA (PLDA). Although classification accuracies yielded by the standard LDA applied on the full-band SITS were good (misclassification error rate, MER = 0.13), they were further improved by 23% (MER = 0.10) with ridge PLDA using the enhanced feature set. The most important bands to discriminate the crops of interest were mainly concentrated on the first two image dates of the time series, corresponding to the crops' greenup stage. Despite the high predictor weights provided by the red and near infrared bands, typically used to construct greenness spectral indices, other spectral regions were also found important for the discrimination, such as the shortwave infrared band at 2.11-2.19 μm, sensitive to foliar water changes. These findings support the usefulness of spectrotemporal indices in the context of SITS-based crop type classifications, which until now have been mainly constructed by the arithmetic combination of two bands of the same image date in order to derive greenness temporal profiles like those from the normalized difference vegetation index.

The imaging performance of the SRC on Mars Express

USGS Publications Warehouse

Oberst, J.; Schwarz, G.; Behnke, T.; Hoffmann, H.; Matz, K.-D.; Flohrer, J.; Hirsch, H.; Roatsch, T.; Scholten, F.; Hauber, E.; Brinkmann, B.; Jaumann, R.; Williams, D.; Kirk, R.; Duxbury, T.; Leu, C.; Neukum, G.

2008-01-01

The Mars Express spacecraft carries the pushbroom scanner high-resolution stereo camera (HRSC) and its added imaging subsystem super resolution channel (SRC). The SRC is equipped with its own optical system and a 1024??1024 framing sensor. SRC produces snapshots with 2.3 m ground pixel size from the nominal spacecraft pericenter height of 250 km, which are typically embedded in the central part of the large HRSC scenes. The salient features of the SRC are its light-weight optics, a reliable CCD detector, and high-speed read-out electronics. The quality and effective visibility of details in the SRC images unfortunately falls short of what has been expected. In cases where thermal balance cannot be reached, artifacts, such as blurring and "ghost features" are observed in the images. In addition, images show large numbers of blemish pixels and are plagued by electronic noise. As a consequence, we have developed various image improving algorithms, which are discussed in this paper. While results are encouraging, further studies of image restoration by dedicated processing appear worthwhile. The SRC has obtained more than 6940 images at the time of writing (1 September 2007), which often show fascinating details in surface morphology. SRC images are highly useful for a variety of applications in planetary geology, for studies of the Mars atmosphere, and for astrometric observations of the Martian satellites. This paper will give a full account of the design philosophy, technical concept, calibration, operation, integration with HRSC, and performance, as well as science accomplishments of the SRC. ?? 2007 Elsevier Ltd. All rights reserved.

Enhanced simulator software for image validation and interpretation for multimodal localization super-resolution fluorescence microscopy

NASA Astrophysics Data System (ADS)

Erdélyi, Miklós; Sinkó, József; Gajdos, Tamás.; Novák, Tibor

2017-02-01

Optical super-resolution techniques such as single molecule localization have become one of the most dynamically developed areas in optical microscopy. These techniques routinely provide images of fixed cells or tissues with sub-diffraction spatial resolution, and can even be applied for live cell imaging under appropriate circumstances. Localization techniques are based on the precise fitting of the point spread functions (PSF) to the measured images of stochastically excited, identical fluorescent molecules. These techniques require controlling the rate between the on, off and the bleached states, keeping the number of active fluorescent molecules at an optimum value, so their diffraction limited images can be detected separately both spatially and temporally. Because of the numerous (and sometimes unknown) parameters, the imaging system can only be handled stochastically. For example, the rotation of the dye molecules obscures the polarization dependent PSF shape, and only an averaged distribution - typically estimated by a Gaussian function - is observed. TestSTORM software was developed to generate image stacks for traditional localization microscopes, where localization meant the precise determination of the spatial position of the molecules. However, additional optical properties (polarization, spectra, etc.) of the emitted photons can be used for further monitoring the chemical and physical properties (viscosity, pH, etc.) of the local environment. The image stack generating program was upgraded by several new features, such as: multicolour, polarization dependent PSF, built-in 3D visualization, structured background. These features make the program an ideal tool for optimizing the imaging and sample preparation conditions.

MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images.

PubMed

Prasad, Dilip K; Rajan, Deepu; Rachmawati, Lily; Rajabally, Eshan; Quek, Chai

2016-12-01

This paper addresses the problem of horizon detection, a fundamental process in numerous object detection algorithms, in a maritime environment. The maritime environment is characterized by the absence of fixed features, the presence of numerous linear features in dynamically changing objects and background and constantly varying illumination, rendering the typically simple problem of detecting the horizon a challenging one. We present a novel method called multi-scale consistence of weighted edge Radon transform, abbreviated as MuSCoWERT. It detects the long linear features consistent over multiple scales using multi-scale median filtering of the image followed by Radon transform on a weighted edge map and computing the histogram of the detected linear features. We show that MuSCoWERT has excellent performance, better than seven other contemporary methods, for 84 challenging maritime videos, containing over 33,000 frames, and captured using visible range and near-infrared range sensors mounted onboard, onshore, or on floating buoys. It has a median error of about 2 pixels (less than 0.2%) from the center of the actual horizon and a median angular error of less than 0.4 deg. We are also sharing a new challenging horizon detection dataset of 65 videos of visible, infrared cameras for onshore and onboard ship camera placement.

A New Approach for Combining Time-of-Flight and RGB Cameras Based on Depth-Dependent Planar Projective Transformations

PubMed Central

Salinas, Carlota; Fernández, Roemi; Montes, Héctor; Armada, Manuel

2015-01-01

Image registration for sensor fusion is a valuable technique to acquire 3D and colour information for a scene. Nevertheless, this process normally relies on feature-matching techniques, which is a drawback for combining sensors that are not able to deliver common features. The combination of ToF and RGB cameras is an instance that problem. Typically, the fusion of these sensors is based on the extrinsic parameter computation of the coordinate transformation between the two cameras. This leads to a loss of colour information because of the low resolution of the ToF camera, and sophisticated algorithms are required to minimize this issue. This work proposes a method for sensor registration with non-common features and that avoids the loss of colour information. The depth information is used as a virtual feature for estimating a depth-dependent homography lookup table (Hlut). The homographies are computed within sets of ground control points of 104 images. Since the distance from the control points to the ToF camera are known, the working distance of each element on the Hlut is estimated. Finally, two series of experimental tests have been carried out in order to validate the capabilities of the proposed method. PMID:26404315

ARC-1979-AC79-7095

NASA Image and Video Library

1979-07-11

Range : 312, 000 kilometers (195,000 miles) This photo of Ganymede (Ice Giant) was taken from Voyager 2 and shows features down to about 5 to 6 kilometers across. Different types of terrain common on Ganymede's surface are visible. The boundary of the largest region of dark ancient terrain on Ganymede can be seen to the east (right), revealing some of the light linear features which may be all that remains of a large ancient impact structure similar to the large ring structure on Callisto. The broad light regions running through the image are the typical grooved structures seen within another example of what might be evidence of large scale lateral motion in Ganymede's crust. The band of grooved terrain (about 100 kilometers wide) in this region appears to be offset by 50 kilometers or more on the left hand edge by a linear feature perpendicular to it. A feature similar to this one was previously discovered by Voyager 1. These are the first clear examples of strike-slip style faulting on any planet other than Earth. Many examples of craters of all ages can be seen in this image, ranging from fresh, bright ray craters to large, subdued circular markings thought to be the 'scars' of large ancient impacts that have been flatteded by glacier-like flows.

Scanning ultrafast electron microscopy.

PubMed

Yang, Ding-Shyue; Mohammed, Omar F; Zewail, Ahmed H

2010-08-24

Progress has been made in the development of four-dimensional ultrafast electron microscopy, which enables space-time imaging of structural dynamics in the condensed phase. In ultrafast electron microscopy, the electrons are accelerated, typically to 200 keV, and the microscope operates in the transmission mode. Here, we report the development of scanning ultrafast electron microscopy using a field-emission-source configuration. Scanning of pulses is made in the single-electron mode, for which the pulse contains at most one or a few electrons, thus achieving imaging without the space-charge effect between electrons, and still in ten(s) of seconds. For imaging, the secondary electrons from surface structures are detected, as demonstrated here for material surfaces and biological specimens. By recording backscattered electrons, diffraction patterns from single crystals were also obtained. Scanning pulsed-electron microscopy with the acquired spatiotemporal resolutions, and its efficient heat-dissipation feature, is now poised to provide in situ 4D imaging and with environmental capability.

Recent advances in synchrotron-based hard x-ray phase contrast imaging

NASA Astrophysics Data System (ADS)

Liu, Y.; Nelson, J.; Holzner, C.; Andrews, J. C.; Pianetta, P.

2013-12-01

Ever since the first demonstration of phase contrast imaging (PCI) in the 1930s by Frits Zernike, people have realized the significant advantage of phase contrast over conventional absorption-based imaging in terms of sensitivity to ‘transparent’ features within specimens. Thus, x-ray phase contrast imaging (XPCI) holds great potential in studies of soft biological tissues, typically containing low Z elements such as C, H, O and N. Particularly when synchrotron hard x-rays are employed, the favourable brightness, energy tunability, monochromatic characteristics and penetration depth have dramatically enhanced the quality and variety of XPCI methods, which permit detection of the phase shift associated with 3D geometry of relatively large samples in a non-destructive manner. In this paper, we review recent advances in several synchrotron-based hard x-ray XPCI methods. Challenges and key factors in methodological development are discussed, and biological and medical applications are presented.

Image processing and recognition for biological images.

PubMed

Uchida, Seiichi

2013-05-01

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. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Multimodal and Multi-tissue Measures of Connectivity Revealed by Joint Independent Component Analysis.

PubMed

Franco, Alexandre R; Ling, Josef; Caprihan, Arvind; Calhoun, Vince D; Jung, Rex E; Heileman, Gregory L; Mayer, Andrew R

2008-12-01

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Investigating Mars: Melas Chasma

NASA Image and Video Library

2017-12-01

Melas Chasma is part of the largest canyon system on Mars, Valles Marineris. At only 563 km long (349 miles) it is not the longest canyon, but it is the widest. Located in the center of Valles Marineris, it has depths up to 9 km below the surrounding plains, and is the location of many large landslide deposits, as will as layered materials and sand dunes. There is evidence of both water and wind action as modes of formation for many of the interior deposits. Today's image covers part of the floor of the canyon. At the top of the image is one of the many hills found on the floor in this region. The linear grooved surface is part of a landslide deposit. Melas Chasma has many large landslide regions. Landslide deposits often have grooved surfaces with the grooves parallel to the direction of movement as the slide occurred. The ends of the landslide typically have a lobate edge, and will flow around large preexisting landforms. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 19112 Latitude: -11.1675 Longitude: 289.748 Instrument: VIS Captured: 2006-04-05 23:00 https://photojournal.jpl.nasa.gov/catalog/PIA22132

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Morphological differences in the mirror neuron system in Williams syndrome.

PubMed

Ng, Rowena; Brown, Timothy T; Erhart, Matthew; Järvinen, Anna M; Korenberg, Julie R; Bellugi, Ursula; Halgren, Eric

2016-01-01

Williams syndrome (WS) is a genetic condition characterized by an overly gregarious personality, including high empathetic concern for others. Although seemingly disparate from the profile of autism spectrum disorder (ASD), both are associated with deficits in social communication/cognition. Notably, the mirror neuron system (MNS) has been implicated in social dysfunction for ASD; yet, the integrity of this network and its association with social functioning in WS remains unknown. Magnetic resonance imaging (MRI) methods were used to examine the structural integrity of the MNS of adults with WS versus typically developing (TD) individuals. The Social Responsiveness Scale (SRS), a tool typically used to screen for social features of ASD, was also employed to assess the relationships between social functioning with the MNS morphology in WS participants. WS individuals showed reduced cortical surface area of MNS substrates yet relatively preserved cortical thickness as compared to TD adults. Increased cortical thickness of the inferior parietal lobule (IPL) was associated with increased deficits in social communication, social awareness, social cognition, and autistic mannerisms. However, social motivation was not related to anatomical features of the MNS. Our findings indicate that social deficits typical to both ASD and WS may be attributed to an aberrant MNS, whereas the unusual social drive marked in WS is subserved by substrates distinct from this network.

Morphological differences in the mirror neuron system in Williams Syndrome

PubMed Central

Ng, Rowena; Brown, Timothy T.; Erhart, Matthew; Järvinen, Anna M.; Korenberg, Julie R.; Bellugi, Ursula; Halgren, Eric

2015-01-01

Williams syndrome (WS) is a genetic condition characterized by an overly gregarious personality, including high empathetic concern for others. Although seemingly disparate from the profile of autism spectrum disorder (ASD), both are associated with deficits in social communication/cognition. Notably, the mirror neuron system (MNS) has been implicated in social dysfunction for ASD; yet, the integrity of this network and its association with social functioning in WS remains unknown. Magnetic resonance imaging methods were used to examine the structural integrity of the MNS of adults with WS versus typically developing (TD) individuals. The Social Responsiveness Scale (SRS), a tool typically used to screen for social features of ASD, was also employed to assess the relationships between social functioning with the MNS morphology in WS participants. WS individuals showed reduced cortical surface area of MNS substrates yet relatively preserved cortical thickness as compared to TD adults. Increased cortical thickness of the inferior parietal lobule was associated with increased deficits in social communication, social awareness, social cognition, and autistic mannerisms. However, social motivation was not related to anatomical features of the MNS. Our findings indicate that social deficits typical to both ASD and WS may be attributed to an aberrant MNS, whereas the unusual social drive marked in WS is subserved by substrates distinct from this network. PMID:26230578

Online image classification under monotonic decision boundary constraint

NASA Astrophysics Data System (ADS)

Lu, Cheng; Allebach, Jan; Wagner, Jerry; Pitta, Brandi; Larson, David; Guo, Yandong

2015-01-01

Image classification is a prerequisite for copy quality enhancement in all-in-one (AIO) device that comprises a printer and scanner, and which can be used to scan, copy and print. Different processing pipelines are provided in an AIO printer. Each of the processing pipelines is designed specifically for one type of input image to achieve the optimal output image quality. A typical approach to this problem is to apply Support Vector Machine to classify the input image and feed it to its corresponding processing pipeline. The online training SVM can help users to improve the performance of classification as input images accumulate. At the same time, we want to make quick decision on the input image to speed up the classification which means sometimes the AIO device does not need to scan the entire image to make a final decision. These two constraints, online SVM and quick decision, raise questions regarding: 1) what features are suitable for classification; 2) how we should control the decision boundary in online SVM training. This paper will discuss the compatibility of online SVM and quick decision capability.

Nutcracker syndrome in adolescent with perineal pain: An interesting case of an adolescent with perineal pain due to pelvic congestion from nutcracker syndrome with relief after balloon venoplasty and sclerotherapy.

PubMed

Boyer, Kathleen; Filan, Eamon; Ching, Brian; Rooks, Veronica; Kellicut, Dwight

2018-02-01

Nutcracker phenomenon is the descriptor for a patient's anatomy whenever the left renal vein becomes compressed between the abdominal aorta and the superior mesenteric artery. Nutcracker syndrome is the terminology used when the nutcracker phenomenon is accompanied by symptoms including pain (abdominal, flank, pelvic), hematuria, and orthostatic proteinuria. Diagnosis can be made with Doppler ultrasound, venography, computed tomography, or magnetic resonance imaging. This case demonstrates some of the typical findings of nutcracker syndrome. The limited clinical features and interesting imaging findings, in addition to the young age of the patient, make this a notable case.

Earth Observations taken by the Expedition 16 Crew

NASA Image and Video Library

2007-12-29

ISS016-E-019239 (29 Dec. 2007) --- Dendi Caldera, Ethiopia is featured in this image photographed by an Expedition 16 crew member on the International Space Station. The Dendi Caldera is located on the Ethiopian Plateau, approximately 86 kilometers to the southwest of Addis Ababa. A caldera is a geological feature formed by the near-total eruption of magma from beneath a volcano, leading to collapse of the volcanic structure into the now-empty magma chamber. This collapse typically leaves a crater or depression where the volcano stood, and later volcanic activity can fill the caldera with younger lavas, ash, pyroclastic rocks, and sediments. While much of the volcanic rock in the area is comprised of basalt erupted as part of the opening of the East African Rift, more silica-rich rock types (characterized by minerals such as quartz and feldspar) are also present. According to scientists, the approximately 4 kilometers wide Dendi Caldera includes some of this silica-rich volcanic rock -- the rim of the caldera, visible in this view, is comprised mainly of poorly-consolidated ash erupted during the Tertiary Period (approximately 65 -- 2 million years ago). A notable feature of the Dendi Caldera is the presence of two shallow lakes formed within the central depression (center). This image also highlights a radial drainage pattern surrounding the remnants of the Dendi volcanic cone. Such patterns typically form around volcanoes, as rainfall has equal potential to move downslope on all sides of the cone and incise channels. No historical volcanic eruptions of Dendi are recorded, however the Wonchi Caldera 13 kilometers to the southwest (not shown) may have been active as "recently" as A.D. 550, say NASA scientists.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

PubMed

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.

Automatic Reconstruction of Spacecraft 3D Shape from Imagery

NASA Astrophysics Data System (ADS)

Poelman, C.; Radtke, R.; Voorhees, H.

We describe a system that computes the three-dimensional (3D) shape of a spacecraft from a sequence of uncalibrated, two-dimensional images. While the mathematics of multi-view geometry is well understood, building a system that accurately recovers 3D shape from real imagery remains an art. A novel aspect of our approach is the combination of algorithms from computer vision, photogrammetry, and computer graphics. We demonstrate our system by computing spacecraft models from imagery taken by the Air Force Research Laboratory's XSS-10 satellite and DARPA's Orbital Express satellite. Using feature tie points (each identified in two or more images), we compute the relative motion of each frame and the 3D location of each feature using iterative linear factorization followed by non-linear bundle adjustment. The "point cloud" that results from this traditional shape-from-motion approach is typically too sparse to generate a detailed 3D model. Therefore, we use the computed motion solution as input to a volumetric silhouette-carving algorithm, which constructs a solid 3D model based on viewpoint consistency with the image frames. The resulting voxel model is then converted to a facet-based surface representation and is texture-mapped, yielding realistic images from arbitrary viewpoints. We also illustrate other applications of the algorithm, including 3D mensuration and stereoscopic 3D movie generation.

The MRI appearances of cancellous allograft bone chips after the excision of bone tumours.

PubMed

Kang, S; Han, I; Hong, S H; Cho, H S; Kim, W; Kim, H-S

2015-01-01

Cancellous allograft bone chips are commonly used in the reconstruction of defects in bone after removal of benign tumours. We investigated the MRI features of grafted bone chips and their change over time, and compared them with those with recurrent tumour. We retrospectively reviewed 66 post-operative MRIs from 34 patients who had undergone curettage and grafting with cancellous bone chips to fill the defect after excision of a tumour. All grafts showed consistent features at least six months after grafting: homogeneous intermediate or low signal intensities with or without scattered hyperintense foci (speckled hyperintensities) on T1 images; high signal intensities with scattered hypointense foci (speckled hypointensities) on T2 images, and peripheral rim enhancement with or without central heterogeneous enhancements on enhanced images. Incorporation of the graft occurred from the periphery to the centre, and was completed within three years. Recurrent lesions consistently showed the same signal intensities as those of pre-operative MRIs of the primary lesions. There were four misdiagnoses, three of which were chondroid tumours. We identified typical MRI features and clarified the incorporation process of grafted cancellous allograft bone chips. The most important characteristics of recurrent tumours were that they showed the same signal intensities as the primary tumours. It might sometimes be difficult to differentiate grafted cancellous allograft bone chips from a recurrent chondroid tumour. ©2015 The British Editorial Society of Bone & Joint Surgery.

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Gatos, I.; Tsantis, S.; Karamesini, M.; Skouroliakou, A.; Kagadis, G.

2015-09-01

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

PubMed

Whitney, Jon; Corredor, German; Janowczyk, Andrew; Ganesan, Shridar; Doyle, Scott; Tomaszewski, John; Feldman, Michael; Gilmore, Hannah; Madabhushi, Anant

2018-05-30

Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.

PubMed

Li, Yuqian; Cornelis, Bruno; Dusa, Alexandra; Vanmeerbeeck, Geert; Vercruysse, Dries; Sohn, Erik; Blaszkiewicz, Kamil; Prodanov, Dimiter; Schelkens, Peter; Lagae, Liesbet

2018-05-01

Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Invasive growth patterns of juvenile nasopharyngeal angiofibroma: radiological imaging and clinical implications.

PubMed

Szymańska, Anna; Szymański, Marcin; Czekajska-Chehab, Elżbieta; Szczerbo-Trojanowska, Małgorzata

2014-07-01

Juvenile nasopharyngeal angiofibroma is a benign lesion with locally aggressive nature. Knowledge of its typical growth patterns is crucial for precise preoperative staging and adequate preoperative patient counseling. This pictorial essay focuses on characteristic radiological features and paths of invasive growth of this rare tumor. Also, the impact of accurate preoperative evaluation of tumor extensions on surgical planning and results of treatment are discussed. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Exploring the Solar System with Stellar Occultations

NASA Technical Reports Server (NTRS)

Elliot, J. L.; Dunham, E. W.

1984-01-01

By recording the light intensity as a function of time when a planet occults a relatively bright star, the thermal structure of the upper atmosphere of the planet can be probed. The main feature of stellar occultation observations is their high spatial resolution, typically several thousand times better than the resolution achievable with ground-based imaging. Five stellar occultations have been observed. The main results of these observations are summarized. Stellar occultations have been observed on Uranus, Mars, Pallas, Neptune and the Jovian Ring.

Unusual Bone Superscan, MIBG Superscan, and 68Ga DOTATATE PET/CT in Metastatic Pheochromocytoma.

PubMed

Tan, Teik Hin; Wong, Teck Huat; Hassan, Siti Zarina Amir; Lee, Boon Nang

2015-11-01

A 17-year-old adolescent boy with biochemically raised 2-hour urinary metanephrine and normetanephrine as well as CT findings of retroperitoneal soft tissue mass and bony metastases was referred for further assessment. Apart from Ga DOTATATE PET/CT evaluation, pretargeted systemic radionuclide therapy assessment with I-MIBG scintigraphy showed unusual phenomenon of MIBG superscan. Postsurgically, restaging Tc-MDP bone scintigraphy showed typical bone superscan features. The MIBG superscan was better delineated on post-I-MIBG therapy images.

Use of field reflectance data for crop mapping using airborne hyperspectral image

NASA Astrophysics Data System (ADS)

Nidamanuri, Rama Rao; Zbell, Bernd

2011-09-01

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question "what is the prospect of using independent reference reflectance spectra for image classification", while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of "non-existence of characteristic reflectance spectral signatures for vegetation", results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.

Investigating Mars: Rabe Crater

NASA Image and Video Library

2017-12-21

This is a false color image of Rabe Crater. In this combination of filters "blue" typically means basaltic sand. This VIS image crosses the entire crater and demonstrates how extensive the dunes are on the floor of Rabe Crater. Rabe Crater is 108 km (67 miles) across. Craters of similar size often have flat floors. Rabe Crater has some areas of flat floor, but also has a large complex pit occupying a substantial part of the floor. The interior fill of the crater is thought to be layered sediments created by wind and or water action. The pit is eroded into this material. The eroded materials appear to have stayed within the crater forming a large sand sheet with surface dune forms as well as individual dunes where the crater floor is visible. The dunes also appear to be moving from the upper floor level into the pit. The THEMIS VIS camera contains 5 filters. The data from different filters can be combined in multiple ways to create a false color image. These false color images may reveal subtle variations of the surface not easily identified in a single band image. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 67013 Latitude: -43.2572 Longitude: 34.5875 Instrument: VIS Captured: 2017-01-21 18:25 https://photojournal.jpl.nasa.gov/catalog/PIA22147

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

PubMed

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

Hakumyi Crater from LAMO

NASA Image and Video Library

2017-07-20

This close-up view of Hakumyi crater, as seen by NASA's Dawn spacecraft, provides insight into the origin of the small crater and lobe-shaped flow next to its southern rim. The sharp edges of these features indicate they are relatively recent with respect to the more subdued Hakumyi, which is 43 miles (70 kilometers) wide. The lobate flow ends in a tongue-shaped deposit. A more discrete feature slightly west (left) of the large lobe-shaped flow suggests an ancient or partially developed lobe. These kinds of flow features, which typically are found at high latitudes on Ceres, are expressions of what is termed "mass wasting," meaning the downslope movement of material. This process is initiated by slumping or detachment of material from crater rims. Here the process seems to have been triggered by small craters whose remnant shapes can be discerned at the top of each flow. Dawn took this image from its low-altitude mapping orbit, or LAMO, at a distance of about 240 miles (385 kilometers) above the surface. The center coordinates of this image are 52 degrees North latitude and 26 degrees east longitude. https://photojournal.jpl.nasa.gov/catalog/PIA21414

Anorectal Disorders

PubMed Central

Rao, Satish S. C.; Bharucha, Adil E.; Chiarioni, Giuseppe; Felt-Bersma, Richelle; Knowles, Charles; Malcolm, Allison; Wald, Arnold

2016-01-01

This report defines criteria and reviews the epidemiology, pathophysiology, and management of the following common anorectal disorders: fecal incontinence (FI), functional anorectal pain, and functional defecation disorders. FI is defined as the recurrent uncontrolled passage of fecal material for at least 3 months. The clinical features of FI are useful for guiding diagnostic testing and therapy. Anorectal manometry and imaging are useful for evaluating anal and pelvic floor structure and function. Education, antidiarrheals, and biofeedback therapy are the mainstay of management; surgery may be useful in refractory cases. Functional anorectal pain syndromes are defined by clinical features and categorized into 3 subtypes. In proctalgia fugax, the pain is typically fleeting and lasts for seconds to minutes. In levator ani syndrome and unspecified anorectal pain, the pain lasts more than 30 minutes, but in levator ani syndrome there is puborectalis tenderness. Functional defecation disorders are defined by ≥2 symptoms of chronic constipation or irritable bowel syndrome with constipation, and with ≥2 features of impaired evacuation, that is, abnormal evacuation pattern on manometry, abnormal balloon expulsion test, or impaired rectal evacuation by imaging. It includes 2 subtypes: dyssynergic defecation and inadequate defecatory propulsion. Pelvic floor biofeedback therapy is effective for treating levator ani syndrome and defecatory disorders. PMID:27144630

Functional Anorectal Disorders.

PubMed

Rao, Satish Sc; Bharucha, Adil E; Chiarioni, Giuseppe; Felt-Bersma, Richelle; Knowles, Charles; Malcolm, Allison; Wald, Arnold

2016-03-25

This report defines criteria and reviews the epidemiology, pathophysiology, and management of common anorectal disorders: fecal incontinence (FI), functional anorectal pain and functional defecation disorders. FI is defined as the recurrent uncontrolled passage of fecal material for at least 3 months. The clinical features of FI are useful for guiding diagnostic testing and therapy. Anorectal manometry and imaging are useful for evaluating anal and pelvic floor structure and function. Education, antidiarrheals and biofeedback therapy are the mainstay of management; surgery may be useful in refractory cases. Functional anorectal pain syndromes are defined by clinical features and categorized into three subtypes. In proctalgia fugax, the pain is typically fleeting and lasts for seconds to minutes. In levator ani syndrome (LAS) and unspecified anorectal pain the pain lasts more than 30 minutes, but in LAS there is puborectalis tenderness. Functional defecation disorders are defined by >2 symptoms of chronic constipation or irritable bowel syndrome with constipation, and with >2 features of impaired evacuation i.e., abnormal evacuation pattern on manometry, abnormal balloon expulsion test or impaired rectal evacuation by imaging. It includes two subtypes; dyssynergic defecation and inadequate defecatory propulsion. Pelvic floor biofeedback therapy is effective for treating LAS and defecatory disorders. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks.

PubMed

Wang, Changhan; Yan, Xinchen; Smith, Max; Kochhar, Kanika; Rubin, Marcie; Warren, Stephen M; Wrobel, James; Lee, Honglak

2015-01-01

Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system.

Earth Observations taken by the Expedition 22 Crew

NASA Image and Video Library

2009-12-02

ISS022-E-005403 (2 Dec. 2009) --- Giens Peninsula, France is featured in this image photographed by an Expedition 22 crew member on the International Space Station. This detailed image depicts the Giens Peninsula located along the Mediterranean coastline of France. The peninsula is part of the Cote d?Azur, also known as the French Riviera, the coastal region bounded by the Rhone River to the west, to the north by the Rhone Alps, and the east by the Italian border. The peninsula itself, extended out southwards from the city of Hyeres to the resort community of Giens, is formed from two tombolos. A tombolo is a ridge of beach material (typically sand) built by wave action that connects an island to the mainland. Tombolos, like many coastal features, typically change dramatically over geologic time due to fluctuating sediment supply, coastal currents, sea levels and storm events. The tombolos of the Giens Peninsula have been modified by human activities including sand dune removal, construction of roadways, and replacement of the original sand by other materials. The long-term survival of these tombolos will be determined by the effects of these changes on the natural coastal processes, with potential sea level rise presenting an additional threat. In addition to Giens, three other urban areas are visible in this image; Carqueiranne, Hyeres, and La Londe-les-Maures. The urban areas are recognizable by both light pink rooftops and grey street grids. These contrast with green to brown vegetated areas including agricultural fields (between Hyeres and La Londe-les-Maures, top center) and dark green vegetated hillslopes (between Hyeres and Carqueiranne, top left). Small white dots and streaks in the Mediterranean Sea are actually yachts and other pleasure craft.

New Analysis Method Application in Metallographic Images through the Construction of Mosaics Via Speeded Up Robust Features and Scale Invariant Feature Transform

PubMed Central

Rebouças Filho, Pedro Pedrosa; Moreira, Francisco Diego Lima; Xavier, Francisco Geilson de Lima; Gomes, Samuel Luz; dos Santos, José Ciro; Freitas, Francisco Nélio Costa; Freitas, Rodrigo Guimarães

2015-01-01

In many applications in metallography and analysis, many regions need to be considered and not only the current region. In cases where there are analyses with multiple images, the specialist should also evaluate neighboring areas. For example, in metallurgy, welding technology is derived from conventional testing and metallographic analysis. In welding, these tests allow us to know the features of the metal, especially in the Heat-Affected Zone (HAZ); the region most likely for natural metallurgical problems to occur in welding. The expanse of the Heat-Affected Zone exceeds the size of the area observed through a microscope and typically requires multiple images to be mounted on a larger picture surface to allow for the study of the entire heat affected zone. This image stitching process is performed manually and is subject to all the inherent flaws of the human being due to results of fatigue and distraction. The analyzing of grain growth is also necessary in the examination of multiple regions, although not necessarily neighboring regions, but this analysis would be a useful tool to aid a specialist. In areas such as microscopic metallography, which study metallurgical products with the aid of a microscope, the assembly of mosaics is done manually, which consumes a lot of time and is also subject to failures due to human limitations. The mosaic technique is used in the construct of environment or scenes with corresponding characteristics between themselves. Through several small images, and with corresponding characteristics between themselves, a new model is generated in a larger size. This article proposes the use of Digital Image Processing for the automatization of the construction of these mosaics in metallographic images. The use of this proposed method is meant to significantly reduce the time required to build the mosaic and reduce the possibility of failures in assembling the final image; therefore increasing efficiency in obtaining results and expediting the decision making process. Two different methods are proposed: One using the transformed Scale Invariant Feature Transform (SIFT), and the second using features extractor Speeded Up Robust Features (SURF). Although slower, the SIFT method is more stable and has a better performance than the SURF method and can be applied to real applications. The best results were obtained using SIFT with Peak Signal-to-Noise Ratio = 61.38, Mean squared error = 0.048 and mean-structural-similarity = 0.999, and processing time of 4.91 seconds for mosaic building. The methodology proposed shows be more promissory in aiding specialists during analysis of metallographic images. PMID:28793412

The almost-invisible perineurioma.

PubMed

Restrepo, Carlos E; Amrami, Kimberly K; Howe, Benjamin M; Dyck, P James B; Mauermann, Michelle L; Spinner, Robert J

2015-09-01

Intraneural perineurioma is a rare, benign slow-growing lesion arising from the perineurial cells that surrounds the peripheral nerve fibers. Typically it presents during childhood and young adulthood as a motor mononeuropathy. MRI plays an essential role in the diagnosis and localization of the lesion, which appears as a fusiform enlargement of the nerve fascicles that enhances intensely with gadolinium. Despite the typical clinical and radiological features, intraneural perineurioma remains largely underdiagnosed because of the lack of familiarity with this entity, but also as a result of technical limitations with conventional MRI that is typically performed as a screening test over a large field of view and without contrast sequences. The purpose of this article is to present the pitfalls and pearls learned from years of experience in the diagnosis and management of this relatively rare condition. Clinical suspicion and detailed neurological examination followed by high-quality electrophysiological studies (EPS) must lead to an adequate preimaging localization of the lesion and narrowing of the imaging area. The use of high-resolution (3-T) MRI combined with gadolinium administration will allow adequate visualization of the internal anatomy of the nerve and help in differentiating other causes of neuropathy. In cases where the lesion is not recognized but clinical suspicion is high, possible errors must be assessed, including the EPS localization, area of imaging, MRI resolution, and slice thickness.

Morphometric information to reduce the semantic gap in the characterization of microscopic images of thyroid nodules.

PubMed

Macedo, Alessandra A; Pessotti, Hugo C; Almansa, Luciana F; Felipe, Joaquim C; Kimura, Edna T

2016-07-01

The analyses of several systems for medical-imaging processing typically support the extraction of image attributes, but do not comprise some information that characterizes images. For example, morphometry can be applied to find new information about the visual content of an image. The extension of information may result in knowledge. Subsequently, results of mappings can be applied to recognize exam patterns, thus improving the accuracy of image retrieval and allowing a better interpretation of exam results. Although successfully applied in breast lesion images, the morphometric approach is still poorly explored in thyroid lesions due to the high subjectivity thyroid examinations. This paper presents a theoretical-practical study, considering Computer Aided Diagnosis (CAD) and Morphometry, to reduce the semantic discontinuity between medical image features and human interpretation of image content. The proposed method aggregates the content of microscopic images characterized by morphometric information and other image attributes extracted by traditional object extraction algorithms. This method carries out segmentation, feature extraction, image labeling and classification. Morphometric analysis was included as an object extraction method in order to verify the improvement of its accuracy for automatic classification of microscopic images. To validate this proposal and verify the utility of morphometric information to characterize thyroid images, a CAD system was created to classify real thyroid image-exams into Papillary Cancer, Goiter and Non-Cancer. Results showed that morphometric information can improve the accuracy and precision of image retrieval and the interpretation of results in computer-aided diagnosis. For example, in the scenario where all the extractors are combined with the morphometric information, the CAD system had its best performance (70% of precision in Papillary cases). Results signalized a positive use of morphometric information from images to reduce semantic discontinuity between human interpretation and image characterization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

Clayton, James; Shedlock, Daniel; Langeveld, Willem G.J.

Two goals for security scanning of cargo and freight are the ability to determine the type of material that is being imaged, and to do so at low radiation dose. One commonly used technique to determine the effective Z of the cargo is dual-energy imaging, i.e. imaging with different x-ray energy spectra. Another technique uses the fact that the transmitted x-ray spectrum itself also depends on the effective Z. Spectroscopy is difficult because the energy of individual x rays needs to be measured in a very high count-rate environment. Typical accelerators for security applications offer large but short bursts ofmore » x-rays, suitable for current-mode integrated imaging. In order to perform x-ray spectroscopy, a new accelerator design is desired that has the following features: 1) increased duty factor in order to spread out the arrival of x-rays at the detector array over time; 2) x-ray intensity modulation from one delivered pulse to the next by adjusting the accelerator electron beam instantaneous current so as to deliver adequate signal without saturating the spectroscopic detector; and 3) the capability to direct the (forward peaked) x-ray intensity towards high-attenuation areas in the cargo (“fan-beam-steering”). Current sources are capable of 0.1% duty factor, although usually they are operated at significantly lower duty factors (~0.04%), but duty factors in the range 0.4-1.0% are desired. The higher duty factor can be accomplished, e.g., by moving from 300 pulses per second (pps) to 1000 pps and/or increasing the pulse duration from a typical 4 μs to 10 μs. This paper describes initial R&D to examine cost effective modifications that could be performed on a typical accelerator for these purposes, as well as R&D for fan-beam steering.« less

Software for Analyzing Sequences of Flow-Related Images

NASA Technical Reports Server (NTRS)

Klimek, Robert; Wright, Ted

2004-01-01

Spotlight is a computer program for analysis of sequences of images generated in combustion and fluid physics experiments. Spotlight can perform analysis of a single image in an interactive mode or a sequence of images in an automated fashion. The primary type of analysis is tracking of positions of objects over sequences of frames. Features and objects that are typically tracked include flame fronts, particles, droplets, and fluid interfaces. Spotlight automates the analysis of object parameters, such as centroid position, velocity, acceleration, size, shape, intensity, and color. Images can be processed to enhance them before statistical and measurement operations are performed. An unlimited number of objects can be analyzed simultaneously. Spotlight saves results of analyses in a text file that can be exported to other programs for graphing or further analysis. Spotlight is a graphical-user-interface-based program that at present can be executed on Microsoft Windows and Linux operating systems. A version that runs on Macintosh computers is being considered.

Multisensor satellite observations of meso- and submesoscale surface circulation in the Liguro-Provençal Basin

NASA Astrophysics Data System (ADS)

Karimova, Svetlana; Alvera-Azcarate, Aida

2017-04-01

Despite great efforts being paid to studying circulation of the Western Mediterranean Basin and the factors triggering bioproductivity of its marine ecosystem, the evidence provided by satellite imagery has not been fully analysed yet. In the present paper, we concentrate our attention on mesoscale and submesoscale circulation features of the Liguro-Provençal Basin captured by satellite radiometer, spectroradiometer, and radar images. Using such a dataset makes it possible to observe the circulation features from a wide spatial range, from the basin scale through mesoscale to the scales of a few kilometers. Mesoscale features in this study are being mostly observed with thermal infrared imagery retrieved by AVHRR and AATSR sensors. Special attention in the work was paid to an analysis of the data coming from a geostationary satellite, namely ones provided by SEVIRI. Due to their uniquely high temporal resolution, such imagery allows observing circulation features in their evolution. During the winter blooming events, surface circulation at meso- to submesoscales in the region of interest was additionally highlighted by images obtained in the visible range. Full spatial resolution images provided by Envisat MERIS, Sentinel-2 MSI, and Landsat TM/ETM+/OLI made the greatest contribution to this part. The smallest scales (namely submesoscale) are being observed with synthetic aperture radar (SAR) imagery provided by Envisat ASAR and Sentinel-1 SAR. During an analysis of SAR images, it was noted that there was strikingly great amount of biogenic surfactants on the water surface in the region of interest. Apparently, low biological productivity typical for the Western Mediterranean ecosystem is not a limiting factor for the formation of surfactant films seen in SAR imagery. This finding though requires further consideration in some other researches, and hereafter we just benefited from the presence of surfactants, because they behave as good tracers of surface currents. Even though the region of interest belongs to the areas with low mean eddy kinetic energy, analysis of the images listed above revealed that the Liguro-Provençal Basin was showing a surprisingly high eddy activity among submesoscale and mesoscale features. However, the typical size of eddies in this area was smaller than that in the southern part of the Western Mediterranean. The general impression retrieved from the observations performed is that the main contributors to generation of observed mesoscale vortical structures are (i) the instability of the main currents in the region of interest and especially frontal instability at the Liguro-Provençal front and (ii) instabilities caused by the coastline inhomogeneity, especially in the eastern part of the Basin. Submesoscale eddy activity seems to be developed to its full extent during the periods when the mesoscale activity in the region of interest is not so prominent. This study is supported by the University of Liege and the EU in the context of the FP7-PEOPLE-COFUND-BeIPD project. Satellite imagery is provided by the European Space Agency.

Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

PubMed

Kellman, Philip J; Mnookin, Jennifer L; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E

2014-01-01

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons.

Absorption Mode FT-ICR Mass Spectrometry Imaging

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

Smith, Donald F.; Kilgour, David P.; Konijnenburg, Marco

2013-12-03

Fourier transform ion cyclotron resonance mass spectrometry offers the highest mass resolving power for molecular imaging experiments. This high mass resolving power ensures that closely spaced peaks at the same nominal mass are resolved for proper image generation. Typically higher magnetic fields are used to increase mass resolving power. However, a gain in mass resolving power can also be realized by phase correction of the data for absorption mode display. In addition to mass resolving power, absorption mode offers higher mass accuracy and signal-to-noise ratio over the conventional magnitude mode. Here we present the first use of absorption mode formore » Fourier transform ion cyclotron resonance mass spectrometry imaging. The Autophaser algorithm is used to phase correct each spectrum (pixel) in the image and then these parameters are used by the Chameleon work-flow based data processing software to generate absorption mode ?Datacubes? for image and spectral viewing. Absorption mode reveals new mass and spatial features that are not resolved in magnitude mode and results in improved selected ion image contrast.« less

Development of a novel imaging informatics-based system with an intelligent workflow engine (IWEIS) to support imaging-based clinical trials

PubMed Central

Wang, Ximing; Liu, Brent J; Martinez, Clarisa; Zhang, Xuejun; Winstein, Carolee J

2015-01-01

Imaging based clinical trials can benefit from a solution to efficiently collect, analyze, and distribute multimedia data at various stages within the workflow. Currently, the data management needs of these trials are typically addressed with custom-built systems. However, software development of the custom- built systems for versatile workflows can be resource-consuming. To address these challenges, we present a system with a workflow engine for imaging based clinical trials. The system enables a project coordinator to build a data collection and management system specifically related to study protocol workflow without programming. Web Access to DICOM Objects (WADO) module with novel features is integrated to further facilitate imaging related study. The system was initially evaluated by an imaging based rehabilitation clinical trial. The evaluation shows that the cost of the development of system can be much reduced compared to the custom-built system. By providing a solution to customize a system and automate the workflow, the system will save on development time and reduce errors especially for imaging clinical trials. PMID:25870169

Feasibility of ultrasound imaging of osteochondral defects in the ankle: a clinical pilot study.

PubMed

Kok, A C; Terra, M P; Muller, S; Askeland, C; van Dijk, C N; Kerkhoffs, G M M J; Tuijthof, G J M

2014-10-01

Talar osteochondral defects (OCDs) are imaged using magnetic resonance imaging (MRI) or computed tomography (CT). For extensive follow-up, ultrasound might be a fast, non-invasive alternative that images both bone and cartilage. In this study the potential of ultrasound, as compared with CT, in the imaging and grading of OCDs is explored. On the basis of prior CT scans, nine ankles of patients without OCDs and nine ankles of patients with anterocentral OCDs were selected and classified using the Loomer CT classification. A blinded expert skeletal radiologist imaged all ankles with ultrasound and recorded the presence of OCDs. Similarly to CT, ultrasound revealed typical morphologic OCD features, for example, cortex irregularities and loose fragments. Cartilage disruptions, Loomer grades IV (displaced fragment) and V (cyst with fibrous roof), were visible as well. This study encourages further research on the use of ultrasound as a follow-up imaging modality for OCDs located anteriorly or centrally on the talar dome. Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Streamlined approach to mapping the magnetic induction of skyrmionic materials.

PubMed

Chess, Jordan J; Montoya, Sergio A; Harvey, Tyler R; Ophus, Colin; Couture, Simon; Lomakin, Vitaliy; Fullerton, Eric E; McMorran, Benjamin J

2017-06-01

Recently, Lorentz transmission electron microscopy (LTEM) has helped researchers advance the emerging field of magnetic skyrmions. These magnetic quasi-particles, composed of topologically non-trivial magnetization textures, have a large potential for application as information carriers in low-power memory and logic devices. LTEM is one of a very few techniques for direct, real-space imaging of magnetic features at the nanoscale. For Fresnel-contrast LTEM, the transport of intensity equation (TIE) is the tool of choice for quantitative reconstruction of the local magnetic induction through the sample thickness. Typically, this analysis requires collection of at least three images. Here, we show that for uniform, thin, magnetic films, which includes many skyrmionic samples, the magnetic induction can be quantitatively determined from a single defocused image using a simplified TIE approach. Copyright © 2017 Elsevier B.V. All rights reserved.

A liquid crystal microlens array with aluminum and graphene electrodes for plenoptic imaging

NASA Astrophysics Data System (ADS)

Lei, Yu; Tong, Qing; Luo, Jun; Zhang, Xinyu; Sang, Hongshi; Xie, Changsheng

2015-12-01

Currently, several semiconducting oxide materials such as typical indium tin oxide are widely used as the transparent conducting electrodes (TCEs) in liquid crystal microlens arrays. In this paper, we fabricate a liquid crystal microlens array using graphene rather than semiconducting oxides as the TCE. Common optical experiments are carried out to acquire the focusing features of the graphene-based liquid crystal microlens array (GLCMLA) driven electrically. The acquired optical fields show that the GLCMLA can converge incident collimating lights efficiently. The relationship between the focal length and the applied voltage signal is presented. Then the GLCMLA is deployed in a plenoptic camera prototype and the raw images are acquired so as to verify their imaging capability. Our experiments demonstrate that graphene has already presented a broad application prospect in the area of adaptive optics.

Imaging of surgical margin in pancreatic metastasis using two-photon excited fluorescence microscopy

NASA Astrophysics Data System (ADS)

Chen, Jing; Hong, Zhipeng; Chen, Hong; Chen, Youting; Xu, Yahao; Zhu, Xiaoqin; Zhuo, Shuangmu; Shi, Zheng; Chen, Jianxin

2014-09-01

Two-photon excited fluorescence (TPEF) microscopy, has become a powerful tool for imaging unstained tissue samples at subcellular level in biomedical research. The purpose of this study was to determine whether TPEF imaging of histological sections without H-E staining can be used to identify the boundary between normal pancreas and pancreatic metastasis from renal cell carcinoma (RCC). The typical features such as the significant increase of cancerous nests, the absence of pancreatic ductal, the appearance of cancer cells were observed to present the boundary between normal pancreas and pancreatic metastasis from RCC. These results correlated well with the corresponding histological outcomes. With the advent of clinically miniaturized TPEF microscopy and integrative endoscopy, TPEF microscopy has the potential application on surgical location of pancreatic metastasis from RCC in the near future.

A feature-based approach to combine functional MRI, structural MRI and EEG brain imaging data.

PubMed

Calhoun, V; Adali, T; Liu, J

2006-01-01

The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using the Kullback-Leibler divergence. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. We show that combining data types can improve our ability to distinguish differences between groups.

Ambient Noise Tomography of central Java, with Transdimensional Bayesian Inversion

NASA Astrophysics Data System (ADS)

Zulhan, Zulfakriza; Saygin, Erdinc; Cummins, Phil; Widiyantoro, Sri; Nugraha, Andri Dian; Luehr, Birger-G.; Bodin, Thomas

2014-05-01

Delineating the crustal structure of central Java is crucial for understanding its complex tectonic setting. However, seismic imaging of the strong heterogeneity typical of such a tectonically active region can be challenging, particularly in the upper crust where velocity contrasts are strongest and steep body wave ray-paths provide poor resolution. We have applied ambient noise cross correlation of pair stations in central Java, Indonesia by using the MERapi Amphibious EXperiment (MERAMEX) dataset. The data were collected between May to October 2004. We used 120 of 134 temporary seismic stations for about 150 days of observation, which covered central Java. More than 5000 Rayleigh wave Green's function were extracted by cross-correlating the noise simultaneously recorded at available station pairs. We applied a fully nonlinear 2D Bayesian inversion technique to the retrieved travel times. Features in the derived tomographic images correlate well with previous studies, and some shallow structures that were not evident in previous studies are clearly imaged with Ambient Noise Tomography. The Kendeng Basin and several active volcanoes appear with very low group velocities, and anomalies with relatively high velocities can be interpreted in terms of crustal sutures and/or surface geological features.

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework

PubMed Central

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation. PMID:28225811

Surface-Constrained Volumetric Brain Registration Using Harmonic Mappings

PubMed Central

Joshi, Anand A.; Shattuck, David W.; Thompson, Paul M.; Leahy, Richard M.

2015-01-01

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRI). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the inter-subject alignment of expert-labeled sub-cortical structures after registration. PMID:18092736

Automated numerical simulation of biological pattern formation based on visual feedback simulation framework.

PubMed

Sun, Mingzhu; Xu, Hui; Zeng, Xingjuan; Zhao, Xin

2017-01-01

There are various fantastic biological phenomena in biological pattern formation. Mathematical modeling using reaction-diffusion partial differential equation systems is employed to study the mechanism of pattern formation. However, model parameter selection is both difficult and time consuming. In this paper, a visual feedback simulation framework is proposed to calculate the parameters of a mathematical model automatically based on the basic principle of feedback control. In the simulation framework, the simulation results are visualized, and the image features are extracted as the system feedback. Then, the unknown model parameters are obtained by comparing the image features of the simulation image and the target biological pattern. Considering two typical applications, the visual feedback simulation framework is applied to fulfill pattern formation simulations for vascular mesenchymal cells and lung development. In the simulation framework, the spot, stripe, labyrinthine patterns of vascular mesenchymal cells, the normal branching pattern and the branching pattern lacking side branching for lung branching are obtained in a finite number of iterations. The simulation results indicate that it is easy to achieve the simulation targets, especially when the simulation patterns are sensitive to the model parameters. Moreover, this simulation framework can expand to other types of biological pattern formation.

Internal tide transformation across a continental slope off Cape Sines, Portugal

NASA Astrophysics Data System (ADS)

Small, Justin

2002-04-01

During the INTIFANTE 99 experiment in July 1999, observations were made of a prominent internal undular bore off Cape Sines, Portugal. The feature was always present and dominant in a collection of synthetic aperture radar (SAR) images of the area covering the period before, during and after the trial. During the trial, rapid dissemination of SAR data to the survey ship enabled assessment of the progression of the feature, and the consequent planning of a survey of the bore coincident with a new SAR image. Large amplitude internal waves of 50 m amplitude in 250 m water depth, and 40 m in 100 m depth, were observed. The images show that the position of the feature is linked to the phase of the tide, suggesting an internal tide origin. The individual packets of internal waves contain up to seven waves with wavelengths in the range of 500-1500 m, and successive packets are separated by internal tide distances of typically 16-20 km, suggesting phase speeds of 0.35-0.45 m s -1. The internal waves were coherent over crest lengths of between 15 and 70 km, the longer wavefronts being due to the merging of packets. This paper uses the SAR data to detail the transformation of the wave packet as it passes across the continental slope and approaches the coast. The generation sites for the feature are discussed and reasons for its unusually large amplitude are hypothesised. It is concluded that generation at critical slopes of the bathymetry and non-linear interactions are the likely explanations for the large amplitudes.

An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies

NASA Astrophysics Data System (ADS)

Gao, Hua; Ho, Luis C.

2017-08-01

The development of two-dimensional (2D) bulge-to-disk decomposition techniques has shown their advantages over traditional one-dimensional (1D) techniques, especially for galaxies with non-axisymmetric features. However, the full potential of 2D techniques has yet to be fully exploited. Secondary morphological features in nearby disk galaxies, such as bars, lenses, rings, disk breaks, and spiral arms, are seldom accounted for in 2D image decompositions, even though some image-fitting codes, such as GALFIT, are capable of handling them. We present detailed, 2D multi-model and multi-component decomposition of high-quality R-band images of a representative sample of nearby disk galaxies selected from the Carnegie-Irvine Galaxy Survey, using the latest version of GALFIT. The sample consists of five barred and five unbarred galaxies, spanning Hubble types from S0 to Sc. Traditional 1D decomposition is also presented for comparison. In detailed case studies of the 10 galaxies, we successfully model the secondary morphological features. Through a comparison of best-fit parameters obtained from different input surface brightness models, we identify morphological features that significantly impact bulge measurements. We show that nuclear and inner lenses/rings and disk breaks must be properly taken into account to obtain accurate bulge parameters, whereas outer lenses/rings and spiral arms have a negligible effect. We provide an optimal strategy to measure bulge parameters of typical disk galaxies, as well as prescriptions to estimate realistic uncertainties of them, which will benefit subsequent decomposition of a larger galaxy sample.

An Optimal Strategy for Accurate Bulge-to-disk Decomposition of Disk Galaxies

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

Gao Hua; Ho, Luis C.

The development of two-dimensional (2D) bulge-to-disk decomposition techniques has shown their advantages over traditional one-dimensional (1D) techniques, especially for galaxies with non-axisymmetric features. However, the full potential of 2D techniques has yet to be fully exploited. Secondary morphological features in nearby disk galaxies, such as bars, lenses, rings, disk breaks, and spiral arms, are seldom accounted for in 2D image decompositions, even though some image-fitting codes, such as GALFIT, are capable of handling them. We present detailed, 2D multi-model and multi-component decomposition of high-quality R -band images of a representative sample of nearby disk galaxies selected from the Carnegie-Irvine Galaxymore » Survey, using the latest version of GALFIT. The sample consists of five barred and five unbarred galaxies, spanning Hubble types from S0 to Sc. Traditional 1D decomposition is also presented for comparison. In detailed case studies of the 10 galaxies, we successfully model the secondary morphological features. Through a comparison of best-fit parameters obtained from different input surface brightness models, we identify morphological features that significantly impact bulge measurements. We show that nuclear and inner lenses/rings and disk breaks must be properly taken into account to obtain accurate bulge parameters, whereas outer lenses/rings and spiral arms have a negligible effect. We provide an optimal strategy to measure bulge parameters of typical disk galaxies, as well as prescriptions to estimate realistic uncertainties of them, which will benefit subsequent decomposition of a larger galaxy sample.« less

Geologically recent small-scale surface features in Meridiani Planum and Gale Crater, Mars

NASA Astrophysics Data System (ADS)

Horne, David

2014-05-01

Enigmatic small scale (<1m) depositional and erosional features have been imaged at several locations in the equatorial Meridiani Planum region by the rover Opportunity. They occur in loose, dark basaltic sands partly covering exposures of light-toned bedrock. Leveed fissures are narrow, elongate, steep-sided depressions flanked by raised levees or half-cones of soil, typically 2-10 cm wide and up to 50 cm long in most cases. Some cross-cut and are therefore younger than eolian ripples thought to have last been active c. 50,000 years ago. Gutters are elongate, straight or sinuous surface depressions, typically 2-10cm wide and 1-5 cm deep, sometimes internally terraced or with a hollow near one end, and in one case seem to give way to small depositional fans downslope; they have the appearance of having been formed by liquid flow rather than by wind erosion. Leveed fissures were imaged at more than 25 locations by Opportunity between 2004 and 2013, particularly near the rims of Endurance, Erebus and Endeavour craters, but also on the plains between Santa Maria and Endeavour craters; sharply-defined gutters are less common but examples were imaged close to the rim of Endurance and on the approach to Endeavour, whereas subdued, possibly wind-softened examples are more widespread. Scrutiny of images obtained by the rover Spirit in Gusev Crater between 2004 and 2010 has so far failed to find any leveed fissures or gutters, but examples of both types of features, as well as numerous small holes suggestive of surface sediment falling into underlying voids, were imaged by the rover Curiosity in the Yellowknife Bay region of Gale Crater during 2013. Leveed fissures appear to have been formed by venting from beneath. Ground disturbance by the rover can be ruled out in many cases by the appearance of features in images taken before close approach. Blowholes seem plausible close to crater rims (where wind might enter a connected void system through a crater wall) but less so in plains areas between craters. Fumaroles seem unlikely since there is no other evidence of geologically young volcanic activity in the region. There is evidence elsewhere that contemporary ground-ice thaw and consequent transient surface run-off may occur occasionally under present conditions in low, near-equatorial latitudes on Mars; short-lived (even for just a few minutes) meltwater emission and flow at the surface could erode gutters before evaporating. The decomposition of buried pockets of methane clathrates, which theoretical considerations suggest might be present and stable even in equatorial regions, could give rise to both methane venting (leveed fissures) and transient surface water (gutters). Another possibility is the decomposition, due to local changes in thermal conditions, of hydrated magnesium sulphates in the bedrock, releasing liquid water. Whatever their explanation, these features hint at previously unrecognized, young martian surface processes which may even be active at the present day; in this context, the apparent downslope extension of a discrete dark dust streak on Burns Cliff (inside Endurance Crater), during Opportunity's approach to that locality, is particularly thought-provoking.

Analysis of marine multi-channel seismic data using a 2D continuous wavelet transform

NASA Astrophysics Data System (ADS)

Vuong, A. K.; Zhang, J.; Gibson, R. L.; Sager, W. W.

2011-12-01

Marine multi-channel seismic (MCS) profiles provide important constraints on crustal structure beneath the sea floor. MCS data usually provide good images of the upper part of the oceanic crust, especially in sedimentary layers. In contrast, it is often difficult to interpret deeper layers, especially those within the igneous basement, which is often nearly seismically transparent. That difference in interpretability occurs because sediments typically have continuous, well-layered and easily-traced structural features, whereas volcanic materials are characterized by smaller features with poorer lateral continuity and often with weak impedance contrasts. Since the basement tends to create weaker reflections, the signal-to-noise ratio decreases, creating additional difficulties that can be exacerbated by the presence of multiples generated by the sea floor and other sources of noise. However, it is still important to characterize the basement accurately to better understand oceanic crust formation and associated basaltic volcanism. We analyzed marine MCS data collected by R/V Marcus G. Langseth across the TAMU Massif of Shatsky Rise in the Northwest Pacific. The seismic data from this experiment display the typical problems in imaging basement features. Therefore, we seek to facilitate interpretation by applying 2-D continuous wavelet transforms to the data. Conventional Fourier methods transform 2-D seismic data from space and time domains to wavenumber and frequency, but the results are global in that there is no knowledge of temporal or spatial variations in frequency or wavenumber content. In contrast, wavelet transforms provide estimates of the local frequency and wavenumber content of the seismic image. The transform achieves this result by utilizing a localized, 2D wavelet function instead of the infinite sines and cosines applied in Fourier transforms. We utilize an anisotropic Mexican hat wavelet, where the horizontal and vertical scales are related to wavelength and period of the data, respectively. When analyzing the Shatsky Rise data set, we find, for example, that much of the noise in the seismic image of the basement is at small wavelengths corresponding to several traces, about 25 m. Using the wavelet transforms, we can extract reflection events at longer wavelengths corresponding to expected features in the subsurface. Observing reflections at a certain wavelength provides an estimate of the size scale of the associated geologic structures. The results at a frequency of 31.25 Hz, near the dominant frequency of the data, provide images of reflectors in the deep part of oceanic crust with scales from 200 m to 2000 m that are much easier to interpret than in the original seismic image. In particular, at scales from 200 m to 1000 m, we can see many reflectors with consistent with sizes and locations for localized magma intrusions into the oceanic crust. However, for spatial scales of about 2000 m, only a few reflectors are observed, suggesting there are fewer intrusions of this dimension. These features can also be examined at a range of frequencies to provide additional insights, and the wavelet transform can also be generalized to estimate dips of reflectors.

3D superwide-angle one-way propagator and its application in seismic modeling and imaging

NASA Astrophysics Data System (ADS)

Jia, Xiaofeng; Jiang, Yunong; Wu, Ru-Shan

2018-07-01

Traditional one-way wave-equation based propagators have been widely used in past decades. Comparing to two-way propagators, one-way methods have higher efficiency and lower memory demands. These two features are especially important in solving large-scale 3D problems. However, regular one-way propagators cannot simulate waves that propagate in large angles within 90° because of their inherent wide angle limitation. Traditional one-way can only propagate along the determined direction (e.g., z-direction), so simulation of turning waves is beyond the ability of one-way methods. We develop 3D superwide-angle one-way propagator to overcome angle limitation and to simulate turning waves with superwide-angle propagation angle (>90°) for modeling and imaging complex geological structures. Wavefields propagating along vertical and horizontal directions are combined using typical stacking scheme. A weight function related to the propagation angle is used for combining and updating wavefields in each propagating step. In the implementation, we use graphics processing units (GPU) to accelerate the process. Typical workflow is designed to exploit the advantages of GPU architecture. Numerical examples show that the method achieves higher accuracy in modeling and imaging steep structures than regular one-way propagators. Actually, superwide-angle one-way propagator can be applied based on any one-way method to improve the effects of seismic modeling and imaging.

Calibration and validation of projection lithography in chemically amplified resist systems using fluorescence imaging

NASA Astrophysics Data System (ADS)

Mason, Michael D.; Ray, Krishanu; Feke, Gilbert D.; Grober, Robert D.; Pohlers, Gerd; Cameron, James F.

2003-05-01

Coumarin 6 (C6), a pH sensitive fluorescent molecule were doped into commercial resist systems to demonstrate a cost-effective fluorescence microscopy technique for detecting latent photoacid images in exposed chemically amplified resist films. The fluorescenec image contrast is optimized by carefully selecting optical filters to match the spectroscopic properties of C6 in the resist matrices. We demonstrate the potential of this technique for two sepcific non-invasive applications. First, a fast, conventient, fluorescence technique is demonstrated for determination of quantum yeidsl of photo-acid generation. Since the Ka of C6 in the 193nm resist system lies wihtin the range of acid concentrations that can be photogenerated, we have used this technique to evaluate the acid generation efficiency of various photo-acid generators (PAGs). The technique is based on doping the resist formulations containing the candidate PAGs with C6, coating one wafer per PAG, patterning the wafer with a dose ramp and spectroscopically imaging the wafers. The fluorescence of each pattern in the dose ramp is measured as a single image and analyzed with the optical titration model. Second, a nondestructive in-line diagnostic technique is developed for the focus calibration and validation of a projection lithography system. Our experimental results show excellent correlation between the fluorescence images and scanning electron microscope analysis of developed features. This technique has successfully been applied in both deep UV resists e.g., Shipley UVIIHS resist and 193 nm resists e.g., Shipley Vema-type resist. This method of focus calibration has also been extended to samples with feature sizes below the diffraction limit where the pitch between adjacent features is on the order of 300 nm. Image capture, data analysis, and focus latitude verification are all computer controlled from a single hardware/software platform. Typical focus calibration curves can be obtained within several minutes.

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

Classification of small lesions in dynamic breast MRI: Eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement over time.

PubMed

Nagarajan, Mahesh B; Huber, Markus B; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2013-10-01

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they don't exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of sixty annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were also used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter , thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( p < 0.05). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

SVGenes: a library for rendering genomic features in scalable vector graphic format.

PubMed

Etherington, Graham J; MacLean, Daniel

2013-08-01

Drawing genomic features in attractive and informative ways is a key task in visualization of genomics data. Scalable Vector Graphics (SVG) format is a modern and flexible open standard that provides advanced features including modular graphic design, advanced web interactivity and animation within a suitable client. SVGs do not suffer from loss of image quality on re-scaling and provide the ability to edit individual elements of a graphic on the whole object level independent of the whole image. These features make SVG a potentially useful format for the preparation of publication quality figures including genomic objects such as genes or sequencing coverage and for web applications that require rich user-interaction with the graphical elements. SVGenes is a Ruby-language library that uses SVG primitives to render typical genomic glyphs through a simple and flexible Ruby interface. The library implements a simple Page object that spaces and contains horizontal Track objects that in turn style, colour and positions features within them. Tracks are the level at which visual information is supplied providing the full styling capability of the SVG standard. Genomic entities like genes, transcripts and histograms are modelled in Glyph objects that are attached to a track and take advantage of SVG primitives to render the genomic features in a track as any of a selection of defined glyphs. The feature model within SVGenes is simple but flexible and not dependent on particular existing gene feature formats meaning graphics for any existing datasets can easily be created without need for conversion. The library is provided as a Ruby Gem from https://rubygems.org/gems/bio-svgenes under the MIT license, and open source code is available at https://github.com/danmaclean/bioruby-svgenes also under the MIT License. dan.maclean@tsl.ac.uk.

Role of MRI in the early diagnosis of tubal ectopic pregnancy.

PubMed

Si, Ming-Jue; Gui, Shuang; Fan, Qin; Han, Hong-Xiu; Zhao, Qian-Qian; Li, Zhi-Xin; Zhao, Jiang-Min

2016-07-01

To determine the role of MRI in the early diagnosis of tubal ectopic pregnancy (EP). Clinical and MRI features of 27 cases of tubal pregnancy were reviewed. A thick-walled gestational sac (GS)-like structure was demonstrated lateral to the uterus in all cases. On T2-weighted images, the thick wall typically exhibited 3 discrete rings in 22 cases (81 %), among which 17 cases (63 %) displayed small vessels and 6 cases (33 %) exhibited small areas of fresh haemorrhage inside the thick wall. The contents demonstrated non-specific liquid in 26 %, papillary solid components in 56 %, and fresh blood or fluid-fluid level in 19 % of the cases. Dilatation of the affected fallopian tube associated with hematosalpinx was demonstrated in 18 cases (67 %) and marked enhancement of the tubal wall was observed in 22 cases (81 %). No correlation was found between the size of the GS and the estimated gestational age (r = 0.056). MRI plays an important role in the early diagnosis and management of tubal pregnancy. The characteristic MRI features include a GS-like structure with a "three rings" appearance on T2-weighted images, presence of solid components in the sac, dilatation of the affected fallopian tube with hematosalpinx, and tubal wall enhancement. • MR imaging has served as a problem-solving procedure in ectopic pregnancy. • MR imaging features can be criteria for early diagnosis of tubal pregnancy. • Detailed assessment of ectopic implantation is necessary for management decision-making.

Adaptive skin segmentation via feature-based face detection

NASA Astrophysics Data System (ADS)

Taylor, Michael J.; Morris, Tim

2014-05-01

Variations in illumination can have significant effects on the apparent colour of skin, which can be damaging to the efficacy of any colour-based segmentation approach. We attempt to overcome this issue by presenting a new adaptive approach, capable of generating skin colour models at run-time. Our approach adopts a Viola-Jones feature-based face detector, in a moderate-recall, high-precision configuration, to sample faces within an image, with an emphasis on avoiding potentially detrimental false positives. From these samples, we extract a set of pixels that are likely to be from skin regions, filter them according to their relative luma values in an attempt to eliminate typical non-skin facial features (eyes, mouths, nostrils, etc.), and hence establish a set of pixels that we can be confident represent skin. Using this representative set, we train a unimodal Gaussian function to model the skin colour in the given image in the normalised rg colour space - a combination of modelling approach and colour space that benefits us in a number of ways. A generated function can subsequently be applied to every pixel in the given image, and, hence, the probability that any given pixel represents skin can be determined. Segmentation of the skin, therefore, can be as simple as applying a binary threshold to the calculated probabilities. In this paper, we touch upon a number of existing approaches, describe the methods behind our new system, present the results of its application to arbitrary images of people with detectable faces, which we have found to be extremely encouraging, and investigate its potential to be used as part of real-time systems.

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

NASA Astrophysics Data System (ADS)

Senthil Kumar, A.; Keerthi, V.; Manjunath, A. S.; Werff, Harald van der; Meer, Freek van der

2010-08-01

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.

Lexical Processing in Toddlers with ASD: Does Weak Central Coherence Play a Role?

PubMed

Ellis Weismer, Susan; Haebig, Eileen; Edwards, Jan; Saffran, Jenny; Venker, Courtney E

2016-12-01

This study investigated whether vocabulary delays in toddlers with autism spectrum disorders (ASD) can be explained by a cognitive style that prioritizes processing of detailed, local features of input over global contextual integration-as claimed by the weak central coherence (WCC) theory. Thirty toddlers with ASD and 30 younger, cognition-matched typical controls participated in a looking-while-listening task that assessed whether perceptual or semantic similarities among named images disrupted word recognition relative to a neutral condition. Overlap of perceptual features invited local processing whereas semantic overlap invited global processing. With the possible exception of a subset of toddlers who had very low vocabulary skills, these results provide no evidence that WCC is characteristic of lexical processing in toddlers with ASD.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Maunder Crater

NASA Technical Reports Server (NTRS)

2002-01-01

(Released 24 May 2002) The Science This image is of a portion of Maunder Crater located at about 49 S and 358 W (2 E). There are a number of interesting features in this image. The lower left portion of the image shows a series of barchan dunes that are traveling from right to left. The sand does not always form dunes as can be seen in the dark and diffuse areas surrounding the dune field. The other interesting item in this image are the gullies that can be seen streaming down from just beneath a number of sharp ridgelines in the upper portion of the image. These gullies were first seen by the MOC camera on the MGS spacecraft and it is though that they formed by groundwater leaking out of the rock layers on the walls of craters. The water runs down the slope and forms the fluvial features seen in the image. Other researchers think that these features could be formed by other fluids, such as CO₂. These features are typically seen on south facing slopes in the southern hemisphere, though this image has gullies on north facing slopes as well. The Story Little black squigglies seem to worm their way down the left-hand side of this image. These land features are called barchan (crescent-shaped) dunes. Barchan dunes are found in sandy deserts on Earth, so it's no surprise the Martian wind makes them a common site on the red planet too. They were first named by a Russian scientist named Alexander von Middendorf, who studied the inland desert dunes of Turkistan. The barchan dunes in this image occur in the basin of Maunder crater on Mars, and are traveling from right to left. The sand does not always form dunes, though, as can be seen in the dark areas of scattered sand surrounding the dune field. Look for the streaming gullies that appear just beneath a number of sharp ridgelines in the upper portion of the image. These gullies were first discovered by the Mars Orbital Camera on the Mars Global Surveyor spacecraft. While most crater gullies are found on south-facing slopes in the southern hemisphere of Mars, you can see from this image that they occur on north-facing slopes as well. Comparing where gullies appear will help scientists understand more about the conditions under which they form. Some researchers are really excited about gullies on Mars, because they believe these surface tracings might be signs that groundwater has leaked out of the rock layers on the walls of craters. If that's true, the water runs down the slope and forms the flow-like features seen in the image. Scientists can get into some really hot debates, however. Other researchers think that these features could be formed by other fluids, such as carbon dioxide. No one knows for sure, so a lot of heads will be studiously bent over these images, continuing to study them closely. The neat thing about science is that the way you get closer to the truth is to hypothesize and then test, test, and test again. Debate for scientists is seen as an essential means of making sure that no wrong assumptions are made or that no important factor is left out. It's what keeps the field interesting and dynamic . . . and sometimes quite loud and entertaining!

Optical Emission Associated with the Galactic Supernova Remnant G179.0+2.6

NASA Astrophysics Data System (ADS)

How, Thomas G.; Fesen, Robert A.; Neustadt, Jack M. M.; Black, Christine S.; Outters, Nicolas

2018-04-01

Narrow passband optical images of the large Galactic supernova remnant G179.0+2.6 reveal a faint but nearly complete emission shell dominated by strong [O 3] 4959,5007 Å line emission. The remnant's optical emission, which consists of both diffuse and filamentary features, is brightest along its southern and northeastern limbs. Deep Hα images detect little coincidence emission indicating an unusually high [O 3]/Hα emission ratio for such a large and apparently old remnant. Low-dispersion optical spectra of several regions confirm large [O 3]/Hα line ratios with typical values around 10. The dominance of [O 3] emission for the majority of the remnant's optical filaments suggests shock velocities above 100 km s-1 are present throughout most of the remnant, likely reflecting a relatively low density ambient ISM. The remnant's unusually strong [O 3] emission adds to the remnant's interesting set of properties which include a thick radio emission shell, radial polarization of its radio emission like that typically seen in young supernova remnants, and an unusually slow-rotating gamma-ray pulsar with a characteristic spin-down age ≃ 50 kyr.

Measuring the accelerating effect of the planetary-scale waves on Venus observed with UVI/AKATSUKI and ground-based telescopes

NASA Astrophysics Data System (ADS)

Imai, M.; Kouyama, T.; Takahashi, Y.; Watanabe, S.; Yamazaki, A.; Yamada, M.; Nakamura, M.; Satoh, T.; Imamura, T.; Nakaoka, T.; Kawabata, M.; Yamanaka, M.; Kawabata, K. S.

2017-12-01

Venus has a global cloud layer, and the atmosphere rotates with the speed over 100 m/s. The scattering of solar radiance and absorber in clouds cause the strong dark and bright contrast in 365 nm unknown absorption bands. The Japanese Venus orbiter AKATSUKI and the onboard instrument UVI capture 100 km mesoscale cloud features over the entire visible dayside area. In contrast, planetary-scale features are observed when the orbiter is at the moderate distance from Venus and when the Sun-Venus-orbiter phase angle is smaller than 45 deg. Cloud top wind velocity was measured with the mesoscale cloud tracking technique, however, observations of the propagation velocity and its variation of the planetary-scale feature are not well conducted because of the limitation of the observable area. The purpose of the study is measuring the effect of wind acceleration by planetary-scale waves. Each cloud motion can be represented as the wind and phase velocity of the planetary-scale waves, respectively. We conducted simultaneous observations of the zonal motion of both mesoscale and planetary-scale feature using UVI/AKATSUKI and ground-based Pirka and Kanata telescopes in Japan. Our previous ground-based observation revealed the periodicity change of planetary-scale waves with a time scale of a couple of months. For the initial analysis of UVI images, we used the time-consecutive images taken in the orbit #32. During this orbit (from Nov. 13 to 20, 2016), 7 images were obtained with 2 hr time-interval in a day whose spatial resolution ranged from 10-35 km. To investigate the typical mesoscale cloud motion, the Gaussian-filters with sigma = 3 deg. were used to smooth geometrically mapped images with 0.25 deg. resolution. Then the amount of zonal shift for each 5 deg. latitudinal bands between the pairs of two time-consecutive images were estimated by searching the 2D cross-correlation maximum. The final wind velocity (or rotation period) for mesoscale features were determined with a small error about +/- 0.1-day period in equatorial region (Figure 2). The same method will be applied for planetary-scale features captured by UVI, and ground-based observations compensate the discontinuity in UVI data. At the presentation, the variability in winds and wave propagation velocity with the time scale of a couple of months will be shown.

Long-term Monitoring of Comet 103P/Hartley 2

NASA Astrophysics Data System (ADS)

Lin, Z.-Y.; Lara, L. M.; Ip, W.-H.

2013-07-01

We report the spectrophotometric, photometric, and imaging monitoring results of comet 103P/Hartley 2 obtained at the Lulin (1 m), Calar Alto (2.2 m), and Beijing Astronomical (2.16 m) observatories from 2010 April to December. We found that a dust feature in the sunward direction was detected starting from the end of September until the beginning of December (our last observation from the Lulin and Calar Alto observatories). Two distinct sunward jet features in the processed images were observed on October 11 and after October 29 until November 2. In parallel, the CN images reveal two asymmetrical jet features which are nearly perpendicular to the Sun-nucleus direction, these asymmetrical features imply that the comet was in a nearly side-on view in late October and early November. In addition to the jet features, the average result of the C2-to-CN production rate ratio ranges from 0.7 to 1.5, consistent with 103P/Hartley 2 being of typical cometary chemistry. We found that the rh dependence for the dust production rate, Afρ (5000 km), is -3.75 ± 0.45 before perihelion and -3.44 ± 1.20 during the post-perihelion period. We detected higher dust reddening around the optocenter and decreased reddening along the sunward jet feature. We concluded that higher dust reddening could be associated with strong jet activity while lower dust reddening could be associated with the outburst or might imply changes in the optical properties. The average dust color did not appear to vary significantly as the comet passed through perihelion. Based on observations collected at the Centro Astronómico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck Institut für Astronomie and the Instituto de Astrofísica de Andalucía (CSIC), at Lulin Observatory operated by the Institute of Astronomy, National Central University in Taiwan, and at Xinglong Station inaugurated by the National Astronomical Observatory (BAO), Beijing.

Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration

PubMed Central

Ji, Songbai; Fan, Xiaoyao; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2014-01-01

Stereovision is an important intraoperative imaging technique that captures the exposed parenchymal surface noninvasively during open cranial surgery. Estimating cortical surface shift efficiently and accurately is critical to compensate for brain deformation in the operating room (OR). In this study, we present an automatic and robust registration technique based on optical flow (OF) motion tracking to compensate for cortical surface displacement throughout surgery. Stereo images of the cortical surface were acquired at multiple time points after dural opening to reconstruct three-dimensional (3D) texture intensity-encoded cortical surfaces. A local coordinate system was established with its z-axis parallel to the average surface normal direction of the reconstructed cortical surface immediately after dural opening in order to produce two-dimensional (2D) projection images. A dense displacement field between the two projection images was determined directly from OF motion tracking without the need for feature identification or tracking. The starting and end points of the displacement vectors on the two cortical surfaces were then obtained following spatial mapping inversion to produce the full 3D displacement of the exposed cortical surface. We evaluated the technique with images obtained from digital phantoms and 18 surgical cases – 10 of which involved independent measurements of feature locations acquired with a tracked stylus for accuracy comparisons, and 8 others of which 4 involved stereo image acquisitions at three or more time points during surgery to illustrate utility throughout a procedure. Results from the digital phantom images were very accurate (0.05 pixels). In the 10 surgical cases with independently digitized point locations, the average agreement between feature coordinates derived from the cortical surface reconstructions was 1.7–2.1 mm relative to those determined with the tracked stylus probe. The agreement in feature displacement tracking was also comparable to tracked probe data (difference in displacement magnitude was <1 mm on average). The average magnitude of cortical surface displacement was 7.9 ± 5.7 mm (range 0.3–24.4 mm) in all patient cases with the displacement components along gravity being 5.2 ± 6.0 mm relative to the lateral movement of 2.4 ± 1.6 mm. Thus, our technique appears to be sufficiently accurate and computationally efficiency (typically ~15 s), for applications in the OR. PMID:25077845

Investigating Mars: Pavonis Mons

NASA Image and Video Library

2017-11-07

This image shows part smaller summit caldera of Pavonis Mons. This caldera is approximately 5km deep. Near the bottom of the image is a region where part of the caldera side has collapsed into the bottom of the caldera. In shield volcanoes calderas are typically formed where the surface collapses into the void formed by an emptied magma chamber. Pavonis Mons is one of the three aligned Tharsis Volcanoes. The four Tharsis volcanoes are Ascreaus Mons, Pavonis Mons, Arsia Mons, and Olympus Mars. All four are shield type volcanoes. Shield volcanoes are formed by lava flows originating near or at the summit, building up layers upon layers of lava. The Hawaiian islands on Earth are shield volcanoes. The three aligned volcanoes are located along a topographic rise in the Tharsis region. Along this trend there are increased tectonic features and additional lava flows. Pavonis Mons is the smallest of the four volcanoes, rising 14km above the mean Mars surface level with a width of 375km. It has a complex summit caldera, with the smallest caldera deeper than the larger caldera. Like most shield volcanoes the surface has a low profile. In the case of Pavonis Mons the average slope is only 4 degrees. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 32776 Latitude: 0.446561 Longitude: 247.283 Instrument: VIS Captured: 2009-05-05 03:21 https://photojournal.jpl.nasa.gov/catalog/PIA22023

Wide-field and high-resolution optical imaging for early detection of oral neoplasia

NASA Astrophysics Data System (ADS)

Pierce, Mark C.; Schwarz, Richard A.; Rosbach, Kelsey; Roblyer, Darren; Muldoon, Tim; Williams, Michelle D.; El-Naggar, Adel K.; Gillenwater, Ann M.; Richards-Kortum, Rebecca

2010-02-01

Current procedures for oral cancer screening typically involve visual inspection of the entire tissue surface at risk under white light illumination. However, pre-cancerous lesions can be difficult to distinguish from many benign conditions when viewed under these conditions. We have developed wide-field (macroscopic) imaging system which additionally images in cross-polarized white light, narrowband reflectance, and fluorescence imaging modes to reduce specular glare, enhance vascular contrast, and detect disease-related alterations in tissue autofluorescence. We have also developed a portable system to enable high-resolution (microscopic) evaluation of cellular features within the oral mucosa in situ. This system is a wide-field epi-fluorescence microscope coupled to a 1 mm diameter, flexible fiber-optic imaging bundle. Proflavine solution was used to specifically label cell nuclei, enabling the characteristic differences in N/C ratio and nuclear distribution between normal, dysplastic, and cancerous oral mucosa to be quantified. This paper discusses the technical design and performance characteristics of these complementary imaging systems. We will also present data from ongoing clinical studies aimed at evaluating diagnostic performance of these systems for detection of oral neoplasia.

A Case of Desmoplastic Small Round Cell Tumor.

PubMed

Reisner, David; Brahee, Deborah; Patel, Shweta; Hartman, Matthew

2015-08-01

Desmoplastic small round cell tumor is a rare, aggressive tumor primarily affecting young males. It is considered a childhood cancer, and is characterized by a unique chromosomal translocation which leads to failure to suppress tumor growth. It is classified as a soft tissue sarcoma, sharing some features with other small round cell tumors such as Ewing's Sarcoma and primitive neuroectodermal tumor. Typical imaging findings include multiple heterogeneous, lobular abdominal masses, which can grow very large. Often there is a dominant mass with additional peritoneal, omental, retroperitoneal and retrovesical masses. Prognosis is relatively poor with a 3 year survival rate of 50% in those treated aggressively with surgical resection, chemotherapy, and radiation therapy. The clinical presentation, imaging characteristics and pathology are discussed in regards to a recent case.

Functional MRI registration with tissue-specific patch-based functional correlation tensors.

PubMed

Zhou, Yujia; Zhang, Han; Zhang, Lichi; Cao, Xiaohuan; Yang, Ru; Feng, Qianjin; Yap, Pew-Thian; Shen, Dinggang

2018-06-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods. © 2018 Wiley Periodicals, Inc.

Photographer : JPL Range : 312, 000 kilometers (195,000 miles) This photo of Ganymede (Ice Giant)

NASA Technical Reports Server (NTRS)

1979-01-01

Photographer : JPL Range : 312, 000 kilometers (195,000 miles) This photo of Ganymede (Ice Giant) was taken from Voyager 2 and shows features down to about 5 to 6 kilometers across. Different types of terrain common on Ganymede's surface are visible. The boundary of the largest region of dark ancient terrain on Ganymede can be seen to the east (right), revealing some of the light linear features which may be all that remains of a large ancient impact structure similar to the large ring structure on Callisto. The broad light regions running through the image are the typical grooved structures seen within another example of what might be evidence of large scale lateral motion in Ganymede's crust. The band of grooved terrain (about 100 kilometers wide) in this region appears to be offset by 50 kilometers or more on the left hand edge by a linear feature perpendicular to it. A feature similar to this one was previously discovered by Voyager 1. These are the first clear examples of strike-slip style faulting on any planet other than Earth. Many examples of craters of all ages can be seen in this image, ranging from fresh, bright ray craters to large, subdued circular markings thought to be the 'scars' of large ancient impacts that have been flatteded by glacier-like flows.

Rapid extraction of image texture by co-occurrence using a hybrid data structure

NASA Astrophysics Data System (ADS)

Clausi, David A.; Zhao, Yongping

2002-07-01

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% ( σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method.

Technical Note: Rod phantom analysis for comparison of PET detector sampling and reconstruction methods.

PubMed

Wollenweber, Scott D; Kemp, Brad J

2016-11-01

This investigation aimed to develop a scanner quantification performance methodology and compare multiple metrics between two scanners under different imaging conditions. Most PET scanners are designed to work over a wide dynamic range of patient imaging conditions. Clinical constraints, however, often impact the realization of the entitlement performance for a particular scanner design. Using less injected dose and imaging for a shorter time are often key considerations, all while maintaining "acceptable" image quality and quantitative capability. A dual phantom measurement including resolution inserts was used to measure the effects of in-plane (x, y) and axial (z) system resolution between two PET/CT systems with different block detector crystal dimensions. One of the scanners had significantly thinner slices. Several quantitative measures, including feature contrast recovery, max/min value, and feature profile accuracy were derived from the resulting data and compared between the two scanners and multiple phantoms and alignments. At the clinically relevant count levels used, the scanner with thinner slices had improved performance of approximately 2%, averaged over phantom alignments, measures, and reconstruction methods, for the head-sized phantom, mainly demonstrated with the rods aligned perpendicular to the scanner axis. That same scanner had a slightly decreased performance of -1% for the larger body-size phantom, mostly due to an apparent noise increase in the images. Most of the differences in the metrics between the two scanners were less than 10%. Using the proposed scanner performance methodology, it was shown that smaller detector elements and a larger number of image voxels require higher count density in order to demonstrate improved image quality and quantitation. In a body imaging scenario under typical clinical conditions, the potential advantages of the design must overcome increases in noise due to lower count density.

a Fast Approach for Stitching of Aerial Images

NASA Astrophysics Data System (ADS)

Moussa, A.; El-Sheimy, N.

2016-06-01

The last few years have witnessed an increasing volume of aerial image data because of the extensive improvements of the Unmanned Aerial Vehicles (UAVs). These newly developed UAVs have led to a wide variety of applications. A fast assessment of the achieved coverage and overlap of the acquired images of a UAV flight mission is of great help to save the time and cost of the further steps. A fast automatic stitching of the acquired images can help to visually assess the achieved coverage and overlap during the flight mission. This paper proposes an automatic image stitching approach that creates a single overview stitched image using the acquired images during a UAV flight mission along with a coverage image that represents the count of overlaps between the acquired images. The main challenge of such task is the huge number of images that are typically involved in such scenarios. A short flight mission with image acquisition frequency of one second can capture hundreds to thousands of images. The main focus of the proposed approach is to reduce the processing time of the image stitching procedure by exploiting the initial knowledge about the images positions provided by the navigation sensors. The proposed approach also avoids solving for all the transformation parameters of all the photos together to save the expected long computation time if all the parameters were considered simultaneously. After extracting the points of interest of all the involved images using Scale-Invariant Feature Transform (SIFT) algorithm, the proposed approach uses the initial image's coordinates to build an incremental constrained Delaunay triangulation that represents the neighborhood of each image. This triangulation helps to match only the neighbor images and therefore reduces the time-consuming features matching step. The estimated relative orientation between the matched images is used to find a candidate seed image for the stitching process. The pre-estimated transformation parameters of the images are employed successively in a growing fashion to create the stitched image and the coverage image. The proposed approach is implemented and tested using the images acquired through a UAV flight mission and the achieved results are presented and discussed.

Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images

NASA Astrophysics Data System (ADS)

Leo, Patrick; Lee, George; Madabhushi, Anant

2016-03-01

Quantitative histomorphometry (QH) is the process of computerized extraction of features from digitized tissue slide images. Typically these features are used in machine learning classifiers to predict disease presence, behavior and outcome. Successful robust classifiers require features that both discriminate between classes of interest and are stable across data from multiple sites. Feature stability may be compromised by variation in slide staining and scanning procedures. These laboratory specific variables include dye batch, slice thickness and the whole slide scanner used to digitize the slide. The key therefore is to be able to identify features that are not only discriminating between the classes of interest (e.g. cancer and non-cancer or biochemical recurrence and non- recurrence) but also features that will not wildly fluctuate on slides representing the same tissue class but from across multiple different labs and sites. While there has been some recent efforts at understanding feature stability in the context of radiomics applications (i.e. feature analysis of radiographic images), relatively few attempts have been made at studying the trade-off between feature stability and discriminability for histomorphometric and digital pathology applications. In this paper we present two new measures, preparation-induced instability score (PI) and latent instability score (LI), to quantify feature instability across and within datasets. Dividing PI by LI yields a ratio for how often a feature for a specific tissue class (e.g. low grade prostate cancer) is different between datasets from different sites versus what would be expected from random chance alone. Using this ratio we seek to quantify feature vulnerability to variations in slide preparation and digitization. Since our goal is to identify stable QH features we evaluate these features for their stability and thus inclusion in machine learning based classifiers in a use case involving prostate cancer. Specifically we examine QH features which may predict 5 year biochemical recurrence for prostate cancer patients who have undergone radical prostatectomy from digital slide images of surgically excised tissue specimens, 5 year biochemical recurrence being a strong predictor of disease recurrence. In this study we evaluated the ability of our feature robustness indices to identify the most stable and predictive features of 5 year biochemical recurrence using digitized slide images of surgically excised prostate cancer specimens from 80 different patients across 4 different sites. A total of 242 features from 5 different feature families were investigated to identify the most stable QH features from our set. Our feature robustness indices (PI and LI) suggested that five feature families (graph, shape, co-occurring gland tensors, gland sub-graphs, texture) were susceptible to variations in slide preparation and digitization across various sites. The family least affected was shape features in which 19.3% of features varied across laboratories while the most vulnerable family, at 55.6%, was the gland disorder features. However the disorder features were the most stable within datasets being different between random halves of a dataset in an average of just 4.1% of comparisons while texture features were the most unstable being different at a rate of 4.7%. We also compared feature stability across two datasets before and after color normalization. Color normalization decreased feature stability with 8% and 34% of features different between the two datasets in two outcome groups prior to normalization and 49% and 51% different afterwards. Our results appear to suggest that evaluation of QH features across multiple sites needs to be undertaken to assess robustness and class discriminability alone should not represent the benchmark for selection of QH features to build diagnostic and prognostic digital pathology classifiers.

Ultrasound Images of the Tongue: A Tutorial for Assessment and Remediation of Speech Sound Errors.

PubMed

Preston, Jonathan L; McAllister Byun, Tara; Boyce, Suzanne E; Hamilton, Sarah; Tiede, Mark; Phillips, Emily; Rivera-Campos, Ahmed; Whalen, Douglas H

2017-01-03

Diagnostic ultrasound imaging has been a common tool in medical practice for several decades. It provides a safe and effective method for imaging structures internal to the body. There has been a recent increase in the use of ultrasound technology to visualize the shape and movements of the tongue during speech, both in typical speakers and in clinical populations. Ultrasound imaging of speech has greatly expanded our understanding of how sounds articulated with the tongue (lingual sounds) are produced. Such information can be particularly valuable for speech-language pathologists. Among other advantages, ultrasound images can be used during speech therapy to provide (1) illustrative models of typical (i.e. "correct") tongue configurations for speech sounds, and (2) a source of insight into the articulatory nature of deviant productions. The images can also be used as an additional source of feedback for clinical populations learning to distinguish their better productions from their incorrect productions, en route to establishing more effective articulatory habits. Ultrasound feedback is increasingly used by scientists and clinicians as both the expertise of the users increases and as the expense of the equipment declines. In this tutorial, procedures are presented for collecting ultrasound images of the tongue in a clinical context. We illustrate these procedures in an extended example featuring one common error sound, American English /r/. Images of correct and distorted /r/ are used to demonstrate (1) how to interpret ultrasound images, (2) how to assess tongue shape during production of speech sounds, (3), how to categorize tongue shape errors, and (4), how to provide visual feedback to elicit a more appropriate and functional tongue shape. We present a sample protocol for using real-time ultrasound images of the tongue for visual feedback to remediate speech sound errors. Additionally, example data are shown to illustrate outcomes with the procedure.

Image matching as a data source for forest inventory - Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction algorithms in a typical managed boreal forest environment

NASA Astrophysics Data System (ADS)

Kukkonen, M.; Maltamo, M.; Packalen, P.

2017-08-01

Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method. Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant.

Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.

PubMed

Zhou, Yujia; Yap, Pew-Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

2017-09-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.

Heterotopic Pancreas: Histopathologic Features, Imaging Findings, and Complications.

PubMed

Rezvani, Maryam; Menias, Christine; Sandrasegaran, Kumaresan; Olpin, Jeffrey D; Elsayes, Khaled M; Shaaban, Akram M

2017-01-01

Heterotopic pancreas is a congenital anomaly in which pancreatic tissue is anatomically separate from the main gland. The most common locations of this displacement include the upper gastrointestinal tract-specifically, the stomach, duodenum, and proximal jejunum. Less common sites are the esophagus, ileum, Meckel diverticulum, biliary tree, mesentery, and spleen. Uncomplicated heterotopic pancreas is typically asymptomatic, with the lesion being discovered incidentally during an unrelated surgery, during an imaging examination, or at autopsy. The most common computed tomographic appearance of heterotopic pancreas is that of a small oval intramural mass with microlobulated margins and an endoluminal growth pattern. The attenuation and enhancement characteristics of these lesions parallel their histologic composition. Acinus-dominant lesions demonstrate avid homogeneous enhancement after intravenous contrast material administration, whereas duct-dominant lesions are hypovascular and heterogeneous. At magnetic resonance imaging, the heterotopic pancreas is isointense to the orthotopic pancreas, with characteristic T1 hyperintensity and early avid enhancement after intravenous gadolinium-based contrast material administration. Heterotopic pancreatic tissue has a rudimentary ductal system in which an orifice is sometimes visible at imaging as a central umbilication of the lesion. Complications of heterotopic pancreas include pancreatitis, pseudocyst formation, malignant degeneration, gastrointestinal bleeding, bowel obstruction, and intussusception. Certain complications may be erroneously diagnosed as malignancy. Paraduodenal pancreatitis is thought to be due to cystic degeneration of heterotopic pancreatic tissue in the medial wall of the duodenum. Recognizing the characteristic imaging features of heterotopic pancreas aids in differentiating it from cancer and thus in avoiding unnecessary surgery. © RSNA, 2017.

Stages as models of scene geometry.

PubMed

Nedović, Vladimir; Smeulders, Arnold W M; Redert, André; Geusebroek, Jan-Mark

2010-09-01

Reconstruction of 3D scene geometry is an important element for scene understanding, autonomous vehicle and robot navigation, image retrieval, and 3D television. We propose accounting for the inherent structure of the visual world when trying to solve the scene reconstruction problem. Consequently, we identify geometric scene categorization as the first step toward robust and efficient depth estimation from single images. We introduce 15 typical 3D scene geometries called stages, each with a unique depth profile, which roughly correspond to a large majority of broadcast video frames. Stage information serves as a first approximation of global depth, narrowing down the search space in depth estimation and object localization. We propose different sets of low-level features for depth estimation, and perform stage classification on two diverse data sets of television broadcasts. Classification results demonstrate that stages can often be efficiently learned from low-dimensional image representations.

Gaze Estimation for Off-Angle Iris Recognition Based on the Biometric Eye Model

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

Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J

Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ANONYMIZED biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction frommore » elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.« less

[Magnetic resonance for the study of osteosarcoma].

PubMed

Spina, V; Romagnoli, R; Manfrini, M; Cerofolini, E; Capanna, R; Gaiani, L; Calandra Buonaura, P; Picci, P; Campanacci, M

1991-01-01

The authors report their experience with MR imaging in the study of osteosarcoma. Two main elements were evaluated: signal characteristics and loco-regional staging. Seventy-one patients were studied: 65 of them had central long-bone osteosarcoma, and 6 had telangiectatic long-bone osteosarcoma. T1- and T2-weighted spin-echo sequences were employed and all cases were scanned on 3 planes (sagittal, coronal, and axial). In 28 patients MR imaging was performed both before and after preoperative chemotherapy. The obtained data were compared to surgical and pathological findings. With the exception of the typical signal patterns of quite-osteoblastic osteosarcoma (which presents with low signal on both T1- and T2-weighted sequences), no particular signal features were observed which could help distinguish the different types of osteosarcoma. MR imaging is the method of choice in loco-regional staging for, in our series, it allowed a rational and adequate surgical planning. For this purpose, at least a longitudinal T1- and an axial T2-weighted images are required.

Lateral epicondylitis and beyond: imaging of lateral elbow pain with clinical-radiologic correlation.

PubMed

Kotnis, Nikhil A; Chiavaras, Mary M; Harish, Srinivasan

2012-04-01

The diagnosis of lateral epicondylitis is often straightforward and can be made on the basis of clinical findings. However, radiological assessment is valuable where the clinical picture is less clear or where symptoms are refractory to treatment. Demographics, aspects of clinical history, or certain physical signs may suggest an alternate diagnosis. Knowledge of the typical clinical presentation and imaging findings of lateral epicondylitis, in addition to other potential causes of lateral elbow pain, is necessary. These include entrapment of the posterior interosseous and lateral antebrachial cutaneous nerves, posterolateral rotatory instability, posterolateral plica syndrome, Panner's disease, osteochondritis dissecans of the capitellum, radiocapitellar overload syndrome, occult fractures and chondral-osseous impaction injuries, and radiocapitellar arthritis. Knowledge of these potential masquerades of lateral epicondylitis and their characteristic clinical and imaging features is essential for accurate diagnosis. The goal of this review is to provide an approach to the imaging of lateral elbow pain, discussing the relevant anatomy, various causes, and discriminating factors, which will allow for an accurate diagnosis.

Scanning ultrafast electron microscopy

PubMed Central

Yang, Ding-Shyue; Mohammed, Omar F.; Zewail, Ahmed H.

2010-01-01

Progress has been made in the development of four-dimensional ultrafast electron microscopy, which enables space-time imaging of structural dynamics in the condensed phase. In ultrafast electron microscopy, the electrons are accelerated, typically to 200 keV, and the microscope operates in the transmission mode. Here, we report the development of scanning ultrafast electron microscopy using a field-emission-source configuration. Scanning of pulses is made in the single-electron mode, for which the pulse contains at most one or a few electrons, thus achieving imaging without the space-charge effect between electrons, and still in ten(s) of seconds. For imaging, the secondary electrons from surface structures are detected, as demonstrated here for material surfaces and biological specimens. By recording backscattered electrons, diffraction patterns from single crystals were also obtained. Scanning pulsed-electron microscopy with the acquired spatiotemporal resolutions, and its efficient heat-dissipation feature, is now poised to provide in situ 4D imaging and with environmental capability. PMID:20696933

Reduced prefrontal connectivity in psychopathy.

PubMed

Motzkin, Julian C; Newman, Joseph P; Kiehl, Kent A; Koenigs, Michael

2011-11-30

Linking psychopathy to a specific brain abnormality could have significant clinical, legal, and scientific implications. Theories on the neurobiological basis of the disorder typically propose dysfunction in a circuit involving ventromedial prefrontal cortex (vmPFC). However, to date there is limited brain imaging data to directly test whether psychopathy may indeed be associated with any structural or functional abnormality within this brain area. In this study, we employ two complementary imaging techniques to assess the structural and functional connectivity of vmPFC in psychopathic and non-psychopathic criminals. Using diffusion tensor imaging, we show that psychopathy is associated with reduced structural integrity in the right uncinate fasciculus, the primary white matter connection between vmPFC and anterior temporal lobe. Using functional magnetic resonance imaging, we show that psychopathy is associated with reduced functional connectivity between vmPFC and amygdala as well as between vmPFC and medial parietal cortex. Together, these data converge to implicate diminished vmPFC connectivity as a characteristic neurobiological feature of psychopathy.

Reduced Prefrontal Connectivity in Psychopathy

PubMed Central

Motzkin, Julian C.; Newman, Joseph P.; Kiehl, Kent A.; Koenigs, Michael

2012-01-01

Linking psychopathy to a specific brain abnormality could have significant clinical, legal, and scientific implications. Theories on the neurobiological basis of the disorder typically propose dysfunction in a circuit involving ventromedial prefrontal cortex (vmPFC). However, to date there is limited brain imaging data to directly test whether psychopathy may indeed be associated with any structural or functional abnormality within this brain area. In this study, we employ two complementary imaging techniques to assess the structural and functional connectivity of vmPFC in psychopathic and non-psychopathic criminals. Using diffusion tensor imaging, we show that psychopathy is associated with reduced structural integrity in the right uncinate fasciculus, the primary white matter connection between vmPFC and anterior temporal lobe. Using functional magnetic resonance imaging, we show that psychopathy is associated with reduced functional connectivity between vmPFC and amygdala as well as between vmPFC and medial parietal cortex. Together, these data converge to implicate diminished vmPFC connectivity as a characteristic neurobiological feature of psychopathy. PMID:22131397

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

PubMed

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-03-23

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

PubMed Central

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-01-01

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. PMID:29570690

Pulmonary hydatid embolization. Report on 2 operated cases and review of published reports.

PubMed Central

Palant, A; Deutsch, V; Kishon, Y; Lieberman, Y; Yahini, J H; Neufeld, H N

1976-01-01

Two patients with pulmonary hydatid embolization are described and commented upon in the light of 43 similar published cases. The diagnosis was strongly suspected from the medical history and the chest x-ray films and supported by angiocardiography. The angiocardiographic features of this condition have not been described previously in detail. They include amputation and filling defects of pulmonary artery branches, which are typically located proximal to a rounded tumour-like opacity seen on the plain x-ray film. Both patients underwent successful embolectomy. Images PMID:973883

Ankle impingement syndromes.

PubMed

Umans, Hilary

2002-06-01

The term "ankle impingement" encompasses a broad range of conditions that are typically post-traumatic and often chronic. Various forms of mechanical impingement can result from synovial proliferation, bone spur formation, or ligamentous scarring and hypertrophy. Since symptoms and physical findings can mimic a variety of disorders, accurate diagnosis may remain elusive, and proper effective therapy may be delayed. The objective of this article is to define and elucidate the etiology of the various forms of ankle impingement, clarify the range of associated osseous and soft-tissue pathology, and describe the imaging features and therapeutic options.

Occupational absorption of tellurium: a report of two cases.

PubMed Central

Blackadder, E S; Manderson, W G

1975-01-01

Industrial uses of tellurium are limited, and reported cases of tellurium absorption of occupational origin are rare. Two such cases are reported here. Both showed typical signs and symptoms of intoxication; in particular, the stench of sour garlic was noted on breath and from excreta. An unusual feature was the bluish-black discoloration of the webs of the fingers and streaks on the face and neck. Full hospital investigation was negative. No permanent damage resulted and each patient made a spontaneous recovery without treatment. Images PMID:123755

Dunes and Clouds in False Color

NASA Technical Reports Server (NTRS)

2005-01-01

[figure removed for brevity, see original site]

The theme for the weeks of 1/17 and 1/24 is the north polar region of Mars as seen in false color THEMIS images. Ice/frost will typically appear as bright blue in color; dust mantled ice will appear in tones of red/orange.

The small greenish features in this image are sand dunes. The white feature on the right side is likely an ice cloud.

Image information: VIS instrument. Latitude 84.6, Longitude 203.1 East (156.9 West). 19 meter/pixel resolution.

Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.

NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.

Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen

PubMed Central

Jia, Shengyao; Li, Hongyang; Wang, Yanjie; Tong, Renyuan; Li, Qing

2017-01-01

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types. PMID:28974005

SU-D-207-01: Markerless Respiratory Motion Tracking with Contrast Enhanced Thoracic Cone Beam CT Projections

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

Chao, M; Yuan, Y; Rosenzweig, K

2015-06-15

Purpose: To develop a novel technique to enhance the image contrast of clinical cone beam CT projections and extract respiratory signals based on anatomical motion using the modified Amsterdam Shroud (AS) method to benefit image guided radiation therapy. Methods: Thoracic cone beam CT projections acquired prior to treatment were preprocessed to increase their contrast for better respiratory signal extraction. Air intensity on raw images was firstly estimated and then applied to correct the projections to generate new attenuation images that were subsequently improved with deeper anatomy feature enhancement through taking logarithm operation, derivative along superior-inferior direction, respectively. All pixels onmore » individual post-processed two dimensional images were horizontally summed to one column and all projections were combined side by side to create an AS image from which patient’s respiratory signal was extracted. The impact of gantry rotation on the breathing signal rendering was also investigated. Ten projection image sets from five lung cancer patients acquired with the Varian Onboard Imager on 21iX Clinac (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Results: Application of the air correction on raw projections showed that more than an order of magnitude of contrast enhancement was achievable. The typical contrast on the raw projections is around 0.02 while that on attenuation images could greater than 0.5. Clear and stable breathing signal can be reliably extracted from the new images while the uncorrected projection sets failed to yield clear signals most of the time. Conclusion: Anatomy feature plays a key role in yielding breathing signal from the projection images using the AS technique. The air correction process facilitated the contrast enhancement significantly and attenuation images thus obtained provides a practical solution to obtaining markerless breathing motion tracking.« less

Familiarity effects in the construction of facial-composite images using modern software systems.

PubMed

Frowd, Charlie D; Skelton, Faye C; Butt, Neelam; Hassan, Amal; Fields, Stephen; Hancock, Peter J B

2011-12-01

We investigate the effect of target familiarity on the construction of facial composites, as used by law enforcement to locate criminal suspects. Two popular software construction methods were investigated. Participants were shown a target face that was either familiar or unfamiliar to them and constructed a composite of it from memory using a typical 'feature' system, involving selection of individual facial features, or one of the newer 'holistic' types, involving repeated selection and breeding from arrays of whole faces. This study found that composites constructed of a familiar face were named more successfully than composites of an unfamiliar face; also, naming of composites of internal and external features was equivalent for construction of unfamiliar targets, but internal features were better named than the external features for familiar targets. These findings applied to both systems, although benefit emerged for the holistic type due to more accurate construction of internal features and evidence for a whole-face advantage. STATEMENT OF RELEVANCE: This work is of relevance to practitioners who construct facial composites with witnesses to and victims of crime, as well as for software designers to help them improve the effectiveness of their composite systems.

FuzzObserver

NASA Technical Reports Server (NTRS)

Howard, Ayanna; Bayard, David

2006-01-01

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Shape classification of wear particles by image boundary analysis using machine learning algorithms

NASA Astrophysics Data System (ADS)

Yuan, Wei; Chin, K. S.; Hua, Meng; Dong, Guangneng; Wang, Chunhui

2016-05-01

The shape features of wear particles generated from wear track usually contain plenty of information about the wear states of a machinery operational condition. Techniques to quickly identify types of wear particles quickly to respond to the machine operation and prolong the machine's life appear to be lacking and are yet to be established. To bridge rapid off-line feature recognition with on-line wear mode identification, this paper presents a new radial concave deviation (RCD) method that mainly involves the use of the particle boundary signal to analyze wear particle features. Signal output from the RCDs subsequently facilitates the determination of several other feature parameters, typically relevant to the shape and size of the wear particle. Debris feature and type are identified through the use of various classification methods, such as linear discriminant analysis, quadratic discriminant analysis, naïve Bayesian method, and classification and regression tree method (CART). The average errors of the training and test via ten-fold cross validation suggest CART is a highly suitable approach for classifying and analyzing particle features. Furthermore, the results of the wear debris analysis enable the maintenance team to diagnose faults appropriately.

A Study on Spectral Signature Analysis of Wetland Vegetation Based on Ground Imaging Spectrum Data

NASA Astrophysics Data System (ADS)

Ling, Chengxing; Liu, Hua; Ju, Hongbo; Zhang, Huaiqing; You, Jia; Li, Weina

2017-10-01

The objective of this study was to verify the application of imaging spectrometer in wetland vegetation remote sensing monitoring, based on analysis of wetland vegetation spectral features. Spectral information of Carex vegetation spectral data under different water environment was collected bySOC710VP and ASD FieldSpec 3; Meanwhile, the chlorophyll contents of wheat leaves were tested in the lab. A total 9 typical vegetation indices were calculated by using two instruments’ data which were spectral values from 400nm to 1000 nm. Then features between the same vegetation indices and soil water contents for two applications were analyzed and compared. The results showed that there were same spectrum curve trends of Carex vegetation (soil moisture content of 51%, 32%, 14% and three regional comparative analysis)reflectance between SOC710VP and ASD FieldSpec 3, including the two reflectance peak of 550nm and 730 nm, two reflectance valley of 690 nm and 970nm, and continuous near infrared reflectance platform. However, The two also have a very clear distinction: (1) The reflection spectra of SOC710VP leaves of Carex Carex leaf spectra in the three soil moisture environment values are greater than ASD FieldSpec 3 collected value; (2) The SOC710VP reflectivity curve does not have the smooth curve of the original spectrum measured by the ASD FieldSpec 3, the amplitude of fluctuation is bigger, and it is more obvious in the near infrared band. It is concluded that SOC710VP spectral data are reliable, with the image features, spectral curve features reliable. It has great potential in the research of hyperspectral remote sensing technology in the development of wetland near earth, remote sensing monitoring of wetland resources.

Benign and Malignant Proliferative Fibro-osseous and Osseous Lesions of the Oral Cavity of Dogs.

PubMed

Soltero-Rivera, M; Engiles, J B; Reiter, A M; Reetz, J; Lewis, J R; Sánchez, M D

2015-09-01

Ossifying fibroma (OF) and fibrous dysplasia (FD) are benign, intraosseous, proliferative fibro-osseous lesions (PFOLs) characterized by replacement of normal bone by a fibrous matrix with various degrees of mineralization and ossification. Osteomas are benign tumors composed of mature, well-differentiated bone. Clinical, imaging, and histologic features of 15 initially diagnosed benign PFOLs and osteomas of the canine oral cavity were evaluated. Final diagnoses after reevaluation were as follows: OF (3 cases), FD (4 cases), low-grade osteosarcoma (LG-OSA) (3 cases), and osteoma (5 cases). Histology alone often did not result in a definitive diagnosis for PFOL. OF appeared as a well-circumscribed, radiopaque mass with some degree of bone lysis on imaging. Most lesions of FD showed soft tissue opacity with bone lysis and ill-defined margins. Low-grade OSA appeared as a lytic lesion with a mixed opacity and ill-defined margins. Osteomas were characterized by a mineralized, expansile, well-circumscribed lesion. Although histologic features of PFOLs were typically bland, the lesions diagnosed as LG-OSA had some features of malignancy (eg, bone invasion or a higher mitotic index). Treatment varied widely. Of the 10 dogs with benign PFOL or osteoma with known outcome (10/12), 9 showed either complete response (6/10) or stable disease (3/10) after treatment. Of the 2 dogs with LG-OSA with known outcome, 1 showed complete response after curative intent surgery, but 1 patient had recurrence after partial maxillectomy. Definitive diagnosis of mandibular/maxillary PFOL is challenging via histopathologic examination alone, and accurate diagnosis is best achieved through assimilation of clinical, imaging, and histopathologic features. © The Author(s) 2015.

Waardenburg syndrome: iris and choroidal hypopigmentation: findings on anterior and posterior segment imaging.

PubMed

Shields, Carol L; Nickerson, Stephanie J; Al-Dahmash, Saad; Shields, Jerry A

2013-09-01

Waardenburg syndrome typically manifests with congenital iris pigmentary abnormalities, but careful inspection can reveal additional posterior uveal pigmentary abnormalities. To demonstrate iris and choroidal hypopigmentation in patients with Waardenburg syndrome. Retrospective review of 7 patients referred for evaluation of presumed ocular melanocytosis. To describe the clinical and imaging features of the anterior and posterior uvea. In all patients, the diagnosis of Waardenburg syndrome was established. The nonocular features included white forelock in 4 of 7 (57%), tubular nose in 5 of 6 (83%), and small nasal alae in 5 of 6 (83%) patients. In 2 patients, a hearing deficit was documented on audiology testing. Family history of Waardenburg syndrome was elicited in 5 of 7 (71%) patients. Ocular features (7 patients) included telecanthus in 5 (71%), synophrys in 2 (29%), iris hypopigmentation in 5 (71%), and choroidal hypopigmentation in 5 (71%) patients. No patient had muscle contractures or Hirschsprung disease. Visual acuity was 20/20 to 20/50 in all patients. Iris hypopigmentation in 8 eyes was sector in 6 (75%) and diffuse (complete) in 2 (25%). Choroidal hypopigmentation in 9 eyes (100%) showed a sector pattern in 6 (67%) and a diffuse pattern in 3 (33%). Anterior segment optical coherence tomography revealed the hypopigmented iris to be thinner and with shallower crypts than the normal iris. Posterior segment optical coherence tomography showed a normal retina in all patients, but the subfoveal choroid in the hypopigmented region was slightly thinner (mean, 197 μm) compared with the opposite normal choroid (243 μm). Fundus autofluorescence demonstrated mild hyperautofluorescence (scleral unmasking) in hypopigmented choroid and no lipofuscin abnormality. Waardenburg syndrome manifests hypopigmentation of the iris and choroid with imaging features showing a slight reduction in the thickness of the affected tissue.

Lexical Processing in Toddlers with ASD: Does Weak Central Coherence Play a Role?

PubMed Central

Weismer, Susan Ellis; Haebig, Eileen; Edwards, Jan; Saffran, Jenny; Venker, Courtney E.

2016-01-01

This study investigated whether vocabulary delays in toddlers with autism spectrum disorders (ASD) can be explained by a cognitive style that prioritizes processing of detailed, local features of input over global contextual integration – as claimed by the weak central coherence (WCC) theory. Thirty toddlers with ASD and 30 younger, cognition-matched typical controls participated in a looking-while-listening task that assessed whether perceptual or semantic similarities among named images disrupted word recognition relative to a neutral condition. Overlap of perceptual features invited local processing whereas semantic overlap invited global processing. With the possible exception of a subset of toddlers who had very low vocabulary skills, these results provide no evidence that WCC is characteristic of lexical processing in toddlers with ASD. PMID:27696177

No-reference quality assessment based on visual perception

NASA Astrophysics Data System (ADS)

Li, Junshan; Yang, Yawei; Hu, Shuangyan; Zhang, Jiao

2014-11-01

The visual quality assessment of images/videos is an ongoing hot research topic, which has become more and more important for numerous image and video processing applications with the rapid development of digital imaging and communication technologies. The goal of image quality assessment (IQA) algorithms is to automatically assess the quality of images/videos in agreement with human quality judgments. Up to now, two kinds of models have been used for IQA, namely full-reference (FR) and no-reference (NR) models. For FR models, IQA algorithms interpret image quality as fidelity or similarity with a perfect image in some perceptual space. However, the reference image is not available in many practical applications, and a NR IQA approach is desired. Considering natural vision as optimized by the millions of years of evolutionary pressure, many methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychological features of the human visual system (HVS). To reach this goal, researchers try to simulate HVS with image sparsity coding and supervised machine learning, which are two main features of HVS. A typical HVS captures the scenes by sparsity coding, and uses experienced knowledge to apperceive objects. In this paper, we propose a novel IQA approach based on visual perception. Firstly, a standard model of HVS is studied and analyzed, and the sparse representation of image is accomplished with the model; and then, the mapping correlation between sparse codes and subjective quality scores is trained with the regression technique of least squaresupport vector machine (LS-SVM), which gains the regressor that can predict the image quality; the visual metric of image is predicted with the trained regressor at last. We validate the performance of proposed approach on Laboratory for Image and Video Engineering (LIVE) database, the specific contents of the type of distortions present in the database are: 227 images of JPEG2000, 233 images of JPEG, 174 images of White Noise, 174 images of Gaussian Blur, 174 images of Fast Fading. The database includes subjective differential mean opinion score (DMOS) for each image. The experimental results show that the proposed approach not only can assess many kinds of distorted images quality, but also exhibits a superior accuracy and monotonicity.

The Evolution of Gully Features in Acidalia Planitia

NASA Image and Video Library

2017-10-23

This observation image from NASA's Mars Reconnaisance Orbiter (MRO) captures details regarding the evolution of gully features observed in a crater in Acidalia Planitia. A Context Camera image provides context for these gullies showing an approximately 7-kilometer diameter crater in which we see that the gullies occur exclusively on the northern wall. This is unlike most of the observed gully sites in the northern Martian hemisphere, which typically have gullies on their pole-facing slopes. Another unique observation of this set of gullies is that they start mid-way down the crater's wall rather than cutting directly into the upper crater wall or rim. The younger, more recently active fans are generally rougher than the older, smoother fans that are located near the base of the slope. Consistent with this interpretation, are a number of observed superposition and cross-cutting relationships. The rougher fans are always superimposed over the older, smoother ones. Discontinuous fractures are observed to cross-cut only older features, while the most recently active portions of the gullies, in this case the channels or fans, are not cut by the fractures, but in some cases even superimpose them. This suggests that the fractures formed prior to the last phase of gully activity. https://photojournal.jpl.nasa.gov/catalog/PIA22054

Rett syndrome: basic features of visual processing-a pilot study of eye-tracking.

PubMed

Djukic, Aleksandra; Valicenti McDermott, Maria; Mavrommatis, Kathleen; Martins, Cristina L

2012-07-01

Consistently observed "strong eye gaze" has not been validated as a means of communication in girls with Rett syndrome, ubiquitously affected by apraxia, unable to reply either verbally or manually to questions during formal psychologic assessment. We examined nonverbal cognitive abilities and basic features of visual processing (visual discrimination attention/memory) by analyzing patterns of visual fixation in 44 girls with Rett syndrome, compared with typical control subjects. To determine features of visual fixation patterns, multiple pictures (with the location of the salient and presence/absence of novel stimuli as variables) were presented on the screen of a TS120 eye-tracker. Of the 44, 35 (80%) calibrated and exhibited meaningful patterns of visual fixation. They looked longer at salient stimuli (cartoon, 2.8 ± 2 seconds S.D., vs shape, 0.9 ± 1.2 seconds S.D.; P = 0.02), regardless of their position on the screen. They recognized novel stimuli, decreasing the fixation time on the central image when another image appeared on the periphery of the slide (2.7 ± 1 seconds S.D. vs 1.8 ± 1 seconds S.D., P = 0.002). Eye-tracking provides a feasible method for cognitive assessment and new insights into the "hidden" abilities of individuals with Rett syndrome. Copyright © 2012 Elsevier Inc. All rights reserved.

Surveying the Newly Digitized Apollo Metric Images for Highland Fault Scarps on the Moon

NASA Astrophysics Data System (ADS)

Williams, N. R.; Pritchard, M. E.; Bell, J. F.; Watters, T. R.; Robinson, M. S.; Lawrence, S.

2009-12-01

The presence and distribution of thrust faults on the Moon have major implications for lunar formation and thermal evolution. For example, thermal history models for the Moon imply that most of the lunar interior was initially hot. As the Moon cooled over time, some models predict global-scale thrust faults should form as stress builds from global thermal contraction. Large-scale thrust fault scarps with lengths of hundreds of kilometers and maximum relief of up to a kilometer or more, like those on Mercury, are not found on the Moon; however, relatively small-scale linear and curvilinear lobate scarps with maximum lengths typically around 10 km have been observed in the highlands [Binder and Gunga, Icarus, v63, 1985]. These small-scale scarps are interpreted to be thrust faults formed by contractional stresses with relatively small maximum (tens of meters) displacements on the faults. These narrow, low relief landforms could only be identified in the highest resolution Lunar Orbiter and Apollo Panoramic Camera images and under the most favorable lighting conditions. To date, the global distribution and other properties of lunar lobate faults are not well understood. The recent micron-resolution scanning and digitization of the Apollo Mapping Camera (Metric) photographic negatives [Lawrence et al., NLSI Conf. #1415, 2008; http://wms.lroc.asu.edu/apollo] provides a new dataset to search for potential scarps. We examined more than 100 digitized Metric Camera image scans, and from these identified 81 images with favorable lighting (incidence angles between about 55 and 80 deg.) to manually search for features that could be potential tectonic scarps. Previous surveys based on Panoramic Camera and Lunar Orbiter images found fewer than 100 lobate scarps in the highlands; in our Apollo Metric Camera image survey, we have found additional regions with one or more previously unidentified linear and curvilinear features on the lunar surface that may represent lobate thrust fault scarps. In this presentation we review the geologic characteristics and context of these newly-identified, potentially tectonic landforms. The lengths and relief of some of these linear and curvilinear features are consistent with previously identified lobate scarps. Most of these features are in the highlands, though a few occur along the edges of mare and/or crater ejecta deposits. In many cases the resolution of the Metric Camera frames (~10 m/pix) is not adequate to unequivocally determine the origin of these features. Thus, to assess if the newly identified features have tectonic or other origins, we are examining them in higher-resolution Panoramic Camera (currently being scanned) and Lunar Reconnaissance Orbiter Camera Narrow Angle Camera images [Watters et al., this meeting, 2009].

Automating digital leaf measurement: the tooth, the whole tooth, and nothing but the tooth.

PubMed

Corney, David P A; Tang, H Lilian; Clark, Jonathan Y; Hu, Yin; Jin, Jing

2012-01-01

Many species of plants produce leaves with distinct teeth around their margins. The presence and nature of these teeth can often help botanists to identify species. Moreover, it has long been known that more species native to colder regions have teeth than species native to warmer regions. It has therefore been suggested that fossilized remains of leaves can be used as a proxy for ancient climate reconstruction. Similar studies on living plants can help our understanding of the relationships. The required analysis of leaves typically involves considerable manual effort, which in practice limits the number of leaves that are analyzed, potentially reducing the power of the results. In this work, we describe a novel algorithm to automate the marginal tooth analysis of leaves found in digital images. We demonstrate our methods on a large set of images of whole herbarium specimens collected from Tilia trees (also known as lime, linden or basswood). We chose the genus Tilia as its constituent species have toothed leaves of varied size and shape. In a previous study we extracted c.1600 leaves automatically from a set of c.1100 images. Our new algorithm locates teeth on the margins of such leaves and extracts features such as each tooth's area, perimeter and internal angles, as well as counting them. We evaluate an implementation of our algorithm's performance against a manually analyzed subset of the images. We found that the algorithm achieves an accuracy of 85% for counting teeth and 75% for estimating tooth area. We also demonstrate that the automatically extracted features are sufficient to identify different species of Tilia using a simple linear discriminant analysis, and that the features relating to teeth are the most useful.

Highest Resolution Topography of 433 Eros and Implications for MUSES-C

NASA Technical Reports Server (NTRS)

Cheng, A. F.; Barnouin-Jha, O.

2003-01-01

The highest resolution observations of surface morphology and topography at asteroid 433 Eros were obtained by the Near Earth Asteroid Rendezvous (NEAR) Shoemaker spacecraft on 12 February 2001, as it landed within a ponded deposit on Eros. Coordinated observations were obtained by the imager and the laser rangefinder, at best image resolution of 1 cm/pixel and best topographic resolution of 0.4 m. The NEAR landing datasets provide unique information on rock size and height distributions and regolith processes. Rocks and soil can be distinguished photometrically, suggesting that bare rock is indeed exposed. The NEAR landing data are the only data at sufficient resolution to be relevant to hazard assessment on future landed missions to asteroids, such as the MUSES-C mission which will land on asteroid 25143 (1998 SF36) in order to obtain samples. In a typical region just outside the pond where NEAR landed, the areal coverage by resolved positive topographic features is 18%. At least one topographic feature in the vicinity of the NEAR landing site would have been hazardous for a spacecraft.

Novel vehicle detection system based on stacked DoG kernel and AdaBoost

PubMed Central

Kang, Hyun Ho; Lee, Seo Won; You, Sung Hyun

2018-01-01

This paper proposes a novel vehicle detection system that can overcome some limitations of typical vehicle detection systems using AdaBoost-based methods. The performance of the AdaBoost-based vehicle detection system is dependent on its training data. Thus, its performance decreases when the shape of a target differs from its training data, or the pattern of a preceding vehicle is not visible in the image due to the light conditions. A stacked Difference of Gaussian (DoG)–based feature extraction algorithm is proposed to address this issue by recognizing common characteristics, such as the shadow and rear wheels beneath vehicles—of vehicles under various conditions. The common characteristics of vehicles are extracted by applying the stacked DoG shaped kernel obtained from the 3D plot of an image through a convolution method and investigating only certain regions that have a similar patterns. A new vehicle detection system is constructed by combining the novel stacked DoG feature extraction algorithm with the AdaBoost method. Experiments are provided to demonstrate the effectiveness of the proposed vehicle detection system under different conditions. PMID:29513727

Toward surface quantification of liver fibrosis progression

NASA Astrophysics Data System (ADS)

He, Yuting; Kang, Chiang Huen; Xu, Shuoyu; Tuo, Xiaoye; Trasti, Scott; Tai, Dean C. S.; Raja, Anju Mythreyi; Peng, Qiwen; So, Peter T. C.; Rajapakse, Jagath C.; Welsch, Roy; Yu, Hanry

2010-09-01

Monitoring liver fibrosis progression by liver biopsy is important for certain treatment decisions, but repeated biopsy is invasive. We envision redefinition or elimination of liver biopsy with surface scanning of the liver with minimally invasive optical methods. This would be possible only if the information contained on or near liver surfaces accurately reflects the liver fibrosis progression in the liver interior. In our study, we acquired the second-harmonic generation and two-photon excitation fluorescence microscopy images of liver tissues from bile duct-ligated rat model of liver fibrosis. We extracted morphology-based features, such as total collagen, collagen in bile duct areas, bile duct proliferation, and areas occupied by remnant hepatocytes, and defined the capsule and subcapsular regions on the liver surface based on image analysis of features. We discovered a strong correlation between the liver fibrosis progression on the anterior surface and interior in both liver lobes, where biopsy is typically obtained. The posterior surface exhibits less correlation with the rest of the liver. Therefore, scanning the anterior liver surface would obtain similar information to that obtained from biopsy for monitoring liver fibrosis progression.

Photographer : JPL Range : 225,000 kilometers (140,625 miles) This image of the Jovian moon Europa

NASA Technical Reports Server (NTRS)

1979-01-01

Photographer : JPL Range : 225,000 kilometers (140,625 miles) This image of the Jovian moon Europa was taken by Voyager 2 along the evening terminator, which best shows the surface topography of complex narrow ridges, seen as curved bright streaks, 5 to 10 kilometers wide, and typically 100 kilometers in length. The area shown is about 600 by 800 kilometers, and the smallest features visible are about 4 kilometers in size. Also visable are dark bands, more diffused in character, 20 to 40 kilometers wide and hundreds to thousands of kilometers in length. A few features are suggestive of impact craters but are rare, indication that the surface thought to be dominantly ice is still active, perhaps warmed by tidal heating like Io. The larger icy satellites, Callisto and Ganymede, are evidently colder with much more rigid crusts and ancient impact craters. The complex intersection of dark markings and bright ridges suggest that the surface has been fractured and material from beneath has welled up to fill the cracks.

Multifunctional mesoporous silica nanoparticles for combined therapeutic, diagnostic and targeted action in cancer treatment.

PubMed

Rosenholm, Jessica M; Sahlgren, Cecilia; Lindén, Mika

2011-07-01

The main objective in the development of nanomedicine is to obtain delivery platforms for targeted delivery of drugs or imaging agents for improved therapeutic efficacy, reduced side effects and increased diagnostic sensitivity. A (nano)material class that has been recognized for its controllable properties on many levels is ordered mesoporous inorganic materials, typically in the form of amorphous silica (SiO2). Characteristics for this class of materials include mesoscopic order, tunable pore dimensions in the (macro)molecular size range, a high pore volume and surface area, the possibility for selective surface functionality as well as morphology control. The robust but biodegradable ceramic matrix moreover provides shelter for incorporated agents (drugs, proteins, imaging agents, photosensitizers) leaving the outer particle surface free for further modification. The unique features make these materials particularly amenable to modular design, whereby functional moieties and features may be interchanged or combined to produce multifunctional nanodelivery systems combining targeting, diagnostic, and therapeutic actions. This review covers the latest developments related to the use of mesoporous silica nanoparticles (MSNs) as nanocarriers in biomedical applications, with special focus on cancer therapy and diagnostics.

Dual-polarized light-field imaging micro-system via a liquid-crystal microlens array for direct three-dimensional observation.

PubMed

Xin, Zhaowei; Wei, Dong; Xie, Xingwang; Chen, Mingce; Zhang, Xinyu; Liao, Jing; Wang, Haiwei; Xie, Changsheng

2018-02-19

Light-field imaging is a crucial and straightforward way of measuring and analyzing surrounding light worlds. In this paper, a dual-polarized light-field imaging micro-system based on a twisted nematic liquid-crystal microlens array (TN-LCMLA) for direct three-dimensional (3D) observation is fabricated and demonstrated. The prototyped camera has been constructed by integrating a TN-LCMLA with a common CMOS sensor array. By switching the working state of the TN-LCMLA, two orthogonally polarized light-field images can be remapped through the functioned imaging sensors. The imaging micro-system in conjunction with the electric-optical microstructure can be used to perform polarization and light-field imaging, simultaneously. Compared with conventional plenoptic cameras using liquid-crystal microlens array, the polarization-independent light-field images with a high image quality can be obtained in the arbitrary polarization state selected. We experimentally demonstrate characters including a relatively wide operation range in the manipulation of incident beams and the multiple imaging modes, such as conventional two-dimensional imaging, light-field imaging, and polarization imaging. Considering the obvious features of the TN-LCMLA, such as very low power consumption, providing multiple imaging modes mentioned, simple and low-cost manufacturing, the imaging micro-system integrated with this kind of liquid-crystal microstructure driven electrically presents the potential capability of directly observing a 3D object in typical scattering media.

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

System theory in medical diagnostic devices: an overview.

PubMed

Baura, Gail D

2006-01-01

Medical diagnostics refers to testing conducted either in vitro or in vivo to provide critical health care information for risk assessment, early diagnosis, treatment, or disease management. Typical in vivo diagnostic tests include the computed tomography scan, magnetic resonance imaging, and blood pressure screening. Typical in vitro diagnostic tests include cholesterol, Papanicolaou smear, and conventional glucose monitoring tests. Historically, devices associated with both types of diagnostics have used heuristic curve fitting during signal analysis. However, since the early 1990s, a few enterprising engineers and physicians have used system theory to improve their core processing for feature detection and system identification. Current applications include automated Pap smear screening for detection of cervical cancer and diagnosis of Alzheimer's disease. Future applications, such as disease prediction before symptom onset and drug treatment customization, have been catalyzed by the Human Genome Project.

Validation of Noninvasive In Vivo Compound Ultrasound Strain Imaging Using Histologic Plaque Vulnerability Features.

PubMed

Hansen, Hendrik H G; de Borst, Gert Jan; Bots, Michiel L; Moll, Frans L; Pasterkamp, Gerard; de Korte, Chris L

2016-11-01

Carotid plaque rupture is a major cause of stroke. Key issue for risk stratification is early identification of rupture-prone plaques. A noninvasive technique, compound ultrasound strain imaging, was developed providing high-resolution radial deformation/strain images of atherosclerotic plaques. This study aims at in vivo validation of compound ultrasound strain imaging in patients by relating the measured strains to typical features of vulnerable plaques derived from histology after carotid endarterectomy. Strains were measured in 34 severely stenotic (>70%) carotid arteries at the culprit lesion site within 48 hours before carotid endarterectomy. In all cases, the lumen-wall boundary was identifiable on B-mode ultrasound, and the imaged cross-section did not move out of the imaging plane from systole to diastole. After endarterectomy, the plaques were processed using a validated histology analysis technique. Locally elevated strain values were observed in regions containing predominantly components related to plaque vulnerability, whereas lower values were observed in fibrous, collagen-rich plaques. The median strain of the inner plaque layer (1 mm thickness) was significantly higher (P<0.01) for (fibro)atheromatous (n=20, strain=0.27%) than that for fibrous plaques (n=14, strain=-0.75%). Also, a significantly larger area percentage of the inner layer revealed strains above 0.5% for (fibro)atheromatous (45.30%) compared with fibrous plaques (31.59%). (Fibro)atheromatous plaques were detected with a sensitivity, specificity, positive predictive value, and negative predictive value of 75%, 86%, 88%, and 71%, respectively. Strain did not significantly correlate with fibrous cap thickness, smooth muscle cell, or macrophage concentration. Compound ultrasound strain imaging allows differentiating (fibro)atheromatous from fibrous carotid artery plaques. © 2016 American Heart Association, Inc.

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

EMAN2: an extensible image processing suite for electron microscopy.

PubMed

Tang, Guang; Peng, Liwei; Baldwin, Philip R; Mann, Deepinder S; Jiang, Wen; Rees, Ian; Ludtke, Steven J

2007-01-01

EMAN is a scientific image processing package with a particular focus on single particle reconstruction from transmission electron microscopy (TEM) images. It was first released in 1999, and new versions have been released typically 2-3 times each year since that time. EMAN2 has been under development for the last two years, with a completely refactored image processing library, and a wide range of features to make it much more flexible and extensible than EMAN1. The user-level programs are better documented, more straightforward to use, and written in the Python scripting language, so advanced users can modify the programs' behavior without any recompilation. A completely rewritten 3D transformation class simplifies translation between Euler angle standards and symmetry conventions. The core C++ library has over 500 functions for image processing and associated tasks, and it is modular with introspection capabilities, so programmers can add new algorithms with minimal effort and programs can incorporate new capabilities automatically. Finally, a flexible new parallelism system has been designed to address the shortcomings in the rigid system in EMAN1.

Imaging of Groin Pain: Magnetic Resonance and Ultrasound Imaging Features

PubMed Central

Lee, Susan C.; Endo, Yoshimi; Potter, Hollis G.

2017-01-01

Context: Evaluation of groin pain in athletes may be challenging as pain is typically poorly localized and the pubic symphyseal region comprises closely approximated tendons and muscles. As such, magnetic resonance imaging (MRI) and ultrasound (US) may help determine the etiology of groin pain. Evidence Acquisition: A PubMed search was performed using the following search terms: ultrasound, magnetic resonance imaging, sports hernia, athletic pubalgia, and groin pain. Date restrictions were not placed on the literature search. Study Design: Clinical review. Level of Evidence: Level 4. Results: MRI is sensitive in diagnosing pathology in groin pain. Not only can MRI be used to image rectus abdominis/adductor longus aponeurosis and pubic bone pathology, but it can also evaluate other pathology within the hip and pelvis. MRI is especially helpful when groin pain is poorly localized. Real-time capability makes ultrasound useful in evaluating the pubic symphyseal region, as it can be used for evaluation and treatment. Conclusion: MRI and US are valuable in diagnosing pathology in athletes with groin pain, with the added utility of treatment using US-guided intervention. Strength-of Recommendation Taxonomy: C PMID:28850315

Handwritten-word spotting using biologically inspired features.

PubMed

van der Zant, Tijn; Schomaker, Lambert; Haak, Koen

2008-11-01

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.

High performance computing environment for multidimensional image analysis

PubMed Central

Rao, A Ravishankar; Cecchi, Guillermo A; Magnasco, Marcelo

2007-01-01

Background The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications. Results We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478× speedup. Conclusion Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets. PMID:17634099

High performance computing environment for multidimensional image analysis.

PubMed

Rao, A Ravishankar; Cecchi, Guillermo A; Magnasco, Marcelo

2007-07-10

The processing of images acquired through microscopy is a challenging task due to the large size of datasets (several gigabytes) and the fast turnaround time required. If the throughput of the image processing stage is significantly increased, it can have a major impact in microscopy applications. We present a high performance computing (HPC) solution to this problem. This involves decomposing the spatial 3D image into segments that are assigned to unique processors, and matched to the 3D torus architecture of the IBM Blue Gene/L machine. Communication between segments is restricted to the nearest neighbors. When running on a 2 Ghz Intel CPU, the task of 3D median filtering on a typical 256 megabyte dataset takes two and a half hours, whereas by using 1024 nodes of Blue Gene, this task can be performed in 18.8 seconds, a 478x speedup. Our parallel solution dramatically improves the performance of image processing, feature extraction and 3D reconstruction tasks. This increased throughput permits biologists to conduct unprecedented large scale experiments with massive datasets.

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

NASA Astrophysics Data System (ADS)

Unger, Jakob; Sun, Tianchen; Chen, Yi-Ling; Phipps, Jennifer E.; Bold, Richard J.; Darrow, Morgan A.; Ma, Kwan-Liu; Marcu, Laura

2018-01-01

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

The Repaired Rotator Cuff: MRI and Ultrasound Evaluation.

PubMed

Lee, Susan C; Williams, Danielle; Endo, Yoshimi

2018-03-01

The purposes of this review were to provide an overview of the current practice of evaluating the postoperative rotator cuff on imaging and to review the salient imaging findings of the normal and abnormal postoperative rotator cuff, as well as of postoperative complications. The repaired rotator cuff frequently appears abnormal on magnetic resonance imaging (MRI) and ultrasound (US). Recent studies have shown that while the tendons typically normalize, they can demonstrate clinically insignificant abnormal imaging appearances for longer than 6 months. Features of capsular thickening or subacromial-subdeltoid bursal thickening and fluid distension were found to decrease substantially in the first 6-month postoperative period. MRI and US were found to be highly comparable in the postoperative assessment of the rotator cuff, although they had a lower sensitivity for partial thickness tears. Imaging evaluation of newer techniques such as patch augmentation and superior capsular reconstruction needs to be further investigated. MRI and US are useful in the postoperative assessment of the rotator cuff, not only for evaluation of the integrity of the rotator cuff, but also for detecting hardware complications and other etiologies of shoulder pain.

Imaging of Groin Pain: Magnetic Resonance and Ultrasound Imaging Features.

PubMed

Lee, Susan C; Endo, Yoshimi; Potter, Hollis G

Evaluation of groin pain in athletes may be challenging as pain is typically poorly localized and the pubic symphyseal region comprises closely approximated tendons and muscles. As such, magnetic resonance imaging (MRI) and ultrasound (US) may help determine the etiology of groin pain. A PubMed search was performed using the following search terms: ultrasound, magnetic resonance imaging, sports hernia, athletic pubalgia, and groin pain. Date restrictions were not placed on the literature search. Clinical review. Level 4. MRI is sensitive in diagnosing pathology in groin pain. Not only can MRI be used to image rectus abdominis/adductor longus aponeurosis and pubic bone pathology, but it can also evaluate other pathology within the hip and pelvis. MRI is especially helpful when groin pain is poorly localized. Real-time capability makes ultrasound useful in evaluating the pubic symphyseal region, as it can be used for evaluation and treatment. MRI and US are valuable in diagnosing pathology in athletes with groin pain, with the added utility of treatment using US-guided intervention. Strength-of Recommendation Taxonomy: C.

Comparison of segmentation algorithms for fluorescence microscopy images of cells.

PubMed

Dima, Alden A; Elliott, John T; Filliben, James J; Halter, Michael; Peskin, Adele; Bernal, Javier; Kociolek, Marcin; Brady, Mary C; Tang, Hai C; Plant, Anne L

2011-07-01

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc.

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

NASA Technical Reports Server (NTRS)

Thompson, David R.; Mandrake, Lukas; Green, Robert O.

2013-01-01

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

Influence of the State of the Tungsten Tip on STM Topographic Images of SnSe Surfaces

NASA Astrophysics Data System (ADS)

Ly, Trinh Thi; Kim, Jungdae

2018-03-01

Tin selenide (SnSe) has recently attracted significant attention because of its excellent thermoelectric properties with a figure of merit (ZT) of 2.6. Previous scanning tunneling microscopy (STM) studies of SnSe surfaces showed that only Sn atoms are resolved in topographic images due to the dominant contribution of the Sn 5 p z states in tunneling. However, when the state of the tungsten (W) tip changes from a typical four-lobe d state such as d xy or {d_{{x^2} - {y^2}}} to a two-lobe {d_{{z^2}}} state, the atomic features observed on the SnSe surface in STM topography can be dramatically altered. In this report, we present the results of a systematic study on the influence of the W tip's states on the STM images of SnSe surfaces. Sn atoms are observed with much stronger corrugation amplitude and smaller apparent radius when the tip is in a {d_{{z^2}}} state. In addition, the atomic features of the Se atoms become visible because of the sharply focused shape of the W {d_{{z^2}}} state. We expect our results to provide important information for establishing a better understanding of the microscopic nature of SnSe surfaces.

Imaging of cerebellopontine angle lesions: an update. Part 2: intra-axial lesions, skull base lesions that may invade the CPA region, and non-enhancing extra-axial lesions.

PubMed

Bonneville, Fabrice; Savatovsky, Julien; Chiras, Jacques

2007-11-01

Computed tomography (CT) and magnetic resonance (MR) imaging reliably demonstrate typical features of vestibular schwannomas or meningiomas in the vast majority of mass lesions responsible for cerebellopontine angle (CPA) syndrome. However, a large variety of unusual lesions can also be encountered in the CPA. Covering the entire spectrum of lesions potentially found in the CPA, these articles explain the pertinent neuroimaging features that radiologists need to know to make clinically relevant diagnoses in these cases, including data from diffusion- and perfusion-weighted imaging or MR spectroscopy, when available. A diagnostic algorithm based on the lesion's site of origin, shape and margins, density, signal intensity and contrast material uptake is also proposed. Non-enhancing extra-axial CPA masses are cystic (epidermoid cyst, arachnoid cyst, neurenteric cyst) or contain fat (dermoid cyst, lipoma). Tumours can also extend into the CPA by extension from the skull base (paraganglioma, chondromatous tumours, chordoma, cholesterol granuloma, endolymphatic sac tumour). Finally, brain stem or ventricular tumours can present with a significant exophytic component in the CPA that may be difficult to differentiate from an extra-axial lesion (lymphoma, hemangioblastoma, choroid plexus papilloma, ependymoma, glioma, medulloblastoma, dysembryoplastic neuroepithelial tumour).

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Advanced Tie Feature Matching for the Registration of Mobile Mapping Imaging Data and Aerial Imagery

NASA Astrophysics Data System (ADS)

Jende, P.; Peter, M.; Gerke, M.; Vosselman, G.

2016-06-01

Mobile Mapping's ability to acquire high-resolution ground data is opposing unreliable localisation capabilities of satellite-based positioning systems in urban areas. Buildings shape canyons impeding a direct line-of-sight to navigation satellites resulting in a deficiency to accurately estimate the mobile platform's position. Consequently, acquired data products' positioning quality is considerably diminished. This issue has been widely addressed in the literature and research projects. However, a consistent compliance of sub-decimetre accuracy as well as a correction of errors in height remain unsolved. We propose a novel approach to enhance Mobile Mapping (MM) image orientation based on the utilisation of highly accurate orientation parameters derived from aerial imagery. In addition to that, the diminished exterior orientation parameters of the MM platform will be utilised as they enable the application of accurate matching techniques needed to derive reliable tie information. This tie information will then be used within an adjustment solution to correct affected MM data. This paper presents an advanced feature matching procedure as a prerequisite to the aforementioned orientation update. MM data is ortho-projected to gain a higher resemblance to aerial nadir data simplifying the images' geometry for matching. By utilising MM exterior orientation parameters, search windows may be used in conjunction with a selective keypoint detection and template matching. Originating from different sensor systems, however, difficulties arise with respect to changes in illumination, radiometry and a different original perspective. To respond to these challenges for feature detection, the procedure relies on detecting keypoints in only one image. Initial tests indicate a considerable improvement in comparison to classic detector/descriptor approaches in this particular matching scenario. This method leads to a significant reduction of outliers due to the limited availability of putative matches and the utilisation of templates instead of feature descriptors. In our experiments discussed in this paper, typical urban scenes have been used for evaluating the proposed method. Even though no additional outlier removal techniques have been used, our method yields almost 90% of correct correspondences. However, repetitive image patterns may still induce ambiguities which cannot be fully averted by this technique. Hence and besides, possible advancements will be briefly presented.

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

A Review of Optical NDT Technologies

PubMed Central

Zhu, Yong-Kai; Tian, Gui-Yun; Lu, Rong-Sheng; Zhang, Hong

2011-01-01

Optical non-destructive testing (NDT) has gained more and more attention in recent years, mainly because of its non-destructive imaging characteristics with high precision and sensitivity. This paper provides a review of the main optical NDT technologies, including fibre optics, electronic speckle, infrared thermography, endoscopic and terahertz technology. Among them, fibre optics features easy integration and embedding, electronic speckle focuses on whole-field high precision detection, infrared thermography has unique advantages for tests of combined materials, endoscopic technology provides images of the internal surface of the object directly, and terahertz technology opens a new direction of internal NDT because of its excellent penetration capability to most of non-metallic materials. Typical engineering applications of these technologies are illustrated, with a brief introduction of the history and discussion of recent progress. PMID:22164045

The Structure and Kinematics of Little Blue Spheroid Galaxies

NASA Astrophysics Data System (ADS)

Moffett, Amanda J.; Phillipps, Steven; Robotham, Aaron; Driver, Simon; Bremer, Malcolm; GAMA survey team, SAMI survey team

2018-01-01

A population of blue, morphologically early-type galaxies, dubbed "Little Blue Spheroids" (LBSs), has been identified as a significant contributor to the low redshift galaxy population in the GAMA survey. Using deep, high-resolution optical imaging from KiDS and the new Bayesian, two-dimensional galaxy profile modelling code PROFIT, we examine the detailed structural characteristics of LBSs, including low surface brightness components not detected in previous SDSS imaging. We find that these LBS galaxies combine features typical of early-type and late-type populations, with structural properties similar to other low-mass early types and star formation rates similar to low-mass late types. We further consider the environments and SAMI-derived IFU kinematics of LBSs in order to investigate the conditions of their formation and the current state of their dynamical evolution.

Reflectance confocal microscopy features of thin versus thick melanomas.

PubMed

Kardynal, Agnieszka; Olszewska, Małgorzata; de Carvalho, Nathalie; Walecka, Irena; Pellacani, Giovanni; Rudnicka, Lidia

2018-01-24

In vivo reflectance confocal microscopy (RCM) plays an increasingly important role in differential diagnosis of melanoma. The aim of the study was to assess typical confocal features of thin (≤1mm according to Breslow index) versus thick (>1mm) melanomas. 30 patients with histopathologically confirmed cutaneous melanoma were included in the study. Reflectance confocal microscopy was performed with Vivascope equipment prior to excision. Fifteen melanomas were thin (Breslow thickness ≤ 1mm) and 15 were thick melanomas (Breslow thickness >1mm). In the RCM examination, the following features were more frequently observed in thin compared to thick melanomas: edged papillae (26.7% vs 0%, p=0.032) and areas with honeycomb or cobblestone pattern (33.3% vs 6.7%, p=0.068). Both features are present in benign melanocytic lesions, so in melanoma are good prognostic factors. The group of thick melanomas compared to the group of thin melanomas in the RCM images presented with greater frequency of roundish cells (100% vs 40%, p=0.001), non-edged papillae (100% vs 60%, p=0.006), numerous pagetoid cells (73.3% vs 33.3%, p=0.028), numerous atypical cells at dermal-epidermal junction (53.3% vs 20%, p=0.058) and epidermal disarray (93.3% vs 66.7%, p=0.068). Non-invasive imaging methods helps in deepening of knowledge about the evolution and biology of melanoma. The most characteristic features for thin melanomas in confocal examination are: fragments of cobblestone or honeycomb pattern and edged papillae (as good prognostic factors). The features of thick melanomas in RCM examination are: roundish cells, non-edged papillae, numerous pagetoid cells at dermal-epidermal junction and epidermal disarray.

ALMA Images of the Host Cloud of the Intermediate-mass Black Hole Candidate CO‑0.40–0.22*: No Evidence for Cloud–Black Hole Interaction, but Evidence for a Cloud–Cloud Collision

NASA Astrophysics Data System (ADS)

Tanaka, Kunihiko

2018-06-01

This paper reports a reanalysis of archival ALMA data of the high velocity(-width) compact cloud CO‑0.40–0.22, which has recently been hypothesized to host an intermediate-mass black hole (IMBH). If beam-smearing effects, difference in beam sizes among frequency bands, and Doppler shift due to the motion of the Earth are considered accurately, none of the features reported as evidence for an IMBH in previous studies are confirmed in the reanalyzed ALMA images. Instead, through analysis of the position–velocity structure of the HCN J = 3–2 data cube, we have found kinematics typical of a cloud–cloud collision (CCC), namely, two distinct velocity components bridged by broad emission features with elevated temperatures and/or densities. One velocity component has a straight filamentary shape with approximately constant centroid velocities along its length but with a steep, V-shaped velocity gradient across its width. This contradicts the IMBH scenario but is consistent with a collision between two dissimilar-sized clouds. From a non-LTE analysis of the multitransition methanol lines, the volume density of the post-shock gas has been measured to be ≳106 cm‑3, indicating that the CCC shock can compress gas in a short timescale to densities typical of star-forming regions. Evidence for star formation has not been found, possibly because the cloud is in an early phase of CCC-triggered star formation or because the collision is nonproductive.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis

PubMed Central

Knecht, Avi C.; Campbell, Malachy T.; Caprez, Adam; Swanson, David R.; Walia, Harkamal

2016-01-01

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets. PMID:27141917

[Establishment of an iRFP and luciferase dual-color fluorescence-traced hepatocellular carcinoma transplantation model in nude mice].

PubMed

Li, Hongjun; Yang, Tianhua; Huang, Yanping; Liu, Mingzhu; Qin, Zhongqiang; Chu, Fei; Li, Zhenghong; Li, Yonghai

2017-11-01

Objective To establish a hepatocellular carcinoma xenograft model in nude mice which could stably express gene and be monitored dynamically. Methods We first constructed the lentiviral particles containing luciferase (Luc) and near-infrared fluorescent protein (iRFP) and puromycin resistance gene, and then transduced them into the HepG2 hepatoma cells. The cell line stably expressing Luc and iRFP genes were screened and inoculated into nude mice to establish xenograft tumor model. Tumor growth was monitored using in vivo imaging system. HE staining and immunohistochemistry were used to evaluate the pathological features and tumorigenic ability. Results HepG2 cells stably expressing iRFP and Luc were obtained; with the engineered cell line, xenograft model was successfully established with the features of proper tumor developing time and high rate of tumor formation as well as typical pathological features as showed by HE staining and immunohistochemistry. Conclusion Hepatocellular carcinoma model in nude mice with the features of stable gene expression and dynamical monitoring has been established successfully with the HepG2-iRFP-Luc cell line.

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets

NASA Astrophysics Data System (ADS)

Talai, Sahand; Boelmans, Kai; Sedlacik, Jan; Forkert, Nils D.

2017-03-01

Parkinsonian syndromes encompass a spectrum of neurodegenerative diseases, which can be classified into various subtypes. The differentiation of these subtypes is typically conducted based on clinical criteria. Due to the overlap of intra-syndrome symptoms, the accurate differential diagnosis based on clinical guidelines remains a challenge with failure rates up to 25%. The aim of this study is to present an image-based classification method of patients with Parkinson's disease (PD) and patients with progressive supranuclear palsy (PSP), an atypical variant of PD. Therefore, apparent diffusion coefficient (ADC) parameter maps were calculated based on diffusion-tensor magnetic resonance imaging (MRI) datasets. Mean ADC values were determined in 82 brain regions using an atlas-based approach. The extracted mean ADC values for each patient were then used as features for classification using a linear kernel support vector machine classifier. To increase the classification accuracy, a feature selection was performed, which resulted in the top 17 attributes to be used as the final input features. A leave-one-out cross validation based on 56 PD and 21 PSP subjects revealed that the proposed method is capable of differentiating PD and PSP patients with an accuracy of 94.8%. In conclusion, the classification of PD and PSP patients based on ADC features obtained from diffusion MRI datasets is a promising new approach for the differentiation of Parkinsonian syndromes in the broader context of decision support systems.

piscope - A Python based software package for the analysis of volcanic SO2 emissions using UV SO2 cameras

NASA Astrophysics Data System (ADS)

Gliss, Jonas; Stebel, Kerstin; Kylling, Arve; Solvejg Dinger, Anna; Sihler, Holger; Sudbø, Aasmund

2017-04-01

UV SO2 cameras have become a common method for monitoring SO2 emission rates from volcanoes. Scattered solar UV radiation is measured in two wavelength windows, typically around 310 nm and 330 nm (distinct / weak SO2 absorption) using interference filters. The data analysis comprises the retrieval of plume background intensities (to calculate plume optical densities), the camera calibration (to convert optical densities into SO2 column densities) and the retrieval of gas velocities within the plume as well as the retrieval of plume distances. SO2 emission rates are then typically retrieved along a projected plume cross section, for instance a straight line perpendicular to the plume propagation direction. Today, for most of the required analysis steps, several alternatives exist due to ongoing developments and improvements related to the measurement technique. We present piscope, a cross platform, open source software toolbox for the analysis of UV SO2 camera data. The code is written in the Python programming language and emerged from the idea of a common analysis platform incorporating a selection of the most prevalent methods found in literature. piscope includes several routines for plume background retrievals, routines for cell and DOAS based camera calibration including two individual methods to identify the DOAS field of view (shape and position) within the camera images. Gas velocities can be retrieved either based on an optical flow analysis or using signal cross correlation. A correction for signal dilution (due to atmospheric scattering) can be performed based on topographic features in the images. The latter requires distance retrievals to the topographic features used for the correction. These distances can be retrieved automatically on a pixel base using intersections of individual pixel viewing directions with the local topography. The main features of piscope are presented based on dataset recorded at Mt. Etna, Italy in September 2015.

Forensic Comparison and Matching of Fingerprints: Using Quantitative Image Measures for Estimating Error Rates through Understanding and Predicting Difficulty

PubMed Central

Kellman, Philip J.; Mnookin, Jennifer L.; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E.

2014-01-01

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons. PMID:24788812

Proposed new accelerator design for homeland security x-ray applications

DOE PAGES

Clayton, James; Shedlock, Daniel; Langeveld, Willem G.J.; ...

2015-01-01

Two goals for security scanning of cargo and freight are the ability to determine the type of material that is being imaged, and to do so at low radiation dose. One commonly used technique to determine the effective Z of the cargo is dual-energy imaging, i.e. imaging with different x-ray energy spectra. Another technique uses the fact that the transmitted x-ray spectrum itself also depends on the effective Z. Spectroscopy is difficult because the energy of individual x rays needs to be measured in a very high count-rate environment. Typical accelerators for security applications offer large but short bursts ofmore » x-rays, suitable for current-mode integrated imaging. In order to perform x-ray spectroscopy, a new accelerator design is desired that has the following features: 1) increased duty factor in order to spread out the arrival of x-rays at the detector array over time; 2) x-ray intensity modulation from one delivered pulse to the next by adjusting the accelerator electron beam instantaneous current so as to deliver adequate signal without saturating the spectroscopic detector; and 3) the capability to direct the (forward peaked) x-ray intensity towards high-attenuation areas in the cargo (“fan-beam-steering”). Current sources are capable of 0.1% duty factor, although usually they are operated at significantly lower duty factors (~0.04%), but duty factors in the range 0.4-1.0% are desired. The higher duty factor can be accomplished, e.g., by moving from 300 pulses per second (pps) to 1000 pps and/or increasing the pulse duration from a typical 4 μs to 10 μs. This paper describes initial R&D to examine cost effective modifications that could be performed on a typical accelerator for these purposes, as well as R&D for fan-beam steering.« less

Electroencephalographic and imaging profile in a subacute sclerosing panencephalitis (SSPE) cohort: a correlative study.

PubMed

Praveen-kumar, S; Sinha, S; Taly, A B; Jayasree, S; Ravi, V; Vijayan, J; Ravishankar, S

2007-09-01

There are only a few studies correlating diverse radiological and EEG features of subacute sclerosing panencephalitis (SSPE). The objective of the study was to (a) describe EEG profile and (b) correlate it with the clinical and imaging data of patients with confirmed SSPE. This study was conducted at a University teaching hospital in south India and involved 58 patients (M:F=37:21, age: 12.3, SD 4.8 years) of SSPE. Diagnosis of SSPE was based on the characteristic clinical manifestations, and raised IgG (1:625) anti-measles antibody in cerebrospinal fluid (CSF) by ELISA in all the patients. Scalp EEGs were recorded on 16 channel machines using standard parameters and procedures. The EEG, clinical and imaging data were reviewed. EEGs were frequently abnormal: typical (37) and atypical (21). Diffuse slowing of background activity (BGA) was noted in 46 records being asymmetrical in six. Periodic complexes were periodic (32), quasi-periodic (21) or a-periodic (4). Periodic complexes (PC) (amplitude: 370.7, SD 171.2 microV; duration - 1.7, SD 2.0 s; inter-complex interval: 8.4, SD 9.2s) were symmetrical in 39 and asymmetrical in 19. CT (32) and MRI (23) scans were normal in 16 patients while others had white matter (15), cerebral edema (8), cerebral atrophy (8), basal ganglia (2), and thalamic (2) changes. There was an independent association of frontally dominant slowing of BGA (p=0.04) and typical PCs (p=0.03) with the diffuse cerebral edema on imaging. White matter changes correlated with slowing of BGA (p=0.04), but not with typical PC (p=0.16). This study provides valuable insight into the structural and clinical correlates of EEG changes in SSPE. Irrespective of the incidence of occurrence of SSPE in a community, a clinician should be aware of the wide spectra of EEG findings. This study also discusses the possible underlying structural and clinical correlates.

Affective evaluation of food images according to stimulus and subject characteristics.

PubMed

Padulo, C; Carlucci, L; Marzoli, D; Manippa, V; Tommasi, L; Saggino, A; Puglisi-Allegra, S; Brancucci, A

2018-04-17

The food-rich environment in which we live makes the regulation of food choices a very complex phenomenon determined by many factors, as well as their interactions. Much evidence suggests that the sensory perception of food can be considered as a central factor affecting individual food choices. Despite this, the approaches used to study the various food aspects usually do not distinguish between different types of food. In the present study, a large and heterogeneous sample of 1149 participants aged 7-90 years was asked to judge food images that were labelled differently (i.e. Raw versus Cooked, Natural versus Transformed and Simple versus Complex) with respect to arousal, valence, typicality and familiarity. We observed that, across food dimensions (i.e., Raw versus Cooked, Natural versus Transformed and Simple versus Complex), arousal, valence and typicality judgments were principally affected by a subjective hunger level and gender (and their interaction) and, to a lesser extent, by age. As a whole, our findings suggest that the level of transformation (which includes cooking) and the complexity of a foodstuff could at least partially affect food processing, entailing that future research should also address these features. © 2018 The British Dietetic Association Ltd.

Unsupervised feature learning for autonomous rock image classification

NASA Astrophysics Data System (ADS)

Shu, Lei; McIsaac, Kenneth; Osinski, Gordon R.; Francis, Raymond

2017-09-01

Autonomous rock image classification can enhance the capability of robots for geological detection and enlarge the scientific returns, both in investigation on Earth and planetary surface exploration on Mars. Since rock textural images are usually inhomogeneous and manually hand-crafting features is not always reliable, we propose an unsupervised feature learning method to autonomously learn the feature representation for rock images. In our tests, rock image classification using the learned features shows that the learned features can outperform manually selected features. Self-taught learning is also proposed to learn the feature representation from a large database of unlabelled rock images of mixed class. The learned features can then be used repeatedly for classification of any subclass. This takes advantage of the large dataset of unlabelled rock images and learns a general feature representation for many kinds of rocks. We show experimental results supporting the feasibility of self-taught learning on rock images.

Online coupled camera pose estimation and dense reconstruction from video

DOEpatents

Medioni, Gerard; Kang, Zhuoliang

2016-11-01

A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing the next video image base on the generated information indicative of the position and orientation of the image capture device at the time of capturing the received image. The product may display the updated 3D model after each update to the model.

Magnetic resonance imaging findings of cellular angiofibroma of the tunica vaginalis of the testis: a case report.

PubMed

Ntorkou, Alexandra A; Tsili, Athina C; Giannakis, Dimitrios; Batistatou, Anna; Stavrou, Sotirios; Sofikitis, Nikolaos; Argyropoulou, Maria I

2016-03-31

Cellular angiofibroma represents a rare mesenchymal tumor typically involving the inguinoscrotal area in middle-aged men. Although the origin of this benign tumor is unknown, it is histologically classified as an angiomyxoid tumor. Cellular angiofibroma is characterized by a diversity of pathological and imaging features. An accurate preoperative diagnosis is challenging. Magnetic resonance imaging examination of the scrotum has been reported as a valuable adjunct modality in the investigation of scrotal pathology. The technique by providing both structural and functional information is useful in the differentiation between extratesticular and intratesticular diseases and in the preoperative characterization of the histologic nature of various scrotal lesions. There are few reports in the English literature addressing the magnetic resonance imaging findings of cellular angiofibroma of the scrotum and no reports on functional magnetic resonance imaging data. Here we present the first case of a cellular angiofibroma arising from the tunica vaginalis of the testis and we discuss the value of a multiparametric magnetic resonance protocol, including diffusion-weighted imaging, magnetization transfer imaging and dynamic contrast-enhanced magnetic resonance imaging in the preoperative diagnosis of this rare neoplasm. A 47-year Greek man presented with a painless left scrotal swelling, which had gradually enlarged during the last 6 months. Magnetic resonance imaging of his scrotum displayed a left paratesticular mass, in close proximity to the tunica vaginalis, with heterogeneous high signal intensity on T2-weighted images and no areas of restricted diffusion. The tumor was hypointense on magnetization transfer images, suggestive for the presence of macromolecules. On dynamic contrast-enhanced magnetic resonance imaging the mass showed intense heterogeneous enhancement with a type II curve. Magnetic resonance imaging findings were strongly suggestive of a benign paratesticular tumor, which was confirmed on pathology following lesion excision. Magnetic resonance imaging of the scrotum by combining conventional and functional magnetic resonance data provides useful diagnostic information in the preoperative characterization of scrotal masses. A possible diagnosis of a benign paratesticular tumor based on magnetic resonance imaging features may improve patient care and decrease the number of unnecessary radical surgical explorations.

Scale-invariant feature extraction of neural network and renormalization group flow

NASA Astrophysics Data System (ADS)

Iso, Satoshi; Shiba, Shotaro; Yokoo, Sumito

2018-05-01

Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from T =0 to T =6 generates a flow along which the temperature approaches the critical value Tc=2.2 7 . This behavior is the opposite of the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards Tc and how the RBM learns to extract features of spin configurations.

Is the recall of verbal-spatial information from working memory affected by symptoms of ADHD?

PubMed

Caterino, Linda C; Verdi, Michael P

2012-10-01

OJECTIVE: The Kulhavy model for text learning using organized spatial displays proposes that learning will be increased when participants view visual images prior to related text. In contrast to previous studies, this study also included students who exhibited symptoms of ADHD. Participants were presented with either a map-text or text-map condition. The map-text condition led to a significantly higher performance than the text-map condition, overall. However, students who endorsed more symptoms of inattention and hyperactivity-impulsivity scored more poorly when asked to recall text facts, text features, and map features and were less able to correctly place map features on a reconstructed map than were students who endorsed fewer symptoms. The results of the study support the Kulhavy model for typical students; however, the benefit of viewing a display prior to text was not seen for students with ADHD symptoms, thus supporting previous studies that have demonstrated that ADHD appears to negatively affect operations that occur in working memory.

Investigating Mars: Pavonis Mons

NASA Image and Video Library

2017-11-10

This image shows the central part of the smaller summit caldera on Pavonis Mons. On the top side of the caldera is a complex region of fault related collapse of the wall of the caldera. Several intersecting faults are visible on the top of the image. The faults would have formed areas of weakness in the caldera wall, precipitating into gravity driven down slope movement of materials. This caldera is approximately 5km deep. In shield volcanoes calderas are typically formed where the surface collapses into the void formed by an emptied magma chamber. Pavonis Mons is one of the three aligned Tharsis Volcanoes. The four Tharsis volcanoes are Ascreaus Mons, Pavonis Mons, Arsia Mons, and Olympus Mars. All four are shield type volcanoes. Shield volcanoes are formed by lava flows originating near or at the summit, building up layers upon layers of lava. The Hawaiian islands on Earth are shield volcanoes. The three aligned volcanoes are located along a topographic rise in the Tharsis region. Along this trend there are increased tectonic features and additional lava flows. Pavonis Mons is the smallest of the four volcanoes, rising 14km above the mean Mars surface level with a width of 375km. It has a complex summit caldera, with the smallest caldera deeper than the larger caldera. Like most shield volcanoes the surface has a low profile. In the case of Pavonis Mons the average slope is only 4 degrees. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 56113 Latitude: 0.512694 Longitude: 247.192 Instrument: VIS Captured: 2014-08-08 02:25 https://photojournal.jpl.nasa.gov/catalog/PIA22026

Investigating Mars: Pavonis Mons

NASA Image and Video Library

2017-11-08

This image shows the western part of the smaller summit caldera on Pavonis Mons. On this side of the caldera is a complex region of fault related collapse of the wall of the caldera. Several intersecting faults are visible to the top and center part of the image. The faults would have formed areas of weakness in the caldera wall, precipitating into gravity driven down slope movement of materials. This caldera is approximately 5km deep. In shield volcanoes calderas are typically formed where the surface collapses into the void formed by an emptied magma chamber. Pavonis Mons is one of the three aligned Tharsis Volcanoes. The four Tharsis volcanoes are Ascreaus Mons, Pavonis Mons, Arsia Mons, and Olympus Mars. All four are shield type volcanoes. Shield volcanoes are formed by lava flows originating near or at the summit, building up layers upon layers of lava. The Hawaiian islands on Earth are shield volcanoes. The three aligned volcanoes are located along a topographic rise in the Tharsis region. Along this trend there are increased tectonic features and additional lava flows. Pavonis Mons is the smallest of the four volcanoes, rising 14km above the mean Mars surface level with a width of 375km. It has a complex summit caldera, with the smallest caldera deeper than the larger caldera. Like most shield volcanoes the surface has a low profile. In the case of Pavonis Mons the average slope is only 4 degrees. The Odyssey spacecraft has spent over 15 years in orbit around Mars, circling the planet more than 69000 times. It holds the record for longest working spacecraft at Mars. THEMIS, the IR/VIS camera system, has collected data for the entire mission and provides images covering all seasons and lighting conditions. Over the years many features of interest have received repeated imaging, building up a suite of images covering the entire feature. From the deepest chasma to the tallest volcano, individual dunes inside craters and dune fields that encircle the north pole, channels carved by water and lava, and a variety of other feature, THEMIS has imaged them all. For the next several months the image of the day will focus on the Tharsis volcanoes, the various chasmata of Valles Marineris, and the major dunes fields. We hope you enjoy these images! Orbit Number: 36607 Latitude: 0.609285 Longitude: 246.862 Instrument: VIS Captured: 2010-03-16 13:44 https://photojournal.jpl.nasa.gov/catalog/PIA22024

High Spatiotemporal Resolution Dynamic Contrast-Enhanced MR Enterography in Crohn Disease Terminal Ileitis Using Continuous Golden-Angle Radial Sampling, Compressed Sensing, and Parallel Imaging.

PubMed

Ream, Justin M; Doshi, Ankur; Lala, Shailee V; Kim, Sooah; Rusinek, Henry; Chandarana, Hersh

2015-06-01

The purpose of this article was to assess the feasibility of golden-angle radial acquisition with compress sensing reconstruction (Golden-angle RAdial Sparse Parallel [GRASP]) for acquiring high temporal resolution data for pharmacokinetic modeling while maintaining high image quality in patients with Crohn disease terminal ileitis. Fourteen patients with biopsy-proven Crohn terminal ileitis were scanned using both contrast-enhanced GRASP and Cartesian breath-hold (volume-interpolated breath-hold examination [VIBE]) acquisitions. GRASP data were reconstructed with 2.4-second temporal resolution and fitted to the generalized kinetic model using an individualized arterial input function to derive the volume transfer coefficient (K(trans)) and interstitial volume (v(e)). Reconstructions, including data from the entire GRASP acquisition and Cartesian VIBE acquisitions, were rated for image quality, artifact, and detection of typical Crohn ileitis features. Inflamed loops of ileum had significantly higher K(trans) (3.36 ± 2.49 vs 0.86 ± 0.49 min(-1), p < 0.005) and v(e) (0.53 ± 0.15 vs 0.20 ± 0.11, p < 0.005) compared with normal bowel loops. There were no significant differences between GRASP and Cartesian VIBE for overall image quality (p = 0.180) or detection of Crohn ileitis features, although streak artifact was worse with the GRASP acquisition (p = 0.001). High temporal resolution data for pharmacokinetic modeling and high spatial resolution data for morphologic image analysis can be achieved in the same acquisition using GRASP.

Towards automatic patient selection for chemotherapy in colorectal cancer trials

NASA Astrophysics Data System (ADS)

Wright, Alexander; Magee, Derek; Quirke, Philip; Treanor, Darren E.

2014-03-01

A key factor in the prognosis of colorectal cancer, and its response to chemoradiotherapy, is the ratio of cancer cells to surrounding tissue (the so called tumour:stroma ratio). Currently tumour:stroma ratio is calculated manually, by examining H&E stained slides and counting the proportion of area of each. Virtual slides facilitate this analysis by allowing pathologists to annotate areas of tumour on a given digital slide image, and in-house developed stereometry tools mark random, systematic points on the slide, known as spots. These spots are examined and classified by the pathologist. Typical analyses require a pathologist to score at least 300 spots per tumour. This is a time consuming (10- 60 minutes per case) and laborious task for the pathologist and automating this process is highly desirable. Using an existing dataset of expert-classified spots from one colorectal cancer clinical trial, an automated tumour:stroma detection algorithm has been trained and validated. Each spot is extracted as an image patch, and then processed for feature extraction, identifying colour, texture, stain intensity and object characteristics. These features are used as training data for a random forest classification algorithm, and validated against unseen image patches. This process was repeated for multiple patch sizes. Over 82,000 such patches have been used, and results show an accuracy of 79%, depending on image patch size. A second study examining contextual requirements for pathologist scoring was conducted and indicates that further analysis of structures within each image patch is required in order to improve algorithm accuracy.

Automated renal histopathology: digital extraction and quantification of renal pathology

NASA Astrophysics Data System (ADS)

Sarder, Pinaki; Ginley, Brandon; Tomaszewski, John E.

2016-03-01

The branch of pathology concerned with excess blood serum proteins being excreted in the urine pays particular attention to the glomerulus, a small intertwined bunch of capillaries located at the beginning of the nephron. Normal glomeruli allow moderate amount of blood proteins to be filtered; proteinuric glomeruli allow large amount of blood proteins to be filtered. Diagnosis of proteinuric diseases requires time intensive manual examination of the structural compartments of the glomerulus from renal biopsies. Pathological examination includes cellularity of individual compartments, Bowman's and luminal space segmentation, cellular morphology, glomerular volume, capillary morphology, and more. Long examination times may lead to increased diagnosis time and/or lead to reduced precision of the diagnostic process. Automatic quantification holds strong potential to reduce renal diagnostic time. We have developed a computational pipeline capable of automatically segmenting relevant features from renal biopsies. Our method first segments glomerular compartments from renal biopsies by isolating regions with high nuclear density. Gabor texture segmentation is used to accurately define glomerular boundaries. Bowman's and luminal spaces are segmented using morphological operators. Nuclei structures are segmented using color deconvolution, morphological processing, and bottleneck detection. Average computation time of feature extraction for a typical biopsy, comprising of ~12 glomeruli, is ˜69 s using an Intel(R) Core(TM) i7-4790 CPU, and is ~65X faster than manual processing. Using images from rat renal tissue samples, automatic glomerular structural feature estimation was reproducibly demonstrated for 15 biopsy images, which contained 148 individual glomeruli images. The proposed method holds immense potential to enhance information available while making clinical diagnoses.

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Hall, Lawrence O.; Goldgof, Dmitry B.; Gatenby, Robert A.; Gillies, Robert; Drukteinis, Jennifer S.

2014-03-01

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

Chance-type flexion-distraction injuries in the thoracolumbar spine: MR imaging characteristics.

PubMed

Groves, Clare J; Cassar-Pullicino, Victor N; Tins, Bernhard J; Tyrrell, Prudencia N M; McCall, Iain W

2005-08-01

To evaluate retrospectively the magnetic resonance (MR) imaging features of Chance-type flexion-distraction injuries. The authors' institutional review board does not require its approval or patient informed consent for retrospective studies. Imaging data were reviewed retrospectively for 24 patients (15 male, nine female; mean age, 28 years; range, 9-71 years) who had sustained radiographically typical Chance-type flexion-distraction injuries. The posterior vertebral body height remained unchanged or was increased in these patients. Two radiologists recorded a variety of bone and soft-tissue abnormalities seen with MR imaging. Based on consensus, the documented findings were sequentially analyzed to determine their frequencies. Combined bone and soft-tissue injuries occurred in 23 (96%) of 24 patients, were more common than soft-tissue damage alone (one [4%] of 24 patients), and occurred primarily at the thoracolumbar junction. Contiguous vertebral injury was seen in 20 (83%) of 24 patients, usually in the form of anterosuperior vertebral endplate edema, while noncontiguous injury occurred in eight (33%) of 24 patients. Extensive subcutaneous and paraspinal muscle edema was seen in all patients and extended over several segments. Posterior osteoligamentous complex disruption also occurred in all patients. Horizontally oriented fractures of the posterior neural arches produced an MR imaging pattern that the authors call the sandwich sign, which consists of linear hemorrhage framed by marrow edema. This sign was seen in 12 (50%) of 24 patients. In seven (29%) of 24 patients, a fracture line extending from a damaged pedicle was seen to exit through the contralateral posterosuperior aspect of the vertebral body, with extension of the fracture fragments into the spinal canal. A spectrum of features is discernible with MR imaging in Chance-type injuries.

Can X-ray spectrum imaging replace backscattered electrons for compositional contrast in the scanning electron microscope?

PubMed

Newbury, Dale E; Ritchie, Nicholas W M

2011-01-01

The high throughput of the silicon drift detector energy dispersive X-ray spectrometer (SDD-EDS) enables X-ray spectrum imaging (XSI) in the scanning electron microscope to be performed in frame times of 10-100 s, the typical time needed to record a high-quality backscattered electron (BSE) image. These short-duration XSIs can reveal all elements, except H, He, and Li, present as major constituents, defined as 0.1 mass fraction (10 wt%) or higher, as well as minor constituents in the range 0.01-0.1 mass fraction, depending on the particular composition and possible interferences. Although BSEs have a greater abundance by a factor of 100 compared with characteristic X-rays, the strong compositional contrast in element-specific X-ray maps enables XSI mapping to compete with BSE imaging to reveal compositional features. Differences in the fraction of the interaction volume sampled by the BSE and X-ray signals lead to more delocalization of the X-ray signal at abrupt compositional boundaries, resulting in poorer spatial resolution. Improved resolution in X-ray elemental maps occurs for the case of a small feature composed of intermediate to high atomic number elements embedded in a matrix of lower atomic number elements. XSI imaging strongly complements BSE imaging, and the SDD-EDS technology enables an efficient combined BSE-XSI measurement strategy that maximizes the compositional information. If 10 s or more are available for the measurement of an area of interest, the analyst should always record the combined BSE-XSI information to gain the advantages of both measures of compositional contrast. Copyright © 2011 Wiley Periodicals, Inc.

Calibrated Multi-Temporal Edge Images for City Infrastructure Growth Assessment and Prediction

NASA Astrophysics Data System (ADS)

Al-Ruzouq, R.; Shanableh, A.; Boharoon, Z.; Khalil, M.

2018-03-01

Urban Growth or urbanization can be defined as the gradual process of city's population growth and infrastructure development. It is typically demonstrated by the expansion of a city's infrastructure, mainly development of its roads and buildings. Uncontrolled urban Growth in cities has been responsible for several problems that include living environment, drinking water, noise and air pollution, waste management, traffic congestion and hydraulic processes. Accurate identification of urban growth is of great importance for urban planning and water/land management. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, allows for efficient identification of change patterns and the prediction of built-up areas. In this study, two approaches were adapted to quantify and assess the pattern of urbanization, in Ajman City at UAE, during the last three decades. The first approach relies on image processing techniques and multi-temporal Landsat satellite images with ground resolution varying between 15 to 60 meters. In this approach, the derived edge images (roads and buildings) were used as the basis of change detection. The second approach relies on digitizing features from high-resolution images captured at different years. The latest approach was adopted, as a reference and ground truth, to calibrate extracted edges from Landsat images. It has been found that urbanized area almost increased by 12 folds during the period 1975-2015 where the growth of buildings and roads were almost parallel until 2005 when the roads spatial expansion witnessed a steep increase due to the vertical expansion of the City. Extracted Edges features, were successfully used for change detection and quantification in term of buildings and roads.

A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

PubMed

Yuan, Tao; Zheng, Xinqi; Hu, Xuan; Zhou, Wei; Wang, Wei

2014-01-01

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

Neptune Long-Lived Atmospheric Features in 2013 - 2015 from Small (28-cm) to Large (10-m) Telescopes

NASA Technical Reports Server (NTRS)

Hueso, R.; de Pater, I.; Simon, A.; Sanchez-Lavega, A.; Delcroix, M.; Wong, M. H.; Tollefson, J. W.; Baranec, C.; de Kleer, K.; Luszcz-Cook, S. H.;

Avoiding drying-artifacts in transmission electron microscopy: Characterizing the size and colloidal state of nanoparticles

PubMed Central

Michen, Benjamin; Geers, Christoph; Vanhecke, Dimitri; Endes, Carola; Rothen-Rutishauser, Barbara; Balog, Sandor; Petri-Fink, Alke

2015-01-01

Standard transmission electron microscopy nanoparticle sample preparation generally requires the complete removal of the suspending liquid. Drying often introduces artifacts, which can obscure the state of the dispersion prior to drying and preclude automated image analysis typically used to obtain number-weighted particle size distribution. Here we present a straightforward protocol for prevention of the onset of drying artifacts, thereby allowing the preservation of in-situ colloidal features of nanoparticles during TEM sample preparation. This is achieved by adding a suitable macromolecular agent to the suspension. Both research- and economically-relevant particles with high polydispersity and/or shape anisotropy are easily characterized following our approach (http://bsa.bionanomaterials.ch), which allows for rapid and quantitative classification in terms of dimensionality and size: features that are major targets of European Union recommendations and legislation. PMID:25965905

Seismic reflection imaging of shallow oceanographic structures

NASA Astrophysics Data System (ADS)

Piété, Helen; Marié, Louis; Marsset, Bruno; Thomas, Yannick; Gutscher, Marc-André

2013-05-01

Multichannel seismic (MCS) reflection profiling can provide high lateral resolution images of deep ocean thermohaline fine structure. However, the shallowest layers of the water column (z < 150 m) have remained unexplored by this technique until recently. In order to explore the feasibility of shallow seismic oceanography (SO), we reprocessed and analyzed four multichannel seismic reflection sections featuring reflectors at depths between 10 and 150 m. The influence of the acquisition parameters was quantified. Seismic data processing dedicated to SO was also investigated. Conventional seismic acquisition systems were found to be ill-suited to the imaging of shallow oceanographic structures, because of a high antenna filter effect induced by large offsets and seismic trace lengths, and sources that typically cannot provide both a high level of emission and fine vertical resolution. We considered a test case, the imagery of the seasonal thermocline on the western Brittany continental shelf. New oceanographic data acquired in this area allowed simulation of the seismic acquisition. Sea trials of a specifically designed system were performed during the ASPEX survey, conducted in early summer 2012. The seismic device featured: (i) four seismic streamers, each consisting of six traces of 1.80 m; (ii) a 1000 J SIG sparker source, providing a 400 Hz signal with a level of emission of 205 dB re 1 μPa @ 1 m. This survey captured the 15 m thick, 30 m deep seasonal thermocline in unprecedented detail, showing images of vertical displacements most probably induced by internal waves.

Fatigue-type stress fractures of the lower limb associated with fibrous cortical defects/non-ossifying fibromas in the skeletally immature.

PubMed

Shimal, A; Davies, A M; James, S L J; Grimer, R J

2010-05-01

To investigate the association of a fatigue-type stress fracture and a fibrous cortical defect/non-ossifying fibroma (FCD/NOF) of the lower limb long bones in skeletally immature patients. The patient database of a specialist orthopaedic oncology centre was searched to determine the number of skeletally immature patients (

Light sheet microscopy.

PubMed

Weber, Michael; Mickoleit, Michaela; Huisken, Jan

2014-01-01

This chapter introduces the concept of light sheet microscopy along with practical advice on how to design and build such an instrument. Selective plane illumination microscopy is presented as an alternative to confocal microscopy due to several superior features such as high-speed full-frame acquisition, minimal phototoxicity, and multiview sample rotation. Based on our experience over the last 10 years, we summarize the key concepts in light sheet microscopy, typical implementations, and successful applications. In particular, sample mounting for long time-lapse imaging and the resulting challenges in data processing are discussed in detail. © 2014 Elsevier Inc. All rights reserved.

Ultrasonography of ovarian masses using a pattern recognition approach

PubMed Central

Jung, Sung Il

2015-01-01

As a primary imaging modality, ultrasonography (US) can provide diagnostic information for evaluating ovarian masses. Using a pattern recognition approach through gray-scale transvaginal US, ovarian masses can be diagnosed with high specificity and sensitivity. Doppler US may allow ovarian masses to be diagnosed as benign or malignant with even greater confidence. In order to differentiate benign and malignant ovarian masses, it is necessary to categorize ovarian masses into unilocular cyst, unilocular solid cyst, multilocular cyst, multilocular solid cyst, and solid tumor, and then to detect typical US features that demonstrate malignancy based on pattern recognition approach. PMID:25797108

Fat Embolism Syndrome: A Case Report and Review Literature.

PubMed

Uransilp, Nattaphol; Muengtaweepongsa, Sombat; Chanalithichai, Nuttawut; Tammachote, Nattapol

2018-01-01

Fat embolism syndrome (FES) is a life-threatening complication in patients with orthopedic trauma, especially long bone fractures. The diagnosis of fat embolism is made by clinical features alone with no specific laboratory findings. FES has no specific treatment and requires supportive care, although it can be prevented by early fixation of bone fractures. Here, we report a case of FES in a patient with right femoral neck fracture, which was diagnosed initially by Gurd's criteria and subsequently confirmed by typical appearances on magnetic resonance imaging (MRI) of the brain. The patient received supportive management and a short course of intravenous methylprednisolone.

Bilateral primary xanthoma of the humeri with pathologic fractures: A case report

PubMed Central

Ali, Sayed; Fedenko, Alex; Syed, Ali B; Matcuk, George; Patel, Dakshesh; Gottsegen, Chris; White, Eric

2013-01-01

Xanthomas are rare bone tumors that occur more often in the appendicular skeleton and typically appear radiographically benign, with a narrow zone of transition and a sclerotic rim. We report the case of a 57-year-old woman with hyperlipidemia presenting with bilateral shoulder pain after minor trauma. Radiographic and histopathologic investigation demonstrated intraosseous xanthoma with atypical features, including multifocality, a wide zone of transition and pathologic fractures-characteristics more commonly associated with aggressive lesions such as multiple myeloma or metastasis. The diagnosis, imaging, and histological appearance of xanthoma of bone are reviewed. PMID:24198913

The pivotal role of inflammation in scar/keloid formation after acne

PubMed Central

Shi, Chao; Zhu, Jianyu; Yang, Degang

2017-01-01

ABSTRACT Most keloids are clinically observed as solid nodules or claw-like extensions. However, they appear hypoechoic on ultrasound images and are therefore easily confused with liquid features such as blood or vessels. The pathological manifestations of typical keloids also include prominent, thick blood vessels. The existing classification of scars fails to reflect the natural history of keloids. The outer characteristics of a typical keloid include bright red hyperplasia with abundant vessels, suggesting the importance of vascular components in the process of scar formation and prompting consideration of the role of inflammation in the development of granular hyperplasia. Additionally, we further considered the potential effectiveness of oral isotretinoin for severe keloids secondary to severe acne. We also explored different principles and applications related to 5-fluorouracil (5-FU), pulsed dye laser (PDL), and CO2 laser treatments for scars. PMID:29707102

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

The DalHouses: 100 new photographs of houses with ratings of typicality, familiarity, and degree of similarity to faces.

PubMed

Filliter, Jillian H; Glover, Jacqueline M; McMullen, Patricia A; Salmon, Joshua P; Johnson, Shannon A

2016-03-01

Houses have often been used as comparison stimuli in face-processing studies because of the many attributes they share with faces (e.g., distinct members of a basic category, consistent internal features, mono-orientation, and relative familiarity). Despite this, no large, well-controlled databases of photographs of houses that have been developed for research use currently exist. To address this gap, we photographed 100 houses and carefully edited these images. We then asked 41 undergraduate students (18 to 31 years of age) to rate each house on three dimensions: typicality, likeability, and face-likeness. The ratings had a high degree of face validity, and analyses revealed a significant positive correlation between typicality and likeability. We anticipate that this stimulus set (i.e., the DalHouses) and the associated ratings will prove useful to face-processing researchers by minimizing the effort required to acquire stimuli and allowing for easier replication and extension of studies. The photographs of all 100 houses and their ratings data can be obtained at http://dx.doi.org/10.6084/m9.figshare.1279430.

Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

PubMed Central

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C.

2015-01-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data,, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked auto-encoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework image registration experiments were conducted on 7.0-tesla brain MR images. In all experiments, the results showed the new image registration framework consistently demonstrated more accurate registration results when compared to state-of-the-art. PMID:26552069

Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.

PubMed

Wu, Guorong; Kim, Minjeong; Wang, Qian; Munsell, Brent C; Shen, Dinggang

2016-07-01

Feature selection is a critical step in deformable image registration. In particular, selecting the most discriminative features that accurately and concisely describe complex morphological patterns in image patches improves correspondence detection, which in turn improves image registration accuracy. Furthermore, since more and more imaging modalities are being invented to better identify morphological changes in medical imaging data, the development of deformable image registration method that scales well to new image modalities or new image applications with little to no human intervention would have a significant impact on the medical image analysis community. To address these concerns, a learning-based image registration framework is proposed that uses deep learning to discover compact and highly discriminative features upon observed imaging data. Specifically, the proposed feature selection method uses a convolutional stacked autoencoder to identify intrinsic deep feature representations in image patches. Since deep learning is an unsupervised learning method, no ground truth label knowledge is required. This makes the proposed feature selection method more flexible to new imaging modalities since feature representations can be directly learned from the observed imaging data in a very short amount of time. Using the LONI and ADNI imaging datasets, image registration performance was compared to two existing state-of-the-art deformable image registration methods that use handcrafted features. To demonstrate the scalability of the proposed image registration framework, image registration experiments were conducted on 7.0-T brain MR images. In all experiments, the results showed that the new image registration framework consistently demonstrated more accurate registration results when compared to state of the art.

Evolution of certain typical and atypical features in a case of subacute sclerosing panencephalitis

PubMed Central

Raut, Tushar Premraj; Singh, Maneesh Kumar; Garg, Ravindra Kumar; Rai, Dheeraj

2012-01-01

Subacute sclerosing panencephalitis (SSPE) is a slowly progressive inflammatory disease of the central nervous system caused by a persistent measles virus usually affecting the childhood and adolescent age group. Clinical features at onset are very subtle and non-specific. Certain atypical features can occur at onset or during the course of illness which can be misleading. Neuroimaging features often are non-specific. Features like myoclonic jerks, cognitive decline and typical EEG findings lead to a strong suspicion of SSPE. Here, we describe the stagewise progression of a case of SSPE in a 14-year-old girl who had myoclonic jerks and cognitive decline at onset. During the course of disease, the patient developed cortical vision loss, atypical extrapyramidal features like segmental and hemifacial dystonia ultimately leading to a bedbound vegetative state. EEG showed typical periodic discharges along with positive cerebrospinal fluid serology for measles. PMID:23266775

Evolution of certain typical and atypical features in a case of subacute sclerosing panencephalitis.

PubMed

Raut, Tushar Premraj; Singh, Maneesh Kumar; Garg, Ravindra Kumar; Rai, Dheeraj

2012-12-23

Subacute sclerosing panencephalitis (SSPE) is a slowly progressive inflammatory disease of the central nervous system caused by a persistent measles virus usually affecting the childhood and adolescent age group. Clinical features at onset are very subtle and non-specific. Certain atypical features can occur at onset or during the course of illness which can be misleading. Neuroimaging features often are non-specific. Features like myoclonic jerks, cognitive decline and typical EEG findings lead to a strong suspicion of SSPE. Here, we describe the stagewise progression of a case of SSPE in a 14-year-old girl who had myoclonic jerks and cognitive decline at onset. During the course of disease, the patient developed cortical vision loss, atypical extrapyramidal features like segmental and hemifacial dystonia ultimately leading to a bedbound vegetative state. EEG showed typical periodic discharges along with positive cerebrospinal fluid serology for measles.

A case of recurrent depressive disorder presenting with Alice in Wonderland syndrome: psychopathology and pre- and post-treatment FDG-PET findings.

PubMed

Yokoyama, Tatsushi; Okamura, Tsuyoshi; Takahashi, Miwako; Momose, Toshimitsu; Kondo, Shinsuke

2017-04-27

Alice in Wonderland syndrome (AIWS) is a rare neuropsychiatric syndrome that typically manifests in distortion of extrapersonal visual image, altered perception of one's body image, and a disturbed sense of the passage of distance and time. Several conditions have been reported to contribute to AIWS, although its biological basis is still unknown. Here, we present the first case demonstrating a clear concurrence of recurrent depressive disorder and AIWS. The clinical manifestations and pre- and post-treatment fluorodeoxyglucose positron-emission tomographic (FDG-PET) images provide insights into the psychopathological and biological basis of AIWS. We describe a 63-year-old Japanese male who developed two distinct episodes of major depression concurrent with AIWS. In addition to typical AIWS perceptual symptoms, he complained of losing the ability to intuitively grasp the seriousness of news and the value of money, which implies disturbance of high-order cognition related to estimating magnitude and worth. Both depression and AIWS remitted after treatment in each episode. Pre-treatment FDG-PET images showed significant hypometabolism in the frontal cortex and hypermetabolism in the occipital and parietal cortex. Post-treatment images showed improvement of these abnormalities. The clinical co-occurrence of depressive episodes and presentation of AIWS can be interpreted to mean that they have certain functional disturbances in common. In view of incapacity, indifference, devitalization, altered perception of one's body image, and disturbed sense of time and space, the features of AIWS analogous to those of psychotic depression imply a common psychopathological basis. These high-order brain dysfunctions are possibly associated with the metabolic abnormalities in visual and parietotemporal association cortices that we observed on the pre- and post-treatment FDG-PET images in this case, while the hypometabolism in the frontal cortex is probably associated with depressive symptoms.

Correlative imaging across microscopy platforms using the fast and accurate relocation of microscopic experimental regions (FARMER) method

NASA Astrophysics Data System (ADS)

Huynh, Toan; Daddysman, Matthew K.; Bao, Ying; Selewa, Alan; Kuznetsov, Andrey; Philipson, Louis H.; Scherer, Norbert F.

2017-05-01

Imaging specific regions of interest (ROIs) of nanomaterials or biological samples with different imaging modalities (e.g., light and electron microscopy) or at subsequent time points (e.g., before and after off-microscope procedures) requires relocating the ROIs. Unfortunately, relocation is typically difficult and very time consuming to achieve. Previously developed techniques involve the fabrication of arrays of features, the procedures for which are complex, and the added features can interfere with imaging the ROIs. We report the Fast and Accurate Relocation of Microscopic Experimental Regions (FARMER) method, which only requires determining the coordinates of 3 (or more) conspicuous reference points (REFs) and employs an algorithm based on geometric operators to relocate ROIs in subsequent imaging sessions. The 3 REFs can be quickly added to various regions of a sample using simple tools (e.g., permanent markers or conductive pens) and do not interfere with the ROIs. The coordinates of the REFs and the ROIs are obtained in the first imaging session (on a particular microscope platform) using an accurate and precise encoded motorized stage. In subsequent imaging sessions, the FARMER algorithm finds the new coordinates of the ROIs (on the same or different platforms), using the coordinates of the manually located REFs and the previously recorded coordinates. FARMER is convenient, fast (3-15 min/session, at least 10-fold faster than manual searches), accurate (4.4 μm average error on a microscope with a 100x objective), and precise (almost all errors are <8 μm), even with deliberate rotating and tilting of the sample well beyond normal repositioning accuracy. We demonstrate this versatility by imaging and re-imaging a diverse set of samples and imaging methods: live mammalian cells at different time points; fixed bacterial cells on two microscopes with different imaging modalities; and nanostructures on optical and electron microscopes. FARMER can be readily adapted to any imaging system with an encoded motorized stage and can facilitate multi-session and multi-platform imaging experiments in biology, materials science, photonics, and nanoscience.

Multi-scale image segmentation method with visual saliency constraints and its application

NASA Astrophysics Data System (ADS)

Chen, Yan; Yu, Jie; Sun, Kaimin

2018-03-01

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object-based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over-segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi- scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non- saliency background areas. Experiments show that this method works better for texture image segmentation than traditional multi-scale image segmentation methods, and can enable us to give priority control to the saliency objects of interest. This method has been used in image quality evaluation, scattered residential area extraction, sparse forest extraction and other applications to verify its validation. All applications showed good results.

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.

Faint F Ring and Prometheus

NASA Image and Video Library

2016-11-21

Surface features are visible on Saturn's moon Prometheus in this view from NASA's Cassini spacecraft. Most of Cassini's images of Prometheus are too distant to resolve individual craters, making views like this a rare treat. Saturn's narrow F ring, which makes a diagonal line beginning at top center, appears bright and bold in some Cassini views, but not here. Since the sun is nearly behind Cassini in this image, most of the light hitting the F ring is being scattered away from the camera, making it appear dim. Light-scattering behavior like this is typical of rings comprised of small particles, such as the F ring. This view looks toward the unilluminated side of the rings from about 14 degrees below the ring plane. The image was taken in visible light with the Cassini spacecraft narrow-angle camera on Sept. 24, 2016. The view was acquired at a distance of approximately 226,000 miles (364,000 kilometers) from Prometheus and at a sun-Prometheus-spacecraft, or phase, angle of 51 degrees. Image scale is 1.2 miles (2 kilometers) per pixel. http://photojournal.jpl.nasa.gov/catalog/PIA20508

A robust motion estimation system for minimal invasive laparoscopy

NASA Astrophysics Data System (ADS)

Marcinczak, Jan Marek; von Öhsen, Udo; Grigat, Rolf-Rainer

2012-02-01

Laparoscopy is a reliable imaging method to examine the liver. However, due to the limited field of view, a lot of experience is required from the surgeon to interpret the observed anatomy. Reconstruction of organ surfaces provide valuable additional information to the surgeon for a reliable diagnosis. Without an additional external tracking system the structure can be recovered from feature correspondences between different frames. In laparoscopic images blurred frames, specular reflections and inhomogeneous illumination make feature tracking a challenging task. We propose an ego-motion estimation system for minimal invasive laparoscopy that can cope with specular reflection, inhomogeneous illumination and blurred frames. To obtain robust feature correspondence, the approach combines SIFT and specular reflection segmentation with a multi-frame tracking scheme. The calibrated five-point algorithm is used with the MSAC robust estimator to compute the motion of the endoscope from multi-frame correspondence. The algorithm is evaluated using endoscopic videos of a phantom. The small incisions and the rigid endoscope limit the motion in minimal invasive laparoscopy. These limitations are considered in our evaluation and are used to analyze the accuracy of pose estimation that can be achieved by our approach. The endoscope is moved by a robotic system and the ground truth motion is recorded. The evaluation on typical endoscopic motion gives precise results and demonstrates the practicability of the proposed pose estimation system.

IXPE - The Imaging X-Ray Polarimetry Explorer

NASA Technical Reports Server (NTRS)

Ramsey, Brian

2014-01-01

The Imaging X-ray Polarimetry Explorer (IXPE) is a Small Explorer Mission that will be proposed in response to NASA's upcoming Announcement of Opportunity. IXPE will transform our understanding of the most energetic and exotic astrophysical objects, especially neutron stars and black holes, by measuring the linear polarization of astronomical objects as a function of energy, time and, where relevant, position. As the first dedicated polarimetry observatory IXPE will add a new dimension to the study of cosmic sources, enlarging the observational phase space and providing answers to fundamental questions. IXPE will feature x-ray optics fabricated at NASA/MSFC and gas pixel focal plane detectors provided by team members in Italy (INAF and INFN). This presentation will give an overview of the proposed IXPE mission, detailing the payload configuration, the expected sensitivity, and a typical observing program.

Image search engine with selective filtering and feature-element-based classification

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

Task-specific image partitioning.

PubMed

Kim, Sungwoong; Nowozin, Sebastian; Kohli, Pushmeet; Yoo, Chang D

2013-02-01

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks.

Finger-Vein Image Enhancement Using a Fuzzy-Based Fusion Method with Gabor and Retinex Filtering

PubMed Central

Shin, Kwang Yong; Park, Young Ho; Nguyen, Dat Tien; Park, Kang Ryoung

2014-01-01

Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods. PMID:24549251

Flow Microscopy Imaging Is Sensitive to Characteristics of Subvisible Particles in Peginesatide Formulations Associated With Severe Adverse Reactions.

PubMed

Daniels, Austin L; Randolph, Theodore W

2018-05-01

The presence of subvisible particles in formulations of therapeutic proteins is a risk factor for adverse immune responses. Although the immunogenic potential of particulate contaminants likely depends on particle structural characteristics (e.g., composition, size, and shape), exact structure-immunogenicity relationships are unknown. Images recorded by flow imaging microscopy reflect information about particle morphology, but flow microscopy is typically used to determine only particle size distributions, neglecting information on particle morphological features that may be immunologically relevant. We recently developed computational techniques that utilize the Kullback-Leibler divergence and multidimensional scaling to compare the morphological properties of particles in sets of flow microscopy images. In the current work, we combined these techniques with expectation maximization cluster analyses and used them to compare flow imaging microscopy data sets that had been collected by the U.S. Food and Drug Administration after severe adverse drug reactions (including 7 fatalities) were observed in patients who had been administered some lots of peginesatide formulations. Flow microscopy images of particle populations found in the peginesatide lots associated with severe adverse reactions in patients were readily distinguishable from images of particles in lots where severe adverse reactions did not occur. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Raman imaging of lipid bilayer membrane by surface enhanced Raman scattering

NASA Astrophysics Data System (ADS)

Mori, Motoaki; Abe, Shunsuke; Kondo, Takahiro; Saito, Yuika

2018-04-01

We investigated two-dimensional lipid bilayers by spectroscopic imaging with surface enhanced Raman spectroscopy (SERS). A DSPC lipid bilayer incubated on a glass substrate was coated with a thin layer of silver. Due to the strong electromagnetic enhancement of the silver film and the affinity to lipid molecules, the Raman spectrum of a single bilayer was obtained in a 1 s exposure time with 0.1 mW of incident laser power. In the C-H vibrational region of the spectra, which is sensitive to bilayer configurations, a randomly stacked area was dominated by the CH3 asymmetric-stretch mode, whereas flat areas including double bilayers showed typical SERS spectra. The spectral features of the randomly stacked area are explained by the existence of many free lipid molecules, which is supported by DFT calculations of paired DSPC molecules. Our method can be applied to reveal the local crystallinity of single lipid bilayers, which is difficult to assess by conventional Raman imaging.

Eye on 'Bounce'

NASA Technical Reports Server (NTRS)

2004-01-01

This mosaic, created from four images taken by the Mars Exploration Rover Opportunity's microscopic imager, outlines the target on 'Bounce' rock that the rover's rock abrasion tool will abrade on sol 66.

This 6-centimeter-square (2.4-inch-square) area was chosen by the rock abrasion tool team as the most advantageous area for grinding.

Preliminary results from the rover's miniature thermal emission spectrometer show that Bounce is rich in hematite. Bounce contains spherules, or 'blueberries,' like some rocks in the 'Eagle Crater' outcrop. However, Bounce's spherules appear smaller and may be formed by an entirely different process. The blueberries seen in the outcrop are typically 3 to 4 millimeters (0.12 to 0.16 inch) each. A good example of a cluster of micro-berries can be seen just left of center in this image. Scientists are currently studying all of the rock's features as well as its chemical content. After next sol's grinding operation, the team will be able to compare the rock's exterior and interior chemical compositions.

cisTEM, user-friendly software for single-particle image processing.

PubMed

Grant, Timothy; Rohou, Alexis; Grigorieff, Nikolaus

2018-03-07

We have developed new open-source software called cis TEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cis TEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k - 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cis TEM is available for download from cistem.org. © 2018, Grant et al.

Vision-aided Monitoring and Control of Thermal Spray, Spray Forming, and Welding Processes

NASA Technical Reports Server (NTRS)

Agapakis, John E.; Bolstad, Jon

1993-01-01

Vision is one of the most powerful forms of non-contact sensing for monitoring and control of manufacturing processes. However, processes involving an arc plasma or flame such as welding or thermal spraying pose particularly challenging problems to conventional vision sensing and processing techniques. The arc or plasma is not typically limited to a single spectral region and thus cannot be easily filtered out optically. This paper presents an innovative vision sensing system that uses intense stroboscopic illumination to overpower the arc light and produce a video image that is free of arc light or glare and dedicated image processing and analysis schemes that can enhance the video images or extract features of interest and produce quantitative process measures which can be used for process monitoring and control. Results of two SBIR programs sponsored by NASA and DOE and focusing on the application of this innovative vision sensing and processing technology to thermal spraying and welding process monitoring and control are discussed.

Speaker-independent phoneme recognition with a binaural auditory image model

NASA Astrophysics Data System (ADS)

Francis, Keith Ivan

1997-09-01

This dissertation presents phoneme recognition techniques based on a binaural fusion of outputs of the auditory image model and subsequent azimuth-selective phoneme recognition in a noisy environment. Background information concerning speech variations, phoneme recognition, current binaural fusion techniques and auditory modeling issues is explained. The research is constrained to sources in the frontal azimuthal plane of a simulated listener. A new method based on coincidence detection of neural activity patterns from the auditory image model of Patterson is used for azimuth-selective phoneme recognition. The method is tested in various levels of noise and the results are reported in contrast to binaural fusion methods based on various forms of correlation to demonstrate the potential of coincidence- based binaural phoneme recognition. This method overcomes smearing of fine speech detail typical of correlation based methods. Nevertheless, coincidence is able to measure similarity of left and right inputs and fuse them into useful feature vectors for phoneme recognition in noise.

Near-infrared morphology of protoplanetary nebulae - The icy dust torus of Minkowski's Footprint (M1-92)

NASA Technical Reports Server (NTRS)

Eiroa, C.; Hodapp, K.-W.

1989-01-01

High-resolution near-infrared images and ice-band spectra of the protoplanetary nebula M1-92 (Minkowski's Footprint) are presented. The direct images of the object display a typical bipolar morphology with the star located in the center of the nebula illuminating two lobes. The overall dimensions are the same in the J, H, and K infrared bands, and they are similar to those in the optical range. The near-infrared color images clearly reveal a dust torus around the central star. The orientation of the object in the plane of the sky allows the simultaneous view of the illuminating star, the nebular lobes, and the dust torus in a highly favorable perspective, only rarely found in other bipolar nebulae. The ice-band spectra make it possible to locate the H2O-ice grains within the dust torus; in addition, the narrow ice feature indicates that the ices are primarily pure crystalline water.

a Cloud Boundary Detection Scheme Combined with Aslic and Cnn Using ZY-3, GF-1/2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Guo, Z.; Li, C.; Wang, Z.; Kwok, E.; Wei, X.

2018-04-01

Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN) is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC) method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

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

Ushizima, Daniela; Perciano, Talita; Krishnan, Harinarayan

Fibers provide exceptional strength-to-weight ratio capabilities when woven into ceramic composites, transforming them into materials with exceptional resistance to high temperature, and high strength combined with improved fracture toughness. Microcracks are inevitable when the material is under strain, which can be imaged using synchrotron X-ray computed micro-tomography (mu-CT) for assessment of material mechanical toughness variation. An important part of this analysis is to recognize fibrillar features. This paper presents algorithms for detecting and quantifying composite cracks and fiber breaks from high-resolution image stacks. First, we propose recognition algorithms to identify the different structures of the composite, including matrix cracks andmore » fibers breaks. Second, we introduce our package F3D for fast filtering of large 3D imagery, implemented in OpenCL to take advantage of graphic cards. Results show that our algorithms automatically identify micro-damage and that the GPU-based implementation introduced here takes minutes, being 17x faster than similar tools on a typical image file.« less

cisTEM, user-friendly software for single-particle image processing

PubMed Central

2018-01-01

We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k – 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org. PMID:29513216

Circles and Streaks

NASA Technical Reports Server (NTRS)

2003-01-01

MGS MOC Release No. MOC2-544, 14 November 2003

This Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) image, acquired less than a week ago on 8 November 2003, shows a typical southern middle-to-high latitude scene at this time of year. It is summer in the southern hemisphere, and regions such as Promethei Terra, where this image was acquired, are being streaked by dust devils that remove or disrupt the coating of dust that was deposited over the region in the previous autumn or winter. While no active dust devils were captured in this scene, their tell-tale tracks are scratched all across the image. The circular features are the sites of buried meteor impact craters; their rims form dark rings; the material that fills the craters has become cracked. This picture is located near 68.1oS, 247.9oW. The area shown is approximately 3 km (1.9 mi) across and is illuminated by sunlight from the upper left.

Disappearance of the Propontis Regional Dark Albedo Feature on Mars

NASA Astrophysics Data System (ADS)

Lee, Steven W.; Thomas, P. C.; Cantor, B. A.

2013-10-01

The appearance of Propontis, one of many distinct classical dark albedo features on Mars, has been documented by ground-based observers for well over a century; Propontis was once thought to be the location of a “typical Martian canal”. The roughly circular feature (centered at 38°N, 179°W) covers about 500km in north-south extent. Modern spacecraft observations have shown the northern plains in which Propontis is located to include many subdued craters, knobs, and troughs. Observations by the Mars Color Imager (MARCI) onboard the Mars Reconnaissance Orbiter (MRO) have documented dramatic changes in the Propontis feature during August 2009. Daily MARCI mosaics (spatial resolution of 1 km/pixel) revealed extensive dust storm activity in this region over a ten day period (August 16-25, Ls ~ 322°-327°). At this time, the north polar seasonal ice cap was at maximum extent (reaching southward to about 55°N), and dust storm activity was frequently observed southward of the seasonal cap. These storms apparently led to sufficient deposition of bright dust to effectively “erase” the dark Propontis feature - yielding one of the most significant changes in regional albedo since Mars Global Surveyor began routine global mapping in 1997. Only minor changes have been detected over the course of repeated MARCI observations of this region since late-2009 - Propontis has not yet “recovered” to its previous extent and appearance. MRO is expected to provide ongoing MARCI mapping, enhanced with regular Context Imager (CTX, spatial resolution of 6 m/pixel) monitoring. An overview of the accumulated observations to date will be presented, along with interpretation of the magnitude of sediment transport required to account for the observed changes in Propontis.

Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

NASA Astrophysics Data System (ADS)

Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

2017-01-01

We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

Textural features for radar image analysis

NASA Technical Reports Server (NTRS)

Shanmugan, K. S.; Narayanan, V.; Frost, V. S.; Stiles, J. A.; Holtzman, J. C.

1981-01-01

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

A Study on Clinical Characteristics and Magnetic Resonance Imaging Manifestations on Systemic Rosai-Dorfman Disease

PubMed Central

Cheng, Xiao; Cheng, Jing-Liang; Gao, An-Kang

2018-01-01

Background: Rosai-Dorfman disease (RDD) is typically characterized by painless bilateral and symmetrical cervical lymphadenopathy, with associated fever and leukocytosis. The aim of the current study was to summarize the clinical features and imaging characteristics of RDD, in an effort to improve its diagnostic accuracy. Methods: The study was analyzed from 32 patients between January 2011 and December 2017; of these, 16 patients had pathologically diagnosed RDD, eight had pathologically diagnosed meningioma, and eight pathologically diagnosed lymphoma. All patients underwent computed tomography and magnetic resonance imaging (MRI). Clinical features and imaging characteristics of RDD were analyzed retrospectively. The mean apparent diffusion coefficient (ADC) values of lesions at different sites were measured, and one-way analysis of variance and the least significant difference t-test were used to compare the differences between groups and draw receiver operating characteristic curves. The tumors were excised for biopsy and analyzed using immunohistochemistry. Results: The mean ADCs were (0.81 ± 0.10) × 10−3 mm2/s for intercranial RDD, (0.73 ± 0.05) × 10−3 mm2/s for nasopharyngeal RDD, (0.74 ± 0.11) × 10−3 mm2/s for bone RDD, and (0.71 ± 0.04) × 10−3 mm2/s for soft-tissue RDD. The optimum ADC to distinguish intracranial RDD from lymphoma was 0.79 × 10−3 mm2/s (62.5% sensitivity and 100% specificity) and to distinguish meningioma from intracranial RDD was 0.92 × 10−3 mm2/s (62.5% sensitivity and 100% specificity). Levels of C-reactive protein, erythrocyte sediment rate and D-dimer were significantly elevated (81%, 87%, and 75%, respectively). On immunohistochemistry, RDD was positive for both S-100 and CD68 proteins but negative for CD1a. Conclusions: Conventional MRI, combined with diffusion-weighted imaging and ADC mapping, is an important diagnostic tool in evaluating RDD patients. An accurate diagnosis of RDD should consider the clinical features, imaging characteristics, and the pathological findings. PMID:29451149

Image Harvest: an open-source platform for high-throughput plant image processing and analysis.

PubMed

Knecht, Avi C; Campbell, Malachy T; Caprez, Adam; Swanson, David R; Walia, Harkamal

2016-05-01

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Learning to rank for blind image quality assessment.

PubMed

Gao, Fei; Tao, Dacheng; Gao, Xinbo; Li, Xuelong

2015-10-01

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as the quality of image Ia is better than that of image Ib for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at a very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves a performance comparable with that of state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

Neural Tuning to Low-Level Features of Speech throughout the Perisylvian Cortex.

PubMed

Berezutskaya, Julia; Freudenburg, Zachary V; Güçlü, Umut; van Gerven, Marcel A J; Ramsey, Nick F

2017-08-16

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain. SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the perisylvian cortex and potentially contribute to the emergence of "coarse" speech representations in inferior frontal gyrus typically associated with high-level language processing. These findings add to the previous work on auditory processing and underline a distinctive role of inferior frontal gyrus in natural speech comprehension. Copyright © 2017 the authors 0270-6474/17/377906-15$15.00/0.

Variable Use of Features Associated with African American English by Typically Developing Children

ERIC Educational Resources Information Center

Jackson, Janice E.; Pearson, Barbara Zurer

2010-01-01

Purpose: The well-known decline in the use of African American English (AAE) features by groups of school-aged AAE-speaking children was reexamined for patterns of overt-, zero-, and mixed-marking for individual features and individual speakers. Methods: Seven hundred twenty-nine typically developing children between the ages of 4 and 12--511…

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

PubMed Central

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-01-01

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

The role of external features in face recognition with central vision loss: A pilot study

PubMed Central

Bernard, Jean-Baptiste; Chung, Susana T.L.

2016-01-01

Purpose We evaluated how the performance for recognizing familiar face images depends on the internal (eyebrows, eyes, nose, mouth) and external face features (chin, outline of face, hairline) in individuals with central vision loss. Methods In Experiment 1, we measured eye movements for four observers with central vision loss to determine whether they fixated more often on the internal or the external features of face images while attempting to recognize the images. We then measured the accuracy for recognizing face images that contained only the internal, only the external, or both internal and external features (Experiment 2), and for hybrid images where the internal and external features came from two different source images (Experiment 3), for five observers with central vision loss and four age-matched control observers. Results When recognizing familiar face images, approximately 40% of the fixations of observers with central vision loss were centered on the external features of faces. The recognition accuracy was higher for images containing only external features (66.8±3.3% correct) than for images containing only internal features (35.8±15.0%), a finding contradicting that of control observers. For hybrid face images, observers with central vision loss responded more accurately to the external features (50.4±17.8%) than to the internal features (9.3±4.9%), while control observers did not show the same bias toward responding to the external features. Conclusions Contrary to people with normal vision who rely more on the internal features of face images for recognizing familiar faces, individuals with central vision loss show a higher dependence on using external features of face images. PMID:26829260

The Role of External Features in Face Recognition with Central Vision Loss.

PubMed

Bernard, Jean-Baptiste; Chung, Susana T L

2016-05-01

We evaluated how the performance of recognizing familiar face images depends on the internal (eyebrows, eyes, nose, mouth) and external face features (chin, outline of face, hairline) in individuals with central vision loss. In experiment 1, we measured eye movements for four observers with central vision loss to determine whether they fixated more often on the internal or the external features of face images while attempting to recognize the images. We then measured the accuracy for recognizing face images that contained only the internal, only the external, or both internal and external features (experiment 2) and for hybrid images where the internal and external features came from two different source images (experiment 3) for five observers with central vision loss and four age-matched control observers. When recognizing familiar face images, approximately 40% of the fixations of observers with central vision loss was centered on the external features of faces. The recognition accuracy was higher for images containing only external features (66.8 ± 3.3% correct) than for images containing only internal features (35.8 ± 15.0%), a finding contradicting that of control observers. For hybrid face images, observers with central vision loss responded more accurately to the external features (50.4 ± 17.8%) than to the internal features (9.3 ± 4.9%), whereas control observers did not show the same bias toward responding to the external features. Contrary to people with normal vision who rely more on the internal features of face images for recognizing familiar faces, individuals with central vision loss show a higher dependence on using external features of face images.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

PubMed

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-02-26

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Prenatal diagnosis of Joubert syndrome

PubMed Central

Zhu, Lingling; Xie, Limei

2017-01-01

Abstract Introduction: Joubert syndrome (JS) is a rare autosomal recessive inherited disease belonging to ciliopathy with the causative mutation of genes. Except for X-linked inheritance, the high recurrence rate of a family is about 25%. After birth, it may cause a series of neurological symptoms, even with retina, kidney, liver, and other organ abnormalities, which is defined as Joubert syndrome and related disorders (JSRD). Molecular genetics research contributes to disease prediction and genetic counseling. Prenatal diagnosis is rare. Magnetic resonance imaging (MRI) is usually the first-choice diagnostic modality with typical brain images characterized by the molar tooth sign. We describe a case of JS prenatally and Dandy-Walker malformation for the differential diagnosis based on ultrasonograms. We also review the etiology, imaging features, clinical symptoms, and diagnosis of JSRD. Case presentation: A 22-year-old woman was pregnant at 27 1/7 weeks’ gestation with fetal cerebellar vermis hypoplasia. Fetal ultrasonography and MRI confirmed a diagnosis of JS at our center. The couple finally opted to terminate the fetus, which had a normal appearance and growth parameters. The couple also had an AHI1 gene mutation on chromosome 6. Conclusions: Currently, a diagnosis of JS is commonly made after birth. Fewer cases of prenatal diagnosis by ultrasonography have been made, and they are more liable to be misdirected because of some nonspecial features that also manifest in Dandy-Walker malformation, cranio-cerebello-cardiac syndrome, and so on. PMID:29390414

Fluorescence Imaging Study of Impinging Underexpanded Jets

NASA Technical Reports Server (NTRS)

Inman, Jennifer A.; Danehy, Paul M.; Nowak, Robert J.; Alderfer, David W.

2008-01-01

An experiment was designed to create a simplified simulation of the flow through a hole in the surface of a hypersonic aerospace vehicle and the subsequent impingement of the flow on internal structures. In addition to planar laser-induced fluorescence (PLIF) flow visualization, pressure measurements were recorded on the surface of an impingement target. The PLIF images themselves provide quantitative spatial information about structure of the impinging jets. The images also help in the interpretation of impingement surface pressure profiles by highlighting the flow structures corresponding to distinctive features of these pressure profiles. The shape of the pressure distribution along the impingement surface was found to be double-peaked in cases with a sufficiently high jet-exit-to-ambient pressure ratio so as to have a Mach disk, as well as in cases where a flow feature called a recirculation bubble formed at the impingement surface. The formation of a recirculation bubble was in turn found to depend very sensitively upon the jet-exit-to-ambient pressure ratio. The pressure measured at the surface was typically less than half the nozzle plenum pressure at low jet pressure ratios and decreased with increasing jet pressure ratios. Angled impingement cases showed that impingement at a 60deg angle resulted in up to a factor of three increase in maximum pressure at the plate compared to normal incidence.

A simplified Suomi NPP VIIRS dust detection algorithm

NASA Astrophysics Data System (ADS)

Yang, Yikun; Sun, Lin; Zhu, Jinshan; Wei, Jing; Su, Qinghua; Sun, Wenxiao; Liu, Fangwei; Shu, Meiyan

2017-11-01

Due to the complex characteristics of dust and sparse ground-based monitoring stations, dust monitoring is facing severe challenges, especially in dust storm-prone areas. Aim at constructing a high-precision dust storm detection model, a pixel database, consisted of dusts over a variety of typical feature types such as cloud, vegetation, Gobi and ice/snow, was constructed, and their distributions of reflectance and Brightness Temperatures (BT) were analysed, based on which, a new Simplified Dust Detection Algorithm (SDDA) for the Suomi National Polar-Orbiting Partnership Visible infrared Imaging Radiometer (NPP VIIRS) is proposed. NPP VIIRS images covering the northern China and Mongolian regions, where features serious dust storms, were selected to perform the dust detection experiments. The monitoring results were compared with the true colour composite images, and results showed that most of the dust areas can be accurately detected, except for fragmented thin dusts over bright surfaces. The dust ground-based measurements obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) and the Ozone Monitoring Instrument Aerosol Index (OMI AI) products were selected for comparison purposes. Results showed that the dust monitoring results agreed well in the spatial distribution with OMI AI dust products and the MICAPS ground-measured data with an average high accuracy of 83.10%. The SDDA is relatively robust and can realize automatic monitoring for dust storms.

Age-related morphological changes of the dermal matrix in human skin documented in vivo by multiphoton microscopy

NASA Astrophysics Data System (ADS)

Wang, Hequn; Shyr, Thomas; Fevola, Michael J.; Cula, Gabriela Oana; Stamatas, Georgios N.

2018-03-01

Two-photon fluorescence (TPF) and second harmonic generation (SHG) microscopy provide direct visualization of the skin dermal fibers in vivo. A typical method for analyzing TPF/SHG images involves averaging the image intensity and therefore disregarding the spatial distribution information. The goal of this study is to develop an algorithm to document age-related effects of the dermal matrix. TPF and SHG images were acquired from the upper inner arm, volar forearm, and cheek of female volunteers of two age groups: 20 to 30 and 60 to 80 years of age. The acquired images were analyzed for parameters relating to collagen and elastin fiber features, such as orientation and density. Both collagen and elastin fibers showed higher anisotropy in fiber orientation for the older group. The greatest difference in elastin fiber anisotropy between the two groups was found for the upper inner arm site. Elastin fiber density increased with age, whereas collagen fiber density decreased with age. The proposed analysis considers the spatial information inherent to the TPF and SHG images and provides additional insights into how the dermal fiber structure is affected by the aging process.

Identifying image preferences based on demographic attributes

NASA Astrophysics Data System (ADS)

Fedorovskaya, Elena A.; Lawrence, Daniel R.

2014-02-01

The intent of this study is to determine what sorts of images are considered more interesting by which demographic groups. Specifically, we attempt to identify images whose interestingness ratings are influenced by the demographic attribute of the viewer's gender. To that end, we use the data from an experiment where 18 participants (9 women and 9 men) rated several hundred images based on "visual interest" or preferences in viewing images. The images were selected to represent the consumer "photo-space" - typical categories of subject matter found in consumer photo collections. They were annotated using perceptual and semantic descriptors. In analyzing the image interestingness ratings, we apply a multivariate procedure known as forced classification, a feature of dual scaling, a discrete analogue of principal components analysis (similar to correspondence analysis). This particular analysis of ratings (i.e., ordered-choice or Likert) data enables the investigator to emphasize the effect of a specific item or collection of items. We focus on the influence of the demographic item of gender on the analysis, so that the solutions are essentially confined to subspaces spanned by the emphasized item. Using this technique, we can know definitively which images' ratings have been influenced by the demographic item of choice. Subsequently, images can be evaluated and linked, on one hand, to their perceptual and semantic descriptors, and, on the other hand, to the preferences associated with viewers' demographic attributes.

Transfer learning for visual categorization: a survey.

PubMed

Shao, Ling; Zhu, Fan; Li, Xuelong

2015-05-01

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Reference Images from Thin Sections of Lunar Regolith

NASA Technical Reports Server (NTRS)

Rickman Doug; Edmunson, Jennifer

2013-01-01

The specialist literature about the lunar regolith is massive. It is also highly focused on specific topics and effectively impenetrable to most non-geologists. Both characteristics of the literature present substantial hurdles to scientists and engineers interested in the regolith In the author's experience it neither surprising or unusual to find serious misconceptions about lunar-type materials outside of the lunar research community. Education of professionals who are non-geologists but interested in the regolith is impeded by a lack of some basic resources. One asset that has been missing is simply detailed images of the regolith "soil". While a few websites offer imagery of specific features, these are of course selected to illustrate specific features. It is almost impossible for a non-specialist to reason from these what "normal" or "typical" regolith looks like. Further, access to lunar material is highly restricted. And as publications rarely do not provide other than highly focused and narrowly tailored data, there is little potential for workers without personal access to sample to do any work with lunar material. To address both problems the authors have begun to make high resolution optical micrographs of entire thin sections of lunar regolith.