Sources of Disconnection in Neurocognitive Aging: Cerebral White Matter Integrity, Resting-state Functional Connectivity, and White Matter Hyperintensity Volume

PubMed Central

Madden, David J.; Parks, Emily L.; Tallman, Catherine W.; Boylan, Maria A.; Hoagey, David A.; Cocjin, Sally B.; Packard, Lauren E.; Johnson, Micah A.; Chou, Ying-hui; Potter, Guy G.; Chen, Nan-kuei; Siciliano, Rachel E.; Monge, Zachary A.; Honig, Jesse A.; Diaz, Michele T.

2017-01-01

Age-related decline in fluid cognition can be characterized as a disconnection among specific brain structures, leading to a decline in functional efficiency. The potential sources of disconnection, however, are unclear. We investigated imaging measures of cerebral white matter integrity, resting-state functional connectivity, and white matter hyperintensity (WMH) volume as mediators of the relation between age and fluid cognition, in 145 healthy, community-dwelling adults 19–79 years of age. At a general level of analysis, with a single composite measure of fluid cognition and single measures of each of the three imaging modalities, age exhibited an independent influence on the cognitive and imaging measures, and the imaging variables did not mediate the age-cognition relation. At a more specific level of analysis, resting-state functional connectivity of sensorimotor networks was a significant mediator of the age-related decline in executive function. These findings suggest that different levels of analysis lead to different models of neurocognitive disconnection, and that resting-state functional connectivity, in particular, may contribute to age-related decline in executive function. PMID:28389085

Predictive assessment of kidney functional recovery following ischemic injury using optical spectroscopy

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

Raman, Rajesh N.; Pivetti, Christopher D.; Ramsamooj, Rajendra

Functional changes in rat kidneys during the induced ischemic injury and recovery phases were explored using multimodal autofluorescence and light scattering imaging. We aim to evaluate the use of noncontact optical signatures for rapid assessment of tissue function and viability. Specifically, autofluorescence images were acquired in vivo under 355, 325, and 266 nm illumination while light scattering images were collected at the excitation wavelengths as well as using relatively narrowband light centered at 500 nm. The images were simultaneously recorded using a multimodal optical imaging system. We also analyzed to obtain time constants, which were correlated to kidney dysfunction asmore » determined by a subsequent survival study and histopathological analysis. This analysis of both the light scattering and autofluorescence images suggests that changes in tissue microstructure, fluorophore emission, and blood absorption spectral characteristics, coupled with vascular response, contribute to the behavior of the observed signal, which may be used to obtain tissue functional information and offer the ability to predict posttransplant kidney function.« less

Predictive assessment of kidney functional recovery following ischemic injury using optical spectroscopy

DOE PAGES

Raman, Rajesh N.; Pivetti, Christopher D.; Ramsamooj, Rajendra; ...

2017-05-03

Functional changes in rat kidneys during the induced ischemic injury and recovery phases were explored using multimodal autofluorescence and light scattering imaging. We aim to evaluate the use of noncontact optical signatures for rapid assessment of tissue function and viability. Specifically, autofluorescence images were acquired in vivo under 355, 325, and 266 nm illumination while light scattering images were collected at the excitation wavelengths as well as using relatively narrowband light centered at 500 nm. The images were simultaneously recorded using a multimodal optical imaging system. We also analyzed to obtain time constants, which were correlated to kidney dysfunction asmore » determined by a subsequent survival study and histopathological analysis. This analysis of both the light scattering and autofluorescence images suggests that changes in tissue microstructure, fluorophore emission, and blood absorption spectral characteristics, coupled with vascular response, contribute to the behavior of the observed signal, which may be used to obtain tissue functional information and offer the ability to predict posttransplant kidney function.« less

Incorporating Functional Imaging Information into rpFNA Analysis for Breast Cancer Detection in High Risk Women

DTIC Science & Technology

2010-03-01

TITLE: INCORPORATING FUNCTIONAL IMAGING INFORMATION TO rpFNA ANALYSIS FOR BREAST CANCER DETECTION IN HIGH-RISK WOMEN PRINCIPAL INVESTIGATOR...Imaging Information into rpFNA 5a. CONTRACT NUMBER Analysis for Breast Cancer Detection in High Risk Women 5b. GRANT NUMBER W81XWH-08-1-0192 5c...results of random periareolar fine needle aspiration (rpFNA) in women at high risk for breast cancer. In this second year of work, efforts have been

Advanced Connectivity Analysis (ACA): a Large Scale Functional Connectivity Data Mining Environment.

PubMed

Chen, Rong; Nixon, Erika; Herskovits, Edward

2016-04-01

Using resting-state functional magnetic resonance imaging (rs-fMRI) to study functional connectivity is of great importance to understand normal development and function as well as a host of neurological and psychiatric disorders. Seed-based analysis is one of the most widely used rs-fMRI analysis methods. Here we describe a freely available large scale functional connectivity data mining software package called Advanced Connectivity Analysis (ACA). ACA enables large-scale seed-based analysis and brain-behavior analysis. It can seamlessly examine a large number of seed regions with minimal user input. ACA has a brain-behavior analysis component to delineate associations among imaging biomarkers and one or more behavioral variables. We demonstrate applications of ACA to rs-fMRI data sets from a study of autism.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks.

PubMed

Smitha, K A; Akhil Raja, K; Arun, K M; Rajesh, P G; Thomas, Bejoy; Kapilamoorthy, T R; Kesavadas, Chandrasekharan

2017-08-01

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Functional optical coherence tomography for live dynamic analysis of mouse embryonic cardiogenesis

NASA Astrophysics Data System (ADS)

Wang, Shang; Lopez, Andrew L.; Larina, Irina V.

2018-02-01

Blood flow, heart contraction, and tissue stiffness are important regulators of cardiac morphogenesis and function during embryonic development. Defining how these factors are integrated is critically important to advance prevention, diagnostics, and treatment of congenital heart defects. Mammalian embryonic development is taking place deep within the female body, which makes cardiodynamic imaging and analysis during early developmental stages in humans inaccessible. With thousands of mutant lines available and well-established genetic manipulation tools, mouse is a great model to understand how biomechanical factors are integrated with molecular pathways to regulate cardiac function and development. Dynamic imaging and quantitative analysis of the biomechanics of live mouse embryos have become increasingly important, which demands continuous advancements in imaging techniques and live assessment approaches. This has been one of the major drives to keep pushing the frontier of embryonic imaging for better resolution, higher speed, deeper penetration, and more diverse and effective contrasts. Optical coherence tomography (OCT) has played a significant role in addressing such demands, and its features in non-labeling imaging, 3D capability, a large working distance, and various functional derivatives allow OCT to cover a number of specific applications in embryonic imaging. Recently, our group has made several technical improvements in using OCT to probe the biomechanical aspects of live developing mouse embryos at early stages. These include the direct volumetric structural and functional imaging of the cardiodynamics, four-dimensional quantitative Doppler imaging and analysis of the cardiac blood flow, and fourdimensional blood flow separation from the cardiac wall tissue in the beating embryonic heart. Here, we present a short review of these studies together with brief descriptions of the previous work that demonstrate OCT as a valuable and useful imaging tool for the research in developmental cardiology.

Echocardiographic Evaluation of Left Atrial Mechanics: Function, History, Novel Techniques, Advantages, and Pitfalls.

PubMed

Leischik, Roman; Littwitz, Henning; Dworrak, Birgit; Garg, Pankaj; Zhu, Meihua; Sahn, David J; Horlitz, Marc

2015-01-01

Left atrial (LA) functional analysis has an established role in assessing left ventricular diastolic function. The current standard echocardiographic parameters used to study left ventricular diastolic function include pulsed-wave Doppler mitral inflow analysis, tissue Doppler imaging measurements, and LA dimension estimation. However, the above-mentioned parameters do not directly quantify LA performance. Deformation studies using strain and strain-rate imaging to assess LA function were validated in previous research, but this technique is not currently used in routine clinical practice. This review discusses the history, importance, and pitfalls of strain technology for the analysis of LA mechanics.

Is functional MR imaging assessment of hemispheric language dominance as good as the Wada test?: a meta-analysis.

PubMed

Dym, R Joshua; Burns, Judah; Freeman, Katherine; Lipton, Michael L

2011-11-01

To perform a systematic review and meta-analysis to quantitatively assess functional magnetic resonance (MR) imaging lateralization of language function in comparison with the Wada test. This study was determined to be exempt from review by the institutional review board. A systematic review and meta-analysis were performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A structured Medline search was conducted to identify all studies that compared functional MR imaging with the Wada test for determining hemispheric language dominance prior to brain surgery. Studies meeting predetermined inclusion criteria were selected independently by two radiologists who also assessed their quality using the Quality Assessment of Diagnostic Accuracy Studies tool. Language dominance was classified as typical (left hemispheric language dominance) or atypical (right hemispheric language dominance or bilateral language representation) for each patient. A meta-analysis was then performed by using a bivariate random-effects model to derive estimates of sensitivity and specificity, with Wada as the standard of reference. Subgroup analyses were also performed to compare the different functional MR imaging techniques utilized by the studies. Twenty-three studies, comprising 442 patients, met inclusion criteria. The sensitivity and specificity of functional MR imaging for atypical language dominance (compared with the Wada test) were 83.5% (95% confidence interval: 80.2%, 86.7%) and 88.1% (95% confidence interval: 87.0%, 89.2%), respectively. Functional MR imaging provides an excellent, noninvasive alternative for language lateralization and should be considered for the initial preoperative assessment of hemispheric language dominance. Further research may help determine which functional MR methods are most accurate for specific patient populations. RSNA, 2011

An advanced software suite for the processing and analysis of silicon luminescence images

NASA Astrophysics Data System (ADS)

Payne, D. N. R.; Vargas, C.; Hameiri, Z.; Wenham, S. R.; Bagnall, D. M.

2017-06-01

Luminescence imaging is a versatile characterisation technique used for a broad range of research and industrial applications, particularly for the field of photovoltaics where photoluminescence and electroluminescence imaging is routinely carried out for materials analysis and quality control. Luminescence imaging can reveal a wealth of material information, as detailed in extensive literature, yet these techniques are often only used qualitatively instead of being utilised to their full potential. Part of the reason for this is the time and effort required for image processing and analysis in order to convert image data to more meaningful results. In this work, a custom built, Matlab based software suite is presented which aims to dramatically simplify luminescence image processing and analysis. The suite includes four individual programs which can be used in isolation or in conjunction to achieve a broad array of functionality, including but not limited to, point spread function determination and deconvolution, automated sample extraction, image alignment and comparison, minority carrier lifetime calibration and iron impurity concentration mapping.

More than looking good: impact on quality of life moderates the relationship between functional body image and physical activity in men with SCI.

PubMed

Bassett, R L; Martin Ginis, K A

2009-03-01

Cross-sectional. To examine the relationship between body image and leisure time physical activity (LTPA) among men with spinal cord injury (SCI). Specifically, to examine the moderating function of the perceived impact of body image on quality of life (QOL). Ontario, Canada. Men with SCI (N=50, 50% paraplegic) reported, (a) their functional and appearance body image (Adult Body Satisfaction Questionnaire), (b) their perceived impact of body image on QOL and (c) LTPA performed over the previous 3 days. Body image was in the 'normal' range compared with the general population. Linear regression analysis found a significant LTPA x body image impact on QOL interaction beta=0.39, P<0.05. Post hoc analysis showed that among individuals who reported a negative effect of body image on QOL, those who engaged in LTPA were less satisfied with their physical function than those who did not. For those who did not perceive their body image to negatively impact their QOL, there was generally no difference in functional body image between those who engaged in LTPA and those who did not. Appearance body image is not related to LTPA for men with SCI. It has been suggested that body dissatisfaction may motivate some individuals to engage in LTPA. However, for men living with SCI, functional body image may be associated with LTPA only when a negative effect on QOL is perceived. Future research should consider the moderating function of the perceived impact of body image on QOL when examining the relationship between LTPA and body image among men living with SCI.

Subband/Transform MATLAB Functions For Processing Images

NASA Technical Reports Server (NTRS)

Glover, D.

1995-01-01

SUBTRANS software is package of routines implementing image-data-processing functions for use with MATLAB*(TM) software. Provides capability to transform image data with block transforms and to produce spatial-frequency subbands of transformed data. Functions cascaded to provide further decomposition into more subbands. Also used in image-data-compression systems. For example, transforms used to prepare data for lossy compression. Written for use in MATLAB mathematical-analysis environment.

Dual wavelength imaging allows analysis of membrane fusion of influenza virus inside cells.

PubMed

Sakai, Tatsuya; Ohuchi, Masanobu; Imai, Masaki; Mizuno, Takafumi; Kawasaki, Kazunori; Kuroda, Kazumichi; Yamashina, Shohei

2006-02-01

Influenza virus hemagglutinin (HA) is a determinant of virus infectivity. Therefore, it is important to determine whether HA of a new influenza virus, which can potentially cause pandemics, is functional against human cells. The novel imaging technique reported here allows rapid analysis of HA function by visualizing viral fusion inside cells. This imaging was designed to detect fusion changing the spectrum of the fluorescence-labeled virus. Using this imaging, we detected the fusion between a virus and a very small endosome that could not be detected previously, indicating that the imaging allows highly sensitive detection of viral fusion.

IDIMS/GEOPAK: Users manual for a geophysical data display and analysis system

NASA Technical Reports Server (NTRS)

Libert, J. M.

1982-01-01

The application of an existing image analysis system to the display and analysis of geophysical data is described, the potential for expanding the capabilities of such a system toward more advanced computer analytic and modeling functions is investigated. The major features of the IDIMS (Interactive Display and Image Manipulation System) and its applicability for image type analysis of geophysical data are described. Development of a basic geophysical data processing system to permit the image representation, coloring, interdisplay and comparison of geophysical data sets using existing IDIMS functions and to provide for the production of hard copies of processed images was described. An instruction manual and documentation for the GEOPAK subsystem was produced. A training course for personnel in the use of the IDIMS/GEOPAK was conducted. The effectiveness of the current IDIMS/GEOPAK system for geophysical data analysis was evaluated.

Using MATLAB software with Tomcat server and Java platform for remote image analysis in pathology.

PubMed

Markiewicz, Tomasz

2011-03-30

The Matlab software is a one of the most advanced development tool for application in engineering practice. From our point of view the most important is the image processing toolbox, offering many built-in functions, including mathematical morphology, and implementation of a many artificial neural networks as AI. It is very popular platform for creation of the specialized program for image analysis, also in pathology. Based on the latest version of Matlab Builder Java toolbox, it is possible to create the software, serving as a remote system for image analysis in pathology via internet communication. The internet platform can be realized based on Java Servlet Pages with Tomcat server as servlet container. In presented software implementation we propose remote image analysis realized by Matlab algorithms. These algorithms can be compiled to executable jar file with the help of Matlab Builder Java toolbox. The Matlab function must be declared with the set of input data, output structure with numerical results and Matlab web figure. Any function prepared in that manner can be used as a Java function in Java Servlet Pages (JSP). The graphical user interface providing the input data and displaying the results (also in graphical form) must be implemented in JSP. Additionally the data storage to database can be implemented within algorithm written in Matlab with the help of Matlab Database Toolbox directly with the image processing. The complete JSP page can be run by Tomcat server. The proposed tool for remote image analysis was tested on the Computerized Analysis of Medical Images (CAMI) software developed by author. The user provides image and case information (diagnosis, staining, image parameter etc.). When analysis is initialized, input data with image are sent to servlet on Tomcat. When analysis is done, client obtains the graphical results as an image with marked recognized cells and also the quantitative output. Additionally, the results are stored in a server database. The internet platform was tested on PC Intel Core2 Duo T9600 2.8 GHz 4 GB RAM server with 768x576 pixel size, 1.28 Mb tiff format images reffering to meningioma tumour (x400, Ki-67/MIB-1). The time consumption was as following: at analysis by CAMI, locally on a server - 3.5 seconds, at remote analysis - 26 seconds, from which 22 seconds were used for data transfer via internet connection. At jpg format image (102 Kb) the consumption time was reduced to 14 seconds. The results have confirmed that designed remote platform can be useful for pathology image analysis. The time consumption is depended mainly on the image size and speed of the internet connections. The presented implementation can be used for many types of analysis at different staining, tissue, morphometry approaches, etc. The significant problem is the implementation of the JSP page in the multithread form, that can be used parallelly by many users. The presented platform for image analysis in pathology can be especially useful for small laboratory without its own image analysis system.

Using MATLAB software with Tomcat server and Java platform for remote image analysis in pathology

PubMed Central

2011-01-01

Background The Matlab software is a one of the most advanced development tool for application in engineering practice. From our point of view the most important is the image processing toolbox, offering many built-in functions, including mathematical morphology, and implementation of a many artificial neural networks as AI. It is very popular platform for creation of the specialized program for image analysis, also in pathology. Based on the latest version of Matlab Builder Java toolbox, it is possible to create the software, serving as a remote system for image analysis in pathology via internet communication. The internet platform can be realized based on Java Servlet Pages with Tomcat server as servlet container. Methods In presented software implementation we propose remote image analysis realized by Matlab algorithms. These algorithms can be compiled to executable jar file with the help of Matlab Builder Java toolbox. The Matlab function must be declared with the set of input data, output structure with numerical results and Matlab web figure. Any function prepared in that manner can be used as a Java function in Java Servlet Pages (JSP). The graphical user interface providing the input data and displaying the results (also in graphical form) must be implemented in JSP. Additionally the data storage to database can be implemented within algorithm written in Matlab with the help of Matlab Database Toolbox directly with the image processing. The complete JSP page can be run by Tomcat server. Results The proposed tool for remote image analysis was tested on the Computerized Analysis of Medical Images (CAMI) software developed by author. The user provides image and case information (diagnosis, staining, image parameter etc.). When analysis is initialized, input data with image are sent to servlet on Tomcat. When analysis is done, client obtains the graphical results as an image with marked recognized cells and also the quantitative output. Additionally, the results are stored in a server database. The internet platform was tested on PC Intel Core2 Duo T9600 2.8GHz 4GB RAM server with 768x576 pixel size, 1.28Mb tiff format images reffering to meningioma tumour (x400, Ki-67/MIB-1). The time consumption was as following: at analysis by CAMI, locally on a server – 3.5 seconds, at remote analysis – 26 seconds, from which 22 seconds were used for data transfer via internet connection. At jpg format image (102 Kb) the consumption time was reduced to 14 seconds. Conclusions The results have confirmed that designed remote platform can be useful for pathology image analysis. The time consumption is depended mainly on the image size and speed of the internet connections. The presented implementation can be used for many types of analysis at different staining, tissue, morphometry approaches, etc. The significant problem is the implementation of the JSP page in the multithread form, that can be used parallelly by many users. The presented platform for image analysis in pathology can be especially useful for small laboratory without its own image analysis system. PMID:21489188

Application of abstract harmonic analysis to the high-speed recognition of images

NASA Technical Reports Server (NTRS)

Usikov, D. A.

1979-01-01

Methods are constructed for rapidly computing correlation functions using the theory of abstract harmonic analysis. The theory developed includes as a particular case the familiar Fourier transform method for a correlation function which makes it possible to find images which are independent of their translation in the plane. Two examples of the application of the general theory described are the search for images, independent of their rotation and scale, and the search for images which are independent of their translations and rotations in the plane.

Digital Imaging

NASA Technical Reports Server (NTRS)

1986-01-01

Digital Imaging is the computer processed numerical representation of physical images. Enhancement of images results in easier interpretation. Quantitative digital image analysis by Perceptive Scientific Instruments, locates objects within an image and measures them to extract quantitative information. Applications are CAT scanners, radiography, microscopy in medicine as well as various industrial and manufacturing uses. The PSICOM 327 performs all digital image analysis functions. It is based on Jet Propulsion Laboratory technology, is accurate and cost efficient.

[Image processing applying in analysis of motion features of cultured cardiac myocyte in rat].

PubMed

Teng, Qizhi; He, Xiaohai; Luo, Daisheng; Wang, Zhengrong; Zhou, Beiyi; Yuan, Zhirun; Tao, Dachang

2007-02-01

Study of mechanism of medicine actions, by quantitative analysis of cultured cardiac myocyte, is one of the cutting edge researches in myocyte dynamics and molecular biology. The characteristics of cardiac myocyte auto-beating without external stimulation make the research sense. Research of the morphology and cardiac myocyte motion using image analysis can reveal the fundamental mechanism of medical actions, increase the accuracy of medicine filtering, and design the optimal formula of medicine for best medical treatments. A system of hardware and software has been built with complete sets of functions including living cardiac myocyte image acquisition, image processing, motion image analysis, and image recognition. In this paper, theories and approaches are introduced for analysis of living cardiac myocyte motion images and implementing quantitative analysis of cardiac myocyte features. A motion estimation algorithm is used for motion vector detection of particular points and amplitude and frequency detection of a cardiac myocyte. Beatings of cardiac myocytes are sometimes very small. In such case, it is difficult to detect the motion vectors from the particular points in a time sequence of images. For this reason, an image correlation theory is employed to detect the beating frequencies. Active contour algorithm in terms of energy function is proposed to approximate the boundary and detect the changes of edge of myocyte.

A Model-Based Approach for Microvasculature Structure Distortion Correction in Two-Photon Fluorescence Microscopy Images

PubMed Central

Dao, Lam; Glancy, Brian; Lucotte, Bertrand; Chang, Lin-Ching; Balaban, Robert S; Hsu, Li-Yueh

2015-01-01

SUMMARY This paper investigates a post-processing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modeling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to sub volumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images. PMID:26224257

AUTOMATED ANALYSIS OF QUANTITATIVE IMAGE DATA USING ISOMORPHIC FUNCTIONAL MIXED MODELS, WITH APPLICATION TO PROTEOMICS DATA.

PubMed

Morris, Jeffrey S; Baladandayuthapani, Veerabhadran; Herrick, Richard C; Sanna, Pietro; Gutstein, Howard

2011-01-01

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method frequently correspond to subregions of visible spots that may represent post-translational modifications or co-migrating proteins that cannot be visually resolved from adjacent, more abundant proteins on the gel image. Thus, it is possible that this image-based approach may actually improve the realized resolution of the gel, revealing differentially expressed proteins that would not have even been detected as spots by modern spot-based analyses.

The Pan-STARRS PS1 Image Processing Pipeline

NASA Astrophysics Data System (ADS)

Magnier, E.

The Pan-STARRS PS1 Image Processing Pipeline (IPP) performs the image processing and data analysis tasks needed to enable the scientific use of the images obtained by the Pan-STARRS PS1 prototype telescope. The primary goals of the IPP are to process the science images from the Pan-STARRS telescopes and make the results available to other systems within Pan-STARRS. It also is responsible for combining all of the science images in a given filter into a single representation of the non-variable component of the night sky defined as the "Static Sky". To achieve these goals, the IPP also performs other analysis functions to generate the calibrations needed in the science image processing, and to occasionally use the derived data to generate improved astrometric and photometric reference catalogs. It also provides the infrastructure needed to store the incoming data and the resulting data products. The IPP inherits lessons learned, and in some cases code and prototype code, from several other astronomy image analysis systems, including Imcat (Kaiser), the Sloan Digital Sky Survey (REF), the Elixir system (Magnier & Cuillandre), and Vista (Tonry). Imcat and Vista have a large number of robust image processing functions. SDSS has demonstrated a working analysis pipeline and large-scale databasesystem for a dedicated project. The Elixir system has demonstrated an automatic image processing system and an object database system for operational usage. This talk will present an overview of the IPP architecture, functional flow, code development structure, and selected analysis algorithms. Also discussed is the HW highly parallel HW configuration necessary to support PS1 operational requirements. Finally, results are presented of the processing of images collected during PS1 early commissioning tasks utilizing the Pan-STARRS Test Camera #3.

SU-F-J-94: Development of a Plug-in Based Image Analysis Tool for Integration Into Treatment Planning

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

Owen, D; Anderson, C; Mayo, C

Purpose: To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image-based metrics within treatment plan optimization and (ii) evaluation of dose-functional volume relationships to assist in functional image adaptive radiotherapy. Methods: A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose-functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel-by-voxel basis through API extensions that can access registration transforms. A user interface was designed through WinFormsmore » to input parameters and display results. To test the performance of this program, image- and dose-based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared. Results: The integration of image analysis information was successfully implemented as a plug-in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image-based metrics as well as dose-intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image-based metrics, dose-intensity computations, and dose-intensity histograms were applied to analyze the image-dose profile. Conclusion: It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image-intensity-based metrics in the context of functional targeting and avoidance. In addition to providing dose-intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug-in optimization system, which can directly use the computed metrics for optimization of post-treatment tumor or normal tissue response models. Supported by NIH - P01 - CA059827.« less

Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment.

PubMed

Larkin, Timothy J; Canuto, Holly C; Kettunen, Mikko I; Booth, Thomas C; Hu, De-En; Krishnan, Anant S; Bohndiek, Sarah E; Neves, André A; McLachlan, Charles; Hobson, Michael P; Brindle, Kevin M

2014-01-01

The acquisition of ever increasing volumes of high resolution magnetic resonance imaging (MRI) data has created an urgent need to develop automated and objective image analysis algorithms that can assist in determining tumor margins, diagnosing tumor stage, and detecting treatment response. We have shown previously that Minkowski functionals, which are precise morphological and structural descriptors of image heterogeneity, can be used to enhance the detection, in T1 -weighted images, of a targeted Gd(3+) -chelate-based contrast agent for detecting tumor cell death. We have used Minkowski functionals here to characterize heterogeneity in T2 -weighted images acquired before and after drug treatment, and obtained without contrast agent administration. We show that Minkowski functionals can be used to characterize the changes in image heterogeneity that accompany treatment of tumors with a vascular disrupting agent, combretastatin A4-phosphate, and with a cytotoxic drug, etoposide. Parameterizing changes in the heterogeneity of T2 -weighted images can be used to detect early responses of tumors to drug treatment, even when there is no change in tumor size. The approach provides a quantitative and therefore objective assessment of treatment response that could be used with other types of MR image and also with other imaging modalities. Copyright © 2013 Wiley Periodicals, Inc.

Image Analysis of DNA Fiber and Nucleus in Plants.

PubMed

Ohmido, Nobuko; Wako, Toshiyuki; Kato, Seiji; Fukui, Kiichi

2016-01-01

Advances in cytology have led to the application of a wide range of visualization methods in plant genome studies. Image analysis methods are indispensable tools where morphology, density, and color play important roles in the biological systems. Visualization and image analysis methods are useful techniques in the analyses of the detailed structure and function of extended DNA fibers (EDFs) and interphase nuclei. The EDF is the highest in the spatial resolving power to reveal genome structure and it can be used for physical mapping, especially for closely located genes and tandemly repeated sequences. One the other hand, analyzing nuclear DNA and proteins would reveal nuclear structure and functions. In this chapter, we describe the image analysis protocol for quantitatively analyzing different types of plant genome, EDFs and interphase nuclei.

High resolution multidetector CT aided tissue analysis and quantification of lung fibrosis

NASA Astrophysics Data System (ADS)

Zavaletta, Vanessa A.; Karwoski, Ronald A.; Bartholmai, Brian; Robb, Richard A.

2006-03-01

Idiopathic pulmonary fibrosis (IPF, also known as Idiopathic Usual Interstitial Pneumontis, pathologically) is a progressive diffuse lung disease which has a median survival rate of less than four years with a prevalence of 15-20/100,000 in the United States. Global function changes are measured by pulmonary function tests and the diagnosis and extent of pulmonary structural changes are typically assessed by acquiring two-dimensional high resolution CT (HRCT) images. The acquisition and analysis of volumetric high resolution Multi-Detector CT (MDCT) images with nearly isotropic pixels offers the potential to measure both lung function and structure. This paper presents a new approach to three dimensional lung image analysis and classification of normal and abnormal structures in lungs with IPF.

Ventilation/perfusion SPECT or SPECT/CT for lung function imaging in patients with pulmonary emphysema?

PubMed

Froeling, Vera; Heimann, Uwe; Huebner, Ralf-Harto; Kroencke, Thomas J; Maurer, Martin H; Doellinger, Felix; Geisel, Dominik; Hamm, Bernd; Brenner, Winfried; Schreiter, Nils F

2015-07-01

To evaluate the utility of attenuation correction (AC) of V/P SPECT images for patients with pulmonary emphysema. Twenty-one patients (mean age 67.6 years) with pulmonary emphysema who underwent V/P SPECT/CT were included. AC/non-AC V/P SPECT images were compared visually and semiquantitatively. Visual comparison of AC/non-AC images was based on a 5-point likert scale. Semiquantitative comparison assessed absolute counts per lung (aCpLu) and lung lobe (aCpLo) for AC/non-AC images using software-based analysis; percentage counts (PC = (aCpLo/aCpLu) × 100) were calculated. Correlation between AC/non-AC V/P SPECT images was analyzed using Spearman's rho correlation coefficient; differences were tested for significance with the Wilcoxon rank sum test. Visual analysis revealed high conformity for AC and non-AC V/P SPECT images. Semiquantitative analysis of PC in AC/non-AC images had an excellent correlation and showed no significant differences in perfusion (ρ = 0.986) or ventilation (ρ = 0.979, p = 0.809) SPECT/CT images. AC of V/P SPECT images for lung lobe-based function imaging in patients with pulmonary emphysema do not improve visual or semiquantitative image analysis.

Promise of new imaging technologies for assessing ovarian function.

PubMed

Singh, Jaswant; Adams, Gregg P; Pierson, Roger A

2003-10-15

Advancements in imaging technologies over the last two decades have ushered a quiet revolution in research approaches to the study of ovarian structure and function. The most significant changes in our understanding of the ovary have resulted from the use of ultrasonography which has enabled sequential analyses in live animals. Computer-assisted image analysis and mathematical modeling of the dynamic changes within the ovary has permitted exciting new avenues of research with readily quantifiable endpoints. Spectral, color-flow and power Doppler imaging now facilitate physiologic interpretations of vascular dynamics over time. Similarly, magnetic resonance imaging (MRI) is emerging as a research tool in ovarian imaging. New technologies, such as three-dimensional ultrasonography and MRI, ultrasound-based biomicroscopy and synchrotron-based techniques each have the potential to enhance our real-time picture of ovarian function to the near-cellular level. Collectively, information available in ultrasonography, MRI, computer-assisted image analysis and mathematical modeling heralds a new era in our understanding of the basic processes of female and male reproduction.

TWave: High-Order Analysis of Functional MRI

PubMed Central

Barnathan, Michael; Megalooikonomou, Vasileios; Faloutsos, Christos; Faro, Scott; Mohamed, Feroze B.

2011-01-01

The traditional approach to functional image analysis models images as matrices of raw voxel intensity values. Although such a representation is widely utilized and heavily entrenched both within neuroimaging and in the wider data mining community, the strong interactions among space, time, and categorical modes such as subject and experimental task inherent in functional imaging yield a dataset with “high-order” structure, which matrix models are incapable of exploiting. Reasoning across all of these modes of data concurrently requires a high-order model capable of representing relationships between all modes of the data in tandem. We thus propose to model functional MRI data using tensors, which are high-order generalizations of matrices equivalent to multidimensional arrays or data cubes. However, several unique challenges exist in the high-order analysis of functional medical data: naïve tensor models are incapable of exploiting spatiotemporal locality patterns, standard tensor analysis techniques exhibit poor efficiency, and mixtures of numeric and categorical modes of data are very often present in neuroimaging experiments. Formulating the problem of image clustering as a form of Latent Semantic Analysis and using the WaveCluster algorithm as a baseline, we propose a comprehensive hybrid tensor and wavelet framework for clustering, concept discovery, and compression of functional medical images which successfully addresses these challenges. Our approach reduced runtime and dataset size on a 9.3 GB finger opposition motor task fMRI dataset by up to 98% while exhibiting improved spatiotemporal coherence relative to standard tensor, wavelet, and voxel-based approaches. Our clustering technique was capable of automatically differentiating between the frontal areas of the brain responsible for task-related habituation and the motor regions responsible for executing the motor task, in contrast to a widely used fMRI analysis program, SPM, which only detected the latter region. Furthermore, our approach discovered latent concepts suggestive of subject handedness nearly 100x faster than standard approaches. These results suggest that a high-order model is an integral component to accurate scalable functional neuroimaging. PMID:21729758

False dyssynchrony: problem with image-based cardiac functional analysis using x-ray computed tomography

NASA Astrophysics Data System (ADS)

Kidoh, Masafumi; Shen, Zeyang; Suzuki, Yuki; Ciuffo, Luisa; Ashikaga, Hiroshi; Fung, George S. K.; Otake, Yoshito; Zimmerman, Stefan L.; Lima, Joao A. C.; Higuchi, Takahiro; Lee, Okkyun; Sato, Yoshinobu; Becker, Lewis C.; Fishman, Elliot K.; Taguchi, Katsuyuki

2017-03-01

We have developed a digitally synthesized patient which we call "Zach" (Zero millisecond Adjustable Clinical Heart) phantom, which allows for an access to the ground truth and assessment of image-based cardiac functional analysis (CFA) using CT images with clinically realistic settings. The study using Zach phantom revealed a major problem with image-based CFA: "False dyssynchrony." Even though the true motion of wall segments is in synchrony, it may appear to be dyssynchrony with the reconstructed cardiac CT images. It is attributed to how cardiac images are reconstructed and how wall locations are updated over cardiac phases. The presence and the degree of false dyssynchrony may vary from scan-to-scan, which could degrade the accuracy and the repeatability (or precision) of image-based CT-CFA exams.

Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets

DOE PAGES

Belianinov, Alex; Vasudevan, Rama K; Strelcov, Evgheni; ...

2015-05-13

The development of electron, and scanning probe microscopies in the second half of the twentieth century have produced spectacular images of internal structure and composition of matter with, at nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition and analysis. The progress in imaging technologies in the beginning of the twenty first century has opened the proverbial floodgates of high-veracity information on structure and functionality. High resolution imaging now allows information on atomic positions with picometer precision, allowing for quantitative measurements of individual bond length and angles. Functional imaging often leadsmore » to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this data into physically and chemically relevant information from imaging data.« less

Big Data and Deep data in scanning and electron microscopies: functionality from multidimensional data sets

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

Belianinov, Alex; Vasudevan, Rama K; Strelcov, Evgheni

The development of electron, and scanning probe microscopies in the second half of the twentieth century have produced spectacular images of internal structure and composition of matter with, at nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition and analysis. The progress in imaging technologies in the beginning of the twenty first century has opened the proverbial floodgates of high-veracity information on structure and functionality. High resolution imaging now allows information on atomic positions with picometer precision, allowing for quantitative measurements of individual bond length and angles. Functional imaging often leadsmore » to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this data into physically and chemically relevant information from imaging data.« less

Analysis of autostereoscopic three-dimensional images using multiview wavelets.

PubMed

Saveljev, Vladimir; Palchikova, Irina

2016-08-10

We propose that multiview wavelets can be used in processing multiview images. The reference functions for the synthesis/analysis of multiview images are described. The synthesized binary images were observed experimentally as three-dimensional visual images. The symmetric multiview B-spline wavelets are proposed. The locations recognized in the continuous wavelet transform correspond to the layout of the test objects. The proposed wavelets can be applied to the multiview, integral, and plenoptic images.

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function

PubMed Central

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D.

2009-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings. PMID:19834575

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function.

PubMed

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D

2008-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

PubMed

Mathotaarachchi, Sulantha; Wang, Seqian; Shin, Monica; Pascoal, Tharick A; Benedet, Andrea L; Kang, Min Su; Beaudry, Thomas; Fonov, Vladimir S; Gauthier, Serge; Labbe, Aurélie; Rosa-Neto, Pedro

2016-01-01

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

IMAGE EXPLORER: Astronomical Image Analysis on an HTML5-based Web Application

NASA Astrophysics Data System (ADS)

Gopu, A.; Hayashi, S.; Young, M. D.

2014-05-01

Large datasets produced by recent astronomical imagers cause the traditional paradigm for basic visual analysis - typically downloading one's entire image dataset and using desktop clients like DS9, Aladin, etc. - to not scale, despite advances in desktop computing power and storage. This paper describes Image Explorer, a web framework that offers several of the basic visualization and analysis functionality commonly provided by tools like DS9, on any HTML5 capable web browser on various platforms. It uses a combination of the modern HTML5 canvas, JavaScript, and several layers of lossless PNG tiles producted from the FITS image data. Astronomers are able to rapidly and simultaneously open up several images on their web-browser, adjust the intensity min/max cutoff or its scaling function, and zoom level, apply color-maps, view position and FITS header information, execute typically used data reduction codes on the corresponding FITS data using the FRIAA framework, and overlay tiles for source catalog objects, etc.

A new feedback image encryption scheme based on perturbation with dynamical compound chaotic sequence cipher generator

NASA Astrophysics Data System (ADS)

Tong, Xiaojun; Cui, Minggen; Wang, Zhu

2009-07-01

The design of the new compound two-dimensional chaotic function is presented by exploiting two one-dimensional chaotic functions which switch randomly, and the design is used as a chaotic sequence generator which is proved by Devaney's definition proof of chaos. The properties of compound chaotic functions are also proved rigorously. In order to improve the robustness against difference cryptanalysis and produce avalanche effect, a new feedback image encryption scheme is proposed using the new compound chaos by selecting one of the two one-dimensional chaotic functions randomly and a new image pixels method of permutation and substitution is designed in detail by array row and column random controlling based on the compound chaos. The results from entropy analysis, difference analysis, statistical analysis, sequence randomness analysis, cipher sensitivity analysis depending on key and plaintext have proven that the compound chaotic sequence cipher can resist cryptanalytic, statistical and brute-force attacks, and especially it accelerates encryption speed, and achieves higher level of security. By the dynamical compound chaos and perturbation technology, the paper solves the problem of computer low precision of one-dimensional chaotic function.

Features and limitations of mobile tablet devices for viewing radiological images.

PubMed

Grunert, J H

2015-03-01

Mobile radiological image display systems are becoming increasingly common, necessitating a comparison of the features of these systems, specifically the operating system employed, connection to stationary PACS, data security and rang of image display and image analysis functions. In the fall of 2013, a total of 17 PACS suppliers were surveyed regarding the technical features of 18 mobile radiological image display systems using a standardized questionnaire. The study also examined to what extent the technical specifications of the mobile image display systems satisfy the provisions of the Germany Medical Devices Act as well as the provisions of the German X-ray ordinance (RöV). There are clear differences in terms of how the mobile systems connected to the stationary PACS. Web-based solutions allow the mobile image display systems to function independently of their operating systems. The examined systems differed very little in terms of image display and image analysis functions. Mobile image display systems complement stationary PACS and can be used to view images. The impacts of the new quality assurance guidelines (QS-RL) as well as the upcoming new standard DIN 6868 - 157 on the acceptance testing of mobile image display units for the purpose of image evaluation are discussed. © Georg Thieme Verlag KG Stuttgart · New York.

Fourier analysis: from cloaking to imaging

NASA Astrophysics Data System (ADS)

Wu, Kedi; Cheng, Qiluan; Wang, Guo Ping

2016-04-01

Regarding invisibility cloaks as an optical imaging system, we present a Fourier approach to analytically unify both Pendry cloaks and complementary media-based invisibility cloaks into one kind of cloak. By synthesizing different transfer functions, we can construct different devices to realize a series of interesting functions such as hiding objects (events), creating illusions, and performing perfect imaging. In this article, we give a brief review on recent works of applying Fourier approach to analysis invisibility cloaks and optical imaging through scattering layers. We show that, to construct devices to conceal an object, no constructive materials with extreme properties are required, making most, if not all, of the above functions realizable by using naturally occurring materials. As instances, we experimentally verify a method of directionally hiding distant objects and create illusions by using all-dielectric materials, and further demonstrate a non-invasive method of imaging objects completely hidden by scattering layers.

Body image disturbance in adults treated for cancer - a concept analysis.

PubMed

Rhoten, Bethany A

2016-05-01

To report an analysis of the concept of body image disturbance in adults who have been treated for cancer as a phenomenon of interest to nurses. Although the concept of body image disturbance has been clearly defined in adolescents and adults with eating disorders, adults who have been treated for cancer may also experience body image disturbance. In this context, the concept of body image disturbance has not been clearly defined. Concept analysis. PubMed, Psychological Information Database and Cumulative Index of Nursing and Allied Health Literature were searched for publications from 1937 - 2015. Search terms included body image, cancer, body image disturbance, adult and concept analysis. Walker and Avant's 8-step method of concept analysis was used. The defining attributes of body image disturbance in adults who have been treated for cancer are: (1) self-perception of a change in appearance and displeasure with the change or perceived change in appearance; (2) decline in an area of function; and (3) psychological distress regarding changes in appearance and/or function. This concept analysis provides a foundation for the development of multidimensional assessment tools and interventions to alleviate body image disturbance in this population. A better understanding of body image disturbance in adults treated for cancer will assist nurses and other clinicians in identifying this phenomenon and nurse scientists in developing instruments that accurately measure this condition, along with interventions that will promote a better quality of life for survivors. © 2016 John Wiley & Sons Ltd.

Functional laser speckle imaging of cerebral blood flow under hypothermia

NASA Astrophysics Data System (ADS)

Li, Minheng; Miao, Peng; Zhu, Yisheng; Tong, Shanbao

2011-08-01

Hypothermia can unintentionally occur in daily life, e.g., in cardiovascular surgery or applied as therapeutics in the neurosciences critical care unit. So far, the temperature-induced spatiotemporal responses of the neural function have not been fully understood. In this study, we investigated the functional change in cerebral blood flow (CBF), accompanied with neuronal activation, by laser speckle imaging (LSI) during hypothermia. Laser speckle images from Sprague-Dawley rats (n = 8, male) were acquired under normothermia (37°C) and moderate hypothermia (32°C). For each animal, 10 trials of electrical hindpaw stimulation were delivered under both temperatures. Using registered laser speckle contrast analysis and temporal clustering analysis (TCA), we found a delayed response peak and a prolonged response window under hypothermia. Hypothermia also decreased the activation area and the amplitude of the peak CBF. The combination of LSI and TCA is a high-resolution functional imaging method to investigate the spatiotemporal neurovascular coupling in both normal and pathological brain functions.

Visually enhanced CCTV digital surveillance utilizing Intranet and Internet.

PubMed

Ozaki, Nobuyuki

2002-07-01

This paper describes a solution for integrated plant supervision utilizing closed circuit television (CCTV) digital surveillance. Three basic requirements are first addressed as the platform of the system, with discussion on the suitable video compression. The system configuration is described in blocks. The system provides surveillance functionality: real-time monitoring, and process analysis functionality: a troubleshooting tool. This paper describes the formulation of practical performance design for determining various encoder parameters. It also introduces image processing techniques for enhancing the original CCTV digital image to lessen the burden on operators. Some screenshots are listed for the surveillance functionality. For the process analysis, an image searching filter supported by image processing techniques is explained with screenshots. Multimedia surveillance, which is the merger with process data surveillance, or the SCADA system, is also explained.

Structure function monitor

DOEpatents

McGraw, John T [Placitas, NM; Zimmer, Peter C [Albuquerque, NM; Ackermann, Mark R [Albuquerque, NM

2012-01-24

Methods and apparatus for a structure function monitor provide for generation of parameters characterizing a refractive medium. In an embodiment, a structure function monitor acquires images of a pupil plane and an image plane and, from these images, retrieves the phase over an aperture, unwraps the retrieved phase, and analyzes the unwrapped retrieved phase. In an embodiment, analysis yields atmospheric parameters measured at spatial scales from zero to the diameter of a telescope used to collect light from a source.

Influence of speckle image reconstruction on photometric precision for large solar telescopes

NASA Astrophysics Data System (ADS)

Peck, C. L.; Wöger, F.; Marino, J.

2017-11-01

Context. High-resolution observations from large solar telescopes require adaptive optics (AO) systems to overcome image degradation caused by Earth's turbulent atmosphere. AO corrections are, however, only partial. Achieving near-diffraction limited resolution over a large field of view typically requires post-facto image reconstruction techniques to reconstruct the source image. Aims: This study aims to examine the expected photometric precision of amplitude reconstructed solar images calibrated using models for the on-axis speckle transfer functions and input parameters derived from AO control data. We perform a sensitivity analysis of the photometric precision under variations in the model input parameters for high-resolution solar images consistent with four-meter class solar telescopes. Methods: Using simulations of both atmospheric turbulence and partial compensation by an AO system, we computed the speckle transfer function under variations in the input parameters. We then convolved high-resolution numerical simulations of the solar photosphere with the simulated atmospheric transfer function, and subsequently deconvolved them with the model speckle transfer function to obtain a reconstructed image. To compute the resulting photometric precision, we compared the intensity of the original image with the reconstructed image. Results: The analysis demonstrates that high photometric precision can be obtained for speckle amplitude reconstruction using speckle transfer function models combined with AO-derived input parameters. Additionally, it shows that the reconstruction is most sensitive to the input parameter that characterizes the atmospheric distortion, and sub-2% photometric precision is readily obtained when it is well estimated.

Haemodynamic imaging of thoracic stent-grafts by computational fluid dynamics (CFD): presentation of a patient-specific method combining magnetic resonance imaging and numerical simulations.

PubMed

Midulla, Marco; Moreno, Ramiro; Baali, Adil; Chau, Ming; Negre-Salvayre, Anne; Nicoud, Franck; Pruvo, Jean-Pierre; Haulon, Stephan; Rousseau, Hervé

2012-10-01

In the last decade, there was been increasing interest in finding imaging techniques able to provide a functional vascular imaging of the thoracic aorta. The purpose of this paper is to present an imaging method combining magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) to obtain a patient-specific haemodynamic analysis of patients treated by thoracic endovascular aortic repair (TEVAR). MRI was used to obtain boundary conditions. MR angiography (MRA) was followed by cardiac-gated cine sequences which covered the whole thoracic aorta. Phase contrast imaging provided the inlet and outlet profiles. A CFD mesh generator was used to model the arterial morphology, and wall movements were imposed according to the cine imaging. CFD runs were processed using the finite volume (FV) method assuming blood as a homogeneous Newtonian fluid. Twenty patients (14 men; mean age 62.2 years) with different aortic lesions were evaluated. Four-dimensional mapping of velocity and wall shear stress were obtained, depicting different patterns of flow (laminar, turbulent, stenosis-like) and local alterations of parietal stress in-stent and along the native aorta. A computational method using a combined approach with MRI appears feasible and seems promising to provide detailed functional analysis of thoracic aorta after stent-graft implantation. • Functional vascular imaging of the thoracic aorta offers new diagnostic opportunities • CFD can model vascular haemodynamics for clinical aortic problems • Combining CFD with MRI offers patient specific method of aortic analysis • Haemodynamic analysis of stent-grafts could improve clinical management and follow-up.

Radiometric analysis of photographic data by the effective exposure method

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

Constantine, B J

1972-04-01

The effective exposure method provides for radiometric analysis of photographic data. A three-dimensional model, where density is a function of energy and wavelength, is postulated to represent the film response function. Calibration exposures serve to eliminate the other factors which affect image density. The effective exposure causing an image can be determined by comparing the image density with that of a calibration exposure. If the relative spectral distribution of the source is known, irradiance and/or radiance can be unfolded from the effective exposure expression.

An independent software system for the analysis of dynamic MR images.

PubMed

Torheim, G; Lombardi, M; Rinck, P A

1997-01-01

A computer system for the manual, semi-automatic, and automatic analysis of dynamic MR images was to be developed on UNIX and personal computer platforms. The system was to offer an integrated and standardized way of performing both image processing and analysis that was independent of the MR unit used. The system consists of modules that are easily adaptable to special needs. Data from MR units or other diagnostic imaging equipment in techniques such as CT, ultrasonography, or nuclear medicine can be processed through the ACR-NEMA/DICOM standard file formats. A full set of functions is available, among them cine-loop visual analysis, and generation of time-intensity curves. Parameters such as cross-correlation coefficients, area under the curve, peak/maximum intensity, wash-in and wash-out slopes, time to peak, and relative signal intensity/contrast enhancement can be calculated. Other parameters can be extracted by fitting functions like the gamma-variate function. Region-of-interest data and parametric values can easily be exported. The system has been successfully tested in animal and patient examinations.

Design Criteria For Networked Image Analysis System

NASA Astrophysics Data System (ADS)

Reader, Cliff; Nitteberg, Alan

1982-01-01

Image systems design is currently undergoing a metamorphosis from the conventional computing systems of the past into a new generation of special purpose designs. This change is motivated by several factors, notably among which is the increased opportunity for high performance with low cost offered by advances in semiconductor technology. Another key issue is a maturing in understanding of problems and the applicability of digital processing techniques. These factors allow the design of cost-effective systems that are functionally dedicated to specific applications and used in a utilitarian fashion. Following an overview of the above stated issues, the paper presents a top-down approach to the design of networked image analysis systems. The requirements for such a system are presented, with orientation toward the hospital environment. The three main areas are image data base management, viewing of image data and image data processing. This is followed by a survey of the current state of the art, covering image display systems, data base techniques, communications networks and software systems control. The paper concludes with a description of the functional subystems and architectural framework for networked image analysis in a production environment.

Targeting Neuronal-like Metabolism of Metastatic Tumor Cells as a Novel Therapy for Breast Cancer Brain Metastasis

DTIC Science & Technology

2017-03-01

Contribution to Project: Ian primarily focuses on developing tissue imaging pipeline and perform imaging data analysis . Funding Support: Partially...3D ReconsTruction), a multi-faceted image analysis pipeline , permitting quantitative interrogation of functional implications of heterogeneous... analysis pipeline , to observe and quantify phenotypic metastatic landscape heterogeneity in situ with spatial and molecular resolution. Our implementation

Electrophoresis gel image processing and analysis using the KODAK 1D software.

PubMed

Pizzonia, J

2001-06-01

The present article reports on the performance of the KODAK 1D Image Analysis Software for the acquisition of information from electrophoresis experiments and highlights the utility of several mathematical functions for subsequent image processing, analysis, and presentation. Digital images of Coomassie-stained polyacrylamide protein gels containing molecular weight standards and ethidium bromide stained agarose gels containing DNA mass standards are acquired using the KODAK Electrophoresis Documentation and Analysis System 290 (EDAS 290). The KODAK 1D software is used to optimize lane and band identification using features such as isomolecular weight lines. Mathematical functions for mass standard representation are presented, and two methods for estimation of unknown band mass are compared. Given the progressive transition of electrophoresis data acquisition and daily reporting in peer-reviewed journals to digital formats ranging from 8-bit systems such as EDAS 290 to more expensive 16-bit systems, the utility of algorithms such as Gaussian modeling, which can correct geometric aberrations such as clipping due to signal saturation common at lower bit depth levels, is discussed. Finally, image-processing tools that can facilitate image preparation for presentation are demonstrated.

Point spread function and depth-invariant focal sweep point spread function for plenoptic camera 2.0.

PubMed

Jin, Xin; Liu, Li; Chen, Yanqin; Dai, Qionghai

2017-05-01

This paper derives a mathematical point spread function (PSF) and a depth-invariant focal sweep point spread function (FSPSF) for plenoptic camera 2.0. Derivation of PSF is based on the Fresnel diffraction equation and image formation analysis of a self-built imaging system which is divided into two sub-systems to reflect the relay imaging properties of plenoptic camera 2.0. The variations in PSF, which are caused by changes of object's depth and sensor position variation, are analyzed. A mathematical model of FSPSF is further derived, which is verified to be depth-invariant. Experiments on the real imaging systems demonstrate the consistency between the proposed PSF and the actual imaging results.

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

Nuclear cardiac

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

Slutsky, R.; Ashburn, W.L.

1982-01-01

The relationship between nuclear medicine and cardiology has continued to produce a surfeit of interesting, illuminating, and important reports involving the analysis of cardiac function, perfusion, and metabolism. To simplify the presentation, this review is broken down into three major subheadings: analysis of myocardial perfusion; imaging of the recent myocardial infarction; and the evaluation of myocardial function. There appears to be an increasingly important relationship between cardiology, particularly cardiac physiology, and nuclear imaging techniques. (KRM)

Use of localized performance-based functions for the specification and correction of hybrid imaging systems

NASA Astrophysics Data System (ADS)

Lisson, Jerold B.; Mounts, Darryl I.; Fehniger, Michael J.

1992-08-01

Localized wavefront performance analysis (LWPA) is a system that allows the full utilization of the system optical transfer function (OTF) for the specification and acceptance of hybrid imaging systems. We show that LWPA dictates the correction of wavefront errors with the greatest impact on critical imaging spatial frequencies. This is accomplished by the generation of an imaging performance map-analogous to a map of the optic pupil error-using a local OTF. The resulting performance map a function of transfer function spatial frequency is directly relatable to the primary viewing condition of the end-user. In addition to optimizing quality for the viewer it will be seen that the system has the potential for an improved matching of the optical and electronic bandpass of the imager and for the development of more realistic acceptance specifications. 1. LOCAL WAVEFRONT PERFORMANCE ANALYSIS The LWPA system generates a local optical quality factor (LOQF) in the form of a map analogous to that used for the presentation and evaluation of wavefront errors. In conjunction with the local phase transfer function (LPTF) it can be used for maximally efficient specification and correction of imaging system pupil errors. The LOQF and LPTF are respectively equivalent to the global modulation transfer function (MTF) and phase transfer function (PTF) parts of the OTF. The LPTF is related to difference of the average of the errors in separated regions of the pupil. Figure

Analysis of identification of digital images from a map of cosmic microwaves

NASA Astrophysics Data System (ADS)

Skeivalas, J.; Turla, V.; Jurevicius, M.; Viselga, G.

2018-04-01

This paper discusses identification of digital images from the cosmic microwave background radiation map formed according to the data of the European Space Agency "Planck" telescope by applying covariance functions and wavelet theory. The estimates of covariance functions of two digital images or single images are calculated according to the random functions formed of the digital images in the form of pixel vectors. The estimates of pixel vectors are formed on expansion of the pixel arrays of the digital images by a single vector. When the scale of a digital image is varied, the frequencies of single-pixel color waves remain constant and the procedure for calculation of covariance functions is not affected. For identification of the images, the RGB format spectrum has been applied. The impact of RGB spectrum components and the color tensor on the estimates of covariance functions was analyzed. The identity of digital images is assessed according to the changes in the values of the correlation coefficients in a certain range of values by applying the developed computer program.

Quantitative Pulmonary Imaging Using Computed Tomography and Magnetic Resonance Imaging

PubMed Central

Washko, George R.; Parraga, Grace; Coxson, Harvey O.

2011-01-01

Measurements of lung function, including spirometry and body plethesmography, are easy to perform and are the current clinical standard for assessing disease severity. However, these lung functional techniques do not adequately explain the observed variability in clinical manifestations of disease and offer little insight into the relationship of lung structure and function. Lung imaging and the image based assessment of lung disease has matured to the extent that it is common for clinical, epidemiologic, and genetic investigation to have a component dedicated to image analysis. There are several exciting imaging modalities currently being used for the non-invasive study of lung anatomy and function. In this review we will focus on two of them, x-ray computed tomography and magnetic resonance imaging. Following a brief introduction of each method we detail some of the most recent work being done to characterize smoking-related lung disease and the clinical applications of such knowledge. PMID:22142490

Atlas-guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision-making responses in young adults.

PubMed

Lin, Zi-Jing; Li, Lin; Cazzell, Mary; Liu, Hanli

2014-08-01

Diffuse optical tomography (DOT) is a variant of functional near infrared spectroscopy and has the capability of mapping or reconstructing three dimensional (3D) hemodynamic changes due to brain activity. Common methods used in DOT image analysis to define brain activation have limitations because the selection of activation period is relatively subjective. General linear model (GLM)-based analysis can overcome this limitation. In this study, we combine the atlas-guided 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with risk decision-making processes. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The Balloon Analog Risk Task (BART) is a valid experimental model and has been commonly used to assess human risk-taking actions and tendencies while facing risks. We have used the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making from 37 human participants (22 males and 15 females). Voxel-wise GLM analysis was performed after a human brain atlas template and a depth compensation algorithm were combined to form atlas-guided DOT images. In this work, we wish to demonstrate the excellence of using voxel-wise GLM analysis with DOT to image and study cognitive functions in response to risk decision-making. Results have shown significant hemodynamic changes in the dorsal lateral prefrontal cortex (DLPFC) during the active-choice mode and a different activation pattern between genders; these findings correlate well with published literature in functional magnetic resonance imaging (fMRI) and fNIRS studies. Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Comparative analysis of numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence

NASA Astrophysics Data System (ADS)

Lachinova, Svetlana L.; Vorontsov, Mikhail A.; Filimonov, Grigory A.; LeMaster, Daniel A.; Trippel, Matthew E.

2017-07-01

Computational efficiency and accuracy of wave-optics-based Monte-Carlo and brightness function numerical simulation techniques for incoherent imaging of extended objects through atmospheric turbulence are evaluated. Simulation results are compared with theoretical estimates based on known analytical solutions for the modulation transfer function of an imaging system and the long-exposure image of a Gaussian-shaped incoherent light source. It is shown that the accuracy of both techniques is comparable over the wide range of path lengths and atmospheric turbulence conditions, whereas the brightness function technique is advantageous in terms of the computational speed.

Flightspeed Integral Image Analysis Toolkit

NASA Technical Reports Server (NTRS)

Thompson, David R.

2009-01-01

The Flightspeed Integral Image Analysis Toolkit (FIIAT) is a C library that provides image analysis functions in a single, portable package. It provides basic low-level filtering, texture analysis, and subwindow descriptor for applications dealing with image interpretation and object recognition. Designed with spaceflight in mind, it addresses: Ease of integration (minimal external dependencies) Fast, real-time operation using integer arithmetic where possible (useful for platforms lacking a dedicated floatingpoint processor) Written entirely in C (easily modified) Mostly static memory allocation 8-bit image data The basic goal of the FIIAT library is to compute meaningful numerical descriptors for images or rectangular image regions. These n-vectors can then be used directly for novelty detection or pattern recognition, or as a feature space for higher-level pattern recognition tasks. The library provides routines for leveraging training data to derive descriptors that are most useful for a specific data set. Its runtime algorithms exploit a structure known as the "integral image." This is a caching method that permits fast summation of values within rectangular regions of an image. This integral frame facilitates a wide range of fast image-processing functions. This toolkit has applicability to a wide range of autonomous image analysis tasks in the space-flight domain, including novelty detection, object and scene classification, target detection for autonomous instrument placement, and science analysis of geomorphology. It makes real-time texture and pattern recognition possible for platforms with severe computational restraints. The software provides an order of magnitude speed increase over alternative software libraries currently in use by the research community. FIIAT can commercially support intelligent video cameras used in intelligent surveillance. It is also useful for object recognition by robots or other autonomous vehicles

Experimental assessment and analysis of super-resolution in fluorescence microscopy based on multiple-point spread function fitting of spectrally demultiplexed images

NASA Astrophysics Data System (ADS)

Nishimura, Takahiro; Kimura, Hitoshi; Ogura, Yusuke; Tanida, Jun

2018-06-01

This paper presents an experimental assessment and analysis of super-resolution microscopy based on multiple-point spread function fitting of spectrally demultiplexed images using a designed DNA structure as a test target. For the purpose, a DNA structure was designed to have binding sites at a certain interval that is smaller than the diffraction limit. The structure was labeled with several types of quantum dots (QDs) to acquire their spatial information as spectrally encoded images. The obtained images are analyzed with a point spread function multifitting algorithm to determine the QD locations that indicate the binding site positions. The experimental results show that the labeled locations can be observed beyond the diffraction-limited resolution using three-colored fluorescence images that were obtained with a confocal fluorescence microscope. Numerical simulations show that labeling with eight types of QDs enables the positions aligned at 27.2-nm pitches on the DNA structure to be resolved with high accuracy.

Projection-based motion estimation for cardiac functional analysis with high temporal resolution: a proof-of-concept study with digital phantom experiment

NASA Astrophysics Data System (ADS)

Suzuki, Yuki; Fung, George S. K.; Shen, Zeyang; Otake, Yoshito; Lee, Okkyun; Ciuffo, Luisa; Ashikaga, Hiroshi; Sato, Yoshinobu; Taguchi, Katsuyuki

2017-03-01

Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of cardiovascular diseases but also for prediction of cardiac future events. Current imaging modalities has limitations that could degrade the accuracy of the analysis indices. In this paper, we present a projection-based motion estimation method for x-ray CT that estimates cardiac motion with high spatio-temporal resolution using projection data and a reference 3D volume image. The experiment using a synthesized digital phantom showed promising results for motion analysis.

Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data

PubMed Central

Wang, Yinxue; Shi, Guilai; Miller, David J.; Wang, Yizhi; Wang, Congchao; Broussard, Gerard; Wang, Yue; Tian, Lin; Yu, Guoqiang

2017-01-01

Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca2+ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca2+ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP. PMID:28769780

Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data.

PubMed

Wang, Yinxue; Shi, Guilai; Miller, David J; Wang, Yizhi; Wang, Congchao; Broussard, Gerard; Wang, Yue; Tian, Lin; Yu, Guoqiang

2017-01-01

Recent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca 2+ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca 2+ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP.

Binary partition tree analysis based on region evolution and its application to tree simplification.

PubMed

Lu, Huihai; Woods, John C; Ghanbari, Mohammed

2007-04-01

Pyramid image representations via tree structures are recognized methods for region-based image analysis. Binary partition trees can be applied which document the merging process with small details found at the bottom levels and larger ones close to the root. Hindsight of the merging process is stored within the tree structure and provides the change histories of an image property from the leaf to the root node. In this work, the change histories are modelled by evolvement functions and their second order statistics are analyzed by using a knee function. Knee values show the reluctancy of each merge. We have systematically formulated these findings to provide a novel framework for binary partition tree analysis, where tree simplification is demonstrated. Based on an evolvement function, for each upward path in a tree, the tree node associated with the first reluctant merge is considered as a pruning candidate. The result is a simplified version providing a reduced solution space and still complying with the definition of a binary tree. The experiments show that image details are preserved whilst the number of nodes is dramatically reduced. An image filtering tool also results which preserves object boundaries and has applications for segmentation.

An improved level set method for brain MR images segmentation and bias correction.

PubMed

Chen, Yunjie; Zhang, Jianwei; Macione, Jim

2009-10-01

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field estimation is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents a variational level set approach to bias correction and segmentation for images with intensity inhomogeneities. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the overall intensity inhomogeneity. We first define a localized K-means-type clustering objective function for image intensities in a neighborhood around each point. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain to define the data term into the level set framework. Our method is able to capture bias of quite general profiles. Moreover, it is robust to initialization, and thereby allows fully automated applications. The proposed method has been used for images of various modalities with promising results.

Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis.

PubMed

Rahaman, Md Matiur; Ahsan, Md Asif; Gillani, Zeeshan; Chen, Ming

2017-09-01

Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.

In situ nondestructive imaging of functional pigments in Micro-Tom tomato fruits by multi spectral imaging based on Wiener estimation method

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Ooe, Shintaro; Todoroki, Shinsuke; Asamizu, Erika

2013-05-01

To evaluate the functional pigments in the tomato fruits nondestructively, we propose a method based on the multispectral diffuse reflectance images estimated by the Wiener estimation for a digital RGB image. Each pixel of the multispectral image is converted to the absorbance spectrum and then analyzed by the multiple regression analysis to visualize the contents of chlorophyll a, lycopene and β-carotene. The result confirms the feasibility of the method for in situ imaging of chlorophyll a, β-carotene and lycopene in the tomato fruits.

Neurological soft signs are not "soft" in brain structure and functional networks: evidence from ALE meta-analysis.

PubMed

Zhao, Qing; Li, Zhi; Huang, Jia; Yan, Chao; Dazzan, Paola; Pantelis, Christos; Cheung, Eric F C; Lui, Simon S Y; Chan, Raymond C K

2014-05-01

Neurological soft signs (NSS) are associated with schizophrenia and related psychotic disorders. NSS have been conventionally considered as clinical neurological signs without localized brain regions. However, recent brain imaging studies suggest that NSS are partly localizable and may be associated with deficits in specific brain areas. We conducted an activation likelihood estimation meta-analysis to quantitatively review structural and functional imaging studies that evaluated the brain correlates of NSS in patients with schizophrenia and other psychotic disorders. Six structural magnetic resonance imaging (sMRI) and 15 functional magnetic resonance imaging (fMRI) studies were included. The results from meta-analysis of the sMRI studies indicated that NSS were associated with atrophy of the precentral gyrus, the cerebellum, the inferior frontal gyrus, and the thalamus. The results from meta-analysis of the fMRI studies demonstrated that the NSS-related task was significantly associated with altered brain activation in the inferior frontal gyrus, bilateral putamen, the cerebellum, and the superior temporal gyrus. Our findings from both sMRI and fMRI meta-analyses further support the conceptualization of NSS as a manifestation of the "cerebello-thalamo-prefrontal" brain network model of schizophrenia and related psychotic disorders.

Contributions of structural connectivity and cerebrovascular parameters to functional magnetic resonance imaging signals in mice at rest and during sensory paw stimulation.

PubMed

Schroeter, Aileen; Grandjean, Joanes; Schlegel, Felix; Saab, Bechara J; Rudin, Markus

2017-07-01

Previously, we reported widespread bilateral increases in stimulus-evoked functional magnetic resonance imaging signals in mouse brain to unilateral sensory paw stimulation. We attributed the pattern to arousal-related cardiovascular changes overruling cerebral autoregulation thereby masking specific signal changes elicited by local neuronal activity. To rule out the possibility that interhemispheric neuronal communication might contribute to bilateral functional magnetic resonance imaging responses, we compared stimulus-evoked functional magnetic resonance imaging responses to unilateral hindpaw stimulation in acallosal I/LnJ, C57BL/6, and BALB/c mice. We found bilateral blood-oxygenation-level dependent signal changes in all three strains, ruling out a dominant contribution of transcallosal communication as reason for bilaterality. Analysis of functional connectivity derived from resting-state functional magnetic resonance imaging, revealed that bilateral cortical functional connectivity is largely abolished in I/LnJ animals. Cortical functional connectivity in all strains correlated with structural connectivity in corpus callosum as revealed by diffusion tensor imaging. Given the profound influence of systemic hemodynamics on stimulus-evoked functional magnetic resonance imaging outcomes, we evaluated whether functional connectivity data might be affected by cerebrovascular parameters, i.e. baseline cerebral blood volume, vascular reactivity, and reserve. We found that effects of cerebral hemodynamics on functional connectivity are largely outweighed by dominating contributions of structural connectivity. In contrast, contributions of transcallosal interhemispheric communication to the occurrence of ipsilateral functional magnetic resonance imaging response of equal amplitude to unilateral stimuli seem negligible.

GiA Roots: software for the high throughput analysis of plant root system architecture.

PubMed

Galkovskyi, Taras; Mileyko, Yuriy; Bucksch, Alexander; Moore, Brad; Symonova, Olga; Price, Charles A; Topp, Christopher N; Iyer-Pascuzzi, Anjali S; Zurek, Paul R; Fang, Suqin; Harer, John; Benfey, Philip N; Weitz, Joshua S

2012-07-26

Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks. We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user. We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

Imaging and Modeling of Myocardial Metabolism

PubMed Central

Jamshidi, Neema; Karimi, Afshin; Birgersdotter-Green, Ulrika; Hoh, Carl

2010-01-01

Current imaging methods have focused on evaluation of myocardial anatomy and function. However, since myocardial metabolism and function are interrelated, metabolic myocardial imaging techniques, such as positron emission tomography, single photon emission tomography, and magnetic resonance spectroscopy present novel opportunities for probing myocardial pathology and developing new therapeutic approaches. Potential clinical applications of metabolic imaging include hypertensive and ischemic heart disease, heart failure, cardiac transplantation, as well as cardiomyopathies. Furthermore, response to therapeutic intervention can be monitored using metabolic imaging. Analysis of metabolic data in the past has been limited, focusing primarily on isolated metabolites. Models of myocardial metabolism, however, such as the oxygen transport and cellular energetics model and constraint-based metabolic network modeling, offer opportunities for evaluation interactions between greater numbers of metabolites in the heart. In this review, the roles of metabolic myocardial imaging and analysis of metabolic data using modeling methods for expanding our understanding of cardiac pathology are discussed. PMID:20559785

Phase Time and Envelope Time in Time-Distance Analysis and Acoustic Imaging

NASA Technical Reports Server (NTRS)

Chou, Dean-Yi; Duvall, Thomas L.; Sun, Ming-Tsung; Chang, Hsiang-Kuang; Jimenez, Antonio; Rabello-Soares, Maria Cristina; Ai, Guoxiang; Wang, Gwo-Ping; Goode Philip; Marquette, William;

Analysis of STM images with pure and CO-functionalized tips: A first-principles and experimental study

NASA Astrophysics Data System (ADS)

Gustafsson, Alexander; Okabayashi, Norio; Peronio, Angelo; Giessibl, Franz J.; Paulsson, Magnus

2017-08-01

We describe a first-principles method to calculate scanning tunneling microscopy (STM) images, and compare the results to well-characterized experiments combining STM with atomic force microscopy (AFM). The theory is based on density functional theory with a localized basis set, where the wave functions in the vacuum gap are computed by propagating the localized-basis wave functions into the gap using a real-space grid. Constant-height STM images are computed using Bardeen's approximation method, including averaging over the reciprocal space. We consider copper adatoms and single CO molecules adsorbed on Cu(111), scanned with a single-atom copper tip with and without CO functionalization. The calculated images agree with state-of-the-art experiments, where the atomic structure of the tip apex is determined by AFM. The comparison further allows for detailed interpretation of the STM images.

Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures.

PubMed

Wong, Kelvin K L; Wang, Defeng; Ko, Jacky K L; Mazumdar, Jagannath; Le, Thu-Thao; Ghista, Dhanjoo

2017-03-21

Cardiac dysfunction constitutes common cardiovascular health issues in the society, and has been an investigation topic of strong focus by researchers in the medical imaging community. Diagnostic modalities based on echocardiography, magnetic resonance imaging, chest radiography and computed tomography are common techniques that provide cardiovascular structural information to diagnose heart defects. However, functional information of cardiovascular flow, which can in fact be used to support the diagnosis of many cardiovascular diseases with a myriad of hemodynamics performance indicators, remains unexplored to its full potential. Some of these indicators constitute important cardiac functional parameters affecting the cardiovascular abnormalities. With the advancement of computer technology that facilitates high speed computational fluid dynamics, the realization of a support diagnostic platform of hemodynamics quantification and analysis can be achieved. This article reviews the state-of-the-art medical imaging and high fidelity multi-physics computational analyses that together enable reconstruction of cardiovascular structures and hemodynamic flow patterns within them, such as of the left ventricle (LV) and carotid bifurcations. The combined medical imaging and hemodynamic analysis enables us to study the mechanisms of cardiovascular disease-causing dysfunctions, such as how (1) cardiomyopathy causes left ventricular remodeling and loss of contractility leading to heart failure, and (2) modeling of LV construction and simulation of intra-LV hemodynamics can enable us to determine the optimum procedure of surgical ventriculation to restore its contractility and health This combined medical imaging and hemodynamics framework can potentially extend medical knowledge of cardiovascular defects and associated hemodynamic behavior and their surgical restoration, by means of an integrated medical image diagnostics and hemodynamic performance analysis framework.

An image analysis system for near-infrared (NIR) fluorescence lymph imaging

NASA Astrophysics Data System (ADS)

Zhang, Jingdan; Zhou, Shaohua Kevin; Xiang, Xiaoyan; Rasmussen, John C.; Sevick-Muraca, Eva M.

2011-03-01

Quantitative analysis of lymphatic function is crucial for understanding the lymphatic system and diagnosing the associated diseases. Recently, a near-infrared (NIR) fluorescence imaging system is developed for real-time imaging lymphatic propulsion by intradermal injection of microdose of a NIR fluorophore distal to the lymphatics of interest. However, the previous analysis software3, 4 is underdeveloped, requiring extensive time and effort to analyze a NIR image sequence. In this paper, we develop a number of image processing techniques to automate the data analysis workflow, including an object tracking algorithm to stabilize the subject and remove the motion artifacts, an image representation named flow map to characterize lymphatic flow more reliably, and an automatic algorithm to compute lymph velocity and frequency of propulsion. By integrating all these techniques to a system, the analysis workflow significantly reduces the amount of required user interaction and improves the reliability of the measurement.

Characterization and simulation of noise in PET images reconstructed with OSEM: Development of a method for the generation of synthetic images.

PubMed

Castro, P; Huerga, C; Chamorro, P; Garayoa, J; Roch, M; Pérez, L

2018-04-17

The goals of the study are to characterize imaging properties in 2D PET images reconstructed with the iterative algorithm ordered-subset expectation maximization (OSEM) and to propose a new method for the generation of synthetic images. The noise is analyzed in terms of its magnitude, spatial correlation, and spectral distribution through standard deviation, autocorrelation function, and noise power spectrum (NPS), respectively. Their variations with position and activity level are also analyzed. This noise analysis is based on phantom images acquired from 18 F uniform distributions. Experimental recovery coefficients of hot spheres in different backgrounds are employed to study the spatial resolution of the system through point spread function (PSF). The NPS and PSF functions provide the baseline for the proposed simulation method: convolution with PSF as kernel and noise addition from NPS. The noise spectral analysis shows that the main contribution is of random nature. It is also proven that attenuation correction does not alter noise texture but it modifies its magnitude. Finally, synthetic images of 2 phantoms, one of them an anatomical brain, are quantitatively compared with experimental images showing a good agreement in terms of pixel values and pixel correlations. Thus, the contrast to noise ratio for the biggest sphere in the NEMA IEC phantom is 10.7 for the synthetic image and 8.8 for the experimental image. The properties of the analyzed OSEM-PET images can be described by NPS and PSF functions. Synthetic images, even anatomical ones, are successfully generated by the proposed method based on the NPS and PSF. Copyright © 2018 Sociedad Española de Medicina Nuclear e Imagen Molecular. Publicado por Elsevier España, S.L.U. All rights reserved.

Structural and functional brain changes in early- and mid-stage primary open-angle glaucoma using voxel-based morphometry and functional magnetic resonance imaging.

PubMed

Jiang, Ming-Ming; Zhou, Qing; Liu, Xiao-Yong; Shi, Chang-Zheng; Chen, Jian; Huang, Xiang-He

2017-03-01

To investigate structural and functional brain changes in patients with primary open-angle glaucoma (POAG) by using voxel-based morphometry based on diffeomorphic anatomical registration through exponentiated Lie algebra (VBM-DARTEL) and blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI), respectively.Thirteen patients diagnosed with POAG and 13 age- and sex-matched healthy controls were enrolled in the study. For each participant, high-resolution structural brain imaging and blood flow imaging were acquired on a 3.0-Tesla magnetic resonance imaging (MRI) scanner. Structural and functional changes between the POAG and control groups were analyzed. An analysis was carried out to identify correlations between structural and functional changes acquired in the previous analysis and the retinal nerve fiber layer (RNFL).Patients in the POAG group showed a significant (P < 0.001) volume increase in the midbrain, left brainstem, frontal gyrus, cerebellar vermis, left inferior parietal lobule, caudate nucleus, thalamus, precuneus, and Brodmann areas 7, 18, and 46. Moreover, significant (P < 0.001) BOLD signal changes were observed in the right supramarginal gyrus, frontal gyrus, superior frontal gyrus, left inferior parietal lobule, left cuneus, and left midcingulate area; many of these regions had high correlations with the RNFL.Patients with POAG undergo widespread and complex changes in cortical brain structure and blood flow. (ClinicalTrials.gov number: NCT02570867).

Application of Image Analysis for Characterization of Spatial Arrangements of Features in Microstructure

NASA Technical Reports Server (NTRS)

Louis, Pascal; Gokhale, Arun M.

1995-01-01

A number of microstructural processes are sensitive to the spatial arrangements of features in microstructure. However, very little attention has been given in the past to the experimental measurements of the descriptors of microstructural distance distributions due to the lack of practically feasible methods. We present a digital image analysis procedure to estimate the micro-structural distance distributions. The application of the technique is demonstrated via estimation of K function, radial distribution function, and nearest-neighbor distribution function of hollow spherical carbon particulates in a polymer matrix composite, observed in a metallographic section.

On an image reconstruction method for ECT

NASA Astrophysics Data System (ADS)

Sasamoto, Akira; Suzuki, Takayuki; Nishimura, Yoshihiro

2007-04-01

An image by Eddy Current Testing(ECT) is a blurred image to original flaw shape. In order to reconstruct fine flaw image, a new image reconstruction method has been proposed. This method is based on an assumption that a very simple relationship between measured data and source were described by a convolution of response function and flaw shape. This assumption leads to a simple inverse analysis method with deconvolution.In this method, Point Spread Function (PSF) and Line Spread Function(LSF) play a key role in deconvolution processing. This study proposes a simple data processing to determine PSF and LSF from ECT data of machined hole and line flaw. In order to verify its validity, ECT data for SUS316 plate(200x200x10mm) with artificial machined hole and notch flaw had been acquired by differential coil type sensors(produced by ZETEC Inc). Those data were analyzed by the proposed method. The proposed method restored sharp discrete multiple hole image from interfered data by multiple holes. Also the estimated width of line flaw has been much improved compared with original experimental data. Although proposed inverse analysis strategy is simple and easy to implement, its validity to holes and line flaw have been shown by many results that much finer image than original image have been reconstructed.

Simulation of realistic abnormal SPECT brain perfusion images: application in semi-quantitative analysis

NASA Astrophysics Data System (ADS)

Ward, T.; Fleming, J. S.; Hoffmann, S. M. A.; Kemp, P. M.

2005-11-01

Simulation is useful in the validation of functional image analysis methods, particularly when considering the number of analysis techniques currently available lacking thorough validation. Problems exist with current simulation methods due to long run times or unrealistic results making it problematic to generate complete datasets. A method is presented for simulating known abnormalities within normal brain SPECT images using a measured point spread function (PSF), and incorporating a stereotactic atlas of the brain for anatomical positioning. This allows for the simulation of realistic images through the use of prior information regarding disease progression. SPECT images of cerebral perfusion have been generated consisting of a control database and a group of simulated abnormal subjects that are to be used in a UK audit of analysis methods. The abnormality is defined in the stereotactic space, then transformed to the individual subject space, convolved with a measured PSF and removed from the normal subject image. The dataset was analysed using SPM99 (Wellcome Department of Imaging Neuroscience, University College, London) and the MarsBaR volume of interest (VOI) analysis toolbox. The results were evaluated by comparison with the known ground truth. The analysis showed improvement when using a smoothing kernel equal to system resolution over the slightly larger kernel used routinely. Significant correlation was found between effective volume of a simulated abnormality and the detected size using SPM99. Improvements in VOI analysis sensitivity were found when using the region median over the region mean. The method and dataset provide an efficient methodology for use in the comparison and cross validation of semi-quantitative analysis methods in brain SPECT, and allow the optimization of analysis parameters.

A Computational Observer For Performing Contrast-Detail Analysis Of Ultrasound Images

NASA Astrophysics Data System (ADS)

Lopez, H.; Loew, M. H.

1988-06-01

Contrast-Detail (C/D) analysis allows the quantitative determination of an imaging system's ability to display a range of varying-size targets as a function of contrast. Using this technique, a contrast-detail plot is obtained which can, in theory, be used to compare image quality from one imaging system to another. The C/D plot, however, is usually obtained by using data from human observer readings. We have shown earlier(7) that the performance of human observers in the task of threshold detection of simulated lesions embedded in random ultrasound noise is highly inaccurate and non-reproducible for untrained observers. We present an objective, computational method for the determination of the C/D curve for ultrasound images. This method utilizes digital images of the C/D phantom developed at CDRH, and lesion-detection algorithms that simulate the Bayesian approach using the likelihood function for an ideal observer. We present the results of this method, and discuss the relationship to the human observer and to the comparability of image quality between systems.

3D Slicer as an Image Computing Platform for the Quantitative Imaging Network

PubMed Central

Fedorov, Andriy; Beichel, Reinhard; Kalpathy-Cramer, Jayashree; Finet, Julien; Fillion-Robin, Jean-Christophe; Pujol, Sonia; Bauer, Christian; Jennings, Dominique; Fennessy, Fiona; Sonka, Milan; Buatti, John; Aylward, Stephen; Miller, James V.; Pieper, Steve; Kikinis, Ron

2012-01-01

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm, and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer. PMID:22770690

Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping1[C][W][OPEN

PubMed Central

Klukas, Christian; Chen, Dijun; Pape, Jean-Michel

2014-01-01

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable. PMID:24760818

Image-derived input function with factor analysis and a-priori information.

PubMed

Simončič, Urban; Zanotti-Fregonara, Paolo

2015-02-01

Quantitative PET studies often require the cumbersome and invasive procedure of arterial cannulation to measure the input function. This study sought to minimize the number of necessary blood samples by developing a factor-analysis-based image-derived input function (IDIF) methodology for dynamic PET brain studies. IDIF estimation was performed as follows: (a) carotid and background regions were segmented manually on an early PET time frame; (b) blood-weighted and tissue-weighted time-activity curves (TACs) were extracted with factor analysis; (c) factor analysis results were denoised and scaled using the voxels with the highest blood signal; (d) using population data and one blood sample at 40 min, whole-blood TAC was estimated from postprocessed factor analysis results; and (e) the parent concentration was finally estimated by correcting the whole-blood curve with measured radiometabolite concentrations. The methodology was tested using data from 10 healthy individuals imaged with [(11)C](R)-rolipram. The accuracy of IDIFs was assessed against full arterial sampling by comparing the area under the curve of the input functions and by calculating the total distribution volume (VT). The shape of the image-derived whole-blood TAC matched the reference arterial curves well, and the whole-blood area under the curves were accurately estimated (mean error 1.0±4.3%). The relative Logan-V(T) error was -4.1±6.4%. Compartmental modeling and spectral analysis gave less accurate V(T) results compared with Logan. A factor-analysis-based IDIF for [(11)C](R)-rolipram brain PET studies that relies on a single blood sample and population data can be used for accurate quantification of Logan-V(T) values.

Correspondence of the brain's functional architecture during activation and rest.

PubMed

Smith, Stephen M; Fox, Peter T; Miller, Karla L; Glahn, David C; Fox, P Mickle; Mackay, Clare E; Filippini, Nicola; Watkins, Kate E; Toro, Roberto; Laird, Angela R; Beckmann, Christian F

2009-08-04

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is "at rest." In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically "active" even when at "rest."

Tensor Based Representation and Analysis of Diffusion-Weighted Magnetic Resonance Images

ERIC Educational Resources Information Center

Barmpoutis, Angelos

2009-01-01

Cartesian tensor bases have been widely used to model spherical functions. In medical imaging, tensors of various orders can approximate the diffusivity function at each voxel of a diffusion-weighted MRI data set. This approximation produces tensor-valued datasets that contain information about the underlying local structure of the scanned tissue.…

NiftyNet: a deep-learning platform for medical imaging.

PubMed

Gibson, Eli; Li, Wenqi; Sudre, Carole; Fidon, Lucas; Shakir, Dzhoshkun I; Wang, Guotai; Eaton-Rosen, Zach; Gray, Robert; Doel, Tom; Hu, Yipeng; Whyntie, Tom; Nachev, Parashkev; Modat, Marc; Barratt, Dean C; Ourselin, Sébastien; Cardoso, M Jorge; Vercauteren, Tom

2018-05-01

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Wihardi, Y.; Setiawan, W.; Nugraha, E.

2018-01-01

On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

Image-Processing Program

NASA Technical Reports Server (NTRS)

Roth, D. J.; Hull, D. R.

1994-01-01

IMAGEP manipulates digital image data to effect various processing, analysis, and enhancement functions. It is keyboard-driven program organized into nine subroutines. Within subroutines are sub-subroutines also selected via keyboard. Algorithm has possible scientific, industrial, and biomedical applications in study of flows in materials, analysis of steels and ores, and pathology, respectively.

LAS - LAND ANALYSIS SYSTEM, VERSION 5.0

NASA Technical Reports Server (NTRS)

Pease, P. B.

1994-01-01

The Land Analysis System (LAS) is an image analysis system designed to manipulate and analyze digital data in raster format and provide the user with a wide spectrum of functions and statistical tools for analysis. LAS offers these features under VMS with optional image display capabilities for IVAS and other display devices as well as the X-Windows environment. LAS provides a flexible framework for algorithm development as well as for the processing and analysis of image data. Users may choose between mouse-driven commands or the traditional command line input mode. LAS functions include supervised and unsupervised image classification, film product generation, geometric registration, image repair, radiometric correction and image statistical analysis. Data files accepted by LAS include formats such as Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Advanced Very High Resolution Radiometer (AVHRR). The enhanced geometric registration package now includes both image to image and map to map transformations. The over 200 LAS functions fall into image processing scenario categories which include: arithmetic and logical functions, data transformations, fourier transforms, geometric registration, hard copy output, image restoration, intensity transformation, multispectral and statistical analysis, file transfer, tape profiling and file management among others. Internal improvements to the LAS code have eliminated the VAX VMS dependencies and improved overall system performance. The maximum LAS image size has been increased to 20,000 lines by 20,000 samples with a maximum of 256 bands per image. The catalog management system used in earlier versions of LAS has been replaced by a more streamlined and maintenance-free method of file management. This system is not dependent on VAX/VMS and relies on file naming conventions alone to allow the use of identical LAS file names on different operating systems. While the LAS code has been improved, the original capabilities of the system have been preserved. These include maintaining associated image history, session logging, and batch, asynchronous and interactive mode of operation. The LAS application programs are integrated under version 4.1 of an interface called the Transportable Applications Executive (TAE). TAE 4.1 has four modes of user interaction: menu, direct command, tutor (or help), and dynamic tutor. In addition TAE 4.1 allows the operation of LAS functions using mouse-driven commands under the TAE-Facelift environment provided with TAE 4.1. These modes of operation allow users, from the beginner to the expert, to exercise specific application options. LAS is written in C-language and FORTRAN 77 for use with DEC VAX computers running VMS with approximately 16Mb of physical memory. This program runs under TAE 4.1. Since TAE 4.1 is not a current version of TAE, TAE 4.1 is included within the LAS distribution. Approximately 130,000 blocks (65Mb) of disk storage space are necessary to store the source code and files generated by the installation procedure for LAS and 44,000 blocks (22Mb) of disk storage space are necessary for TAE 4.1 installation. The only other dependencies for LAS are the subroutine libraries for the specific display device(s) that will be used with LAS/DMS (e.g. X-Windows and/or IVAS). The standard distribution medium for LAS is a set of two 9track 6250 BPI magnetic tapes in DEC VAX BACKUP format. It is also available on a set of two TK50 tape cartridges in DEC VAX BACKUP format. This program was developed in 1986 and last updated in 1992.

Oufti: An integrated software package for high-accuracy, high-throughput quantitative microscopy analysis

PubMed Central

Paintdakhi, Ahmad; Parry, Bradley; Campos, Manuel; Irnov, Irnov; Elf, Johan; Surovtsev, Ivan; Jacobs-Wagner, Christine

2016-01-01

Summary With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re-emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today’s single-cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand-alone, open-source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non-diffraction-limited fluorescence signals, and is scalable for high-throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis, and post-processing analysis, makes the software broadly accessible to users irrespective of their computational skills. PMID:26538279

Automated method for relating regional pulmonary structure and function: integration of dynamic multislice CT and thin-slice high-resolution CT

NASA Astrophysics Data System (ADS)

Tajik, Jehangir K.; Kugelmass, Steven D.; Hoffman, Eric A.

1993-07-01

We have developed a method utilizing x-ray CT for relating pulmonary perfusion to global and regional anatomy, allowing for detailed study of structure to function relationships. A thick slice, high temporal resolution mode is used to follow a bolus contrast agent for blood flow evaluation and is fused with a high spatial resolution, thin slice mode to obtain structure- function detail. To aid analysis of blood flow, we have developed a software module, for our image analysis package (VIDA), to produce the combined structure-function image. Color coded images representing blood flow, mean transit time, regional tissue content, regional blood volume, regional air content, etc. are generated and imbedded in the high resolution volume image. A text file containing these values along with a voxel's 3-D coordinates is also generated. User input can be minimized to identifying the location of the pulmonary artery from which the input function to a blood flow model is derived. Any flow model utilizing one input and one output function can be easily added to a user selectable list. We present examples from our physiologic based research findings to demonstrate the strengths of combining dynamic CT and HRCT relative to other scanning modalities to uniquely characterize pulmonary normal and pathophysiology.

Studies of EGRET sources with a novel image restoration technique

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

Tajima, Hiroyasu; Cohen-Tanugi, Johann; Kamae, Tuneyoshi

2007-07-12

We have developed an image restoration technique based on the Richardson-Lucy algorithm optimized for GLAST-LAT image analysis. Our algorithm is original since it utilizes the PSF (point spread function) that is calculated for each event. This is critical for EGRET and GLAST-LAT image analysis since the PSF depends on the energy and angle of incident gamma-rays and varies by more than one order of magnitude. EGRET and GLAST-LAT image analysis also faces Poisson noise due to low photon statistics. Our technique incorporates wavelet filtering to minimize noise effects. We present studies of EGRET sources using this novel image restoration techniquemore » for possible identification of extended gamma-ray sources.« less

Time evolution of surface chlorophyll patterns from cross-spectrum analysis of satellite color images

NASA Technical Reports Server (NTRS)

Denman, Kenneth L.; Abbott, Mark R.

1988-01-01

The rate of decorrelation of surface chlorophyll patterns as a function of the time separation between pairs of images was determined from two sequences of CZCS images of the Pacific Ocean area adjacent to Vancouver Island, Canada; cloud-free subareas were selected that were common to several images separated in time by 1-17 days. Image pairs were subjected to two-dimensional autospectrum and cross-spectrum analysis in an array processor, and squared coherence estimates found for several wave bands were plotted against time separation, in analogy with a time-lagged cross correlation function. It was found that, for wavelengths of 50-150 km, significant coherence was lost after 7-10 days, while for wavelengths of 25-50 km, significant coherence was lost after only 5-7 days. In both cases, offshore regions maintained coherence longer than coastal regions.

A study on quantifying COPD severity by combining pulmonary function tests and CT image analysis

NASA Astrophysics Data System (ADS)

Nimura, Yukitaka; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

2011-03-01

This paper describes a novel method that can evaluate chronic obstructive pulmonary disease (COPD) severity by combining measurements of pulmonary function tests and measurements obtained from CT image analysis. There is no cure for COPD. However, with regular medical care and consistent patient compliance with treatments and lifestyle changes, the symptoms of COPD can be minimized and progression of the disease can be slowed. Therefore, many diagnosis methods based on CT image analysis have been proposed for quantifying COPD. Most of diagnosis methods for COPD extract the lesions as low-attenuation areas (LAA) by thresholding and evaluate the COPD severity by calculating the LAA in the lung (LAA%). However, COPD is usually the result of a combination of two conditions, emphysema and chronic obstructive bronchitis. Therefore, the previous methods based on only LAA% do not work well. The proposed method utilizes both of information including the measurements of pulmonary function tests and the results of the chest CT image analysis to evaluate the COPD severity. In this paper, we utilize a multi-class AdaBoost to combine both of information and classify the COPD severity into five stages automatically. The experimental results revealed that the accuracy rate of the proposed method was 88.9% (resubstitution scheme) and 64.4% (leave-one-out scheme).

Computer-based quantitative computed tomography image analysis in idiopathic pulmonary fibrosis: A mini review.

PubMed

Ohkubo, Hirotsugu; Nakagawa, Hiroaki; Niimi, Akio

2018-01-01

Idiopathic pulmonary fibrosis (IPF) is the most common type of progressive idiopathic interstitial pneumonia in adults. Many computer-based image analysis methods of chest computed tomography (CT) used in patients with IPF include the mean CT value of the whole lungs, density histogram analysis, density mask technique, and texture classification methods. Most of these methods offer good assessment of pulmonary functions, disease progression, and mortality. Each method has merits that can be used in clinical practice. One of the texture classification methods is reported to be superior to visual CT scoring by radiologist for correlation with pulmonary function and prediction of mortality. In this mini review, we summarize the current literature on computer-based CT image analysis of IPF and discuss its limitations and several future directions. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Graphical user interface for image acquisition and processing

DOEpatents

Goldberg, Kenneth A.

2002-01-01

An event-driven GUI-based image acquisition interface for the IDL programming environment designed for CCD camera control and image acquisition directly into the IDL environment where image manipulation and data analysis can be performed, and a toolbox of real-time analysis applications. Running the image acquisition hardware directly from IDL removes the necessity of first saving images in one program and then importing the data into IDL for analysis in a second step. Bringing the data directly into IDL creates an opportunity for the implementation of IDL image processing and display functions in real-time. program allows control over the available charge coupled device (CCD) detector parameters, data acquisition, file saving and loading, and image manipulation and processing, all from within IDL. The program is built using IDL's widget libraries to control the on-screen display and user interface.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

PubMed Central

Soares, José M.; Magalhães, Ricardo; Moreira, Pedro S.; Sousa, Alexandre; Ganz, Edward; Sampaio, Adriana; Alves, Victor; Marques, Paulo; Sousa, Nuno

2016-01-01

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community. PMID:27891073

Monitoring of human brain functions in risk decision-making task by diffuse optical tomography using voxel-wise general linear model

NASA Astrophysics Data System (ADS)

Lin, Zi-Jing; Li, Lin; Cazzell, Marry; Liu, Hanli

2013-03-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique which measures the hemodynamic changes that reflect the brain activity. Diffuse optical tomography (DOT), a variant of fNIRS with multi-channel NIRS measurements, has demonstrated capability of three dimensional (3D) reconstructions of hemodynamic changes due to the brain activity. Conventional method of DOT image analysis to define the brain activation is based upon the paired t-test between two different states, such as resting-state versus task-state. However, it has limitation because the selection of activation and post-activation period is relatively subjective. General linear model (GLM) based analysis can overcome this limitation. In this study, we combine the 3D DOT image reconstruction with GLM-based analysis (i.e., voxel-wise GLM analysis) to investigate the brain activity that is associated with the risk-decision making process. Risk decision-making is an important cognitive process and thus is an essential topic in the field of neuroscience. The balloon analogue risk task (BART) is a valid experimental model and has been commonly used in behavioral measures to assess human risk taking action and tendency while facing risks. We have utilized the BART paradigm with a blocked design to investigate brain activations in the prefrontal and frontal cortical areas during decision-making. Voxel-wise GLM analysis was performed on 18human participants (10 males and 8females).In this work, we wish to demonstrate the feasibility of using voxel-wise GLM analysis to image and study cognitive functions in response to risk decision making by DOT. Results have shown significant changes in the dorsal lateral prefrontal cortex (DLPFC) during the active choice mode and a different hemodynamic pattern between genders, which are in good agreements with published literatures in functional magnetic resonance imaging (fMRI) and fNIRS studies.

Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

NASA Astrophysics Data System (ADS)

Wang, Bingyuan; Zhang, Yao; Liu, Dongyuan; Ding, Xuemei; Dan, Mai; Pan, Tiantian; Wang, Yihan; Li, Jiao; Zhou, Zhongxing; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2018-02-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.

IMAGEP - A FORTRAN ALGORITHM FOR DIGITAL IMAGE PROCESSING

NASA Technical Reports Server (NTRS)

Roth, D. J.

1994-01-01

IMAGEP is a FORTRAN computer algorithm containing various image processing, analysis, and enhancement functions. It is a keyboard-driven program organized into nine subroutines. Within the subroutines are other routines, also, selected via keyboard. Some of the functions performed by IMAGEP include digitization, storage and retrieval of images; image enhancement by contrast expansion, addition and subtraction, magnification, inversion, and bit shifting; display and movement of cursor; display of grey level histogram of image; and display of the variation of grey level intensity as a function of image position. This algorithm has possible scientific, industrial, and biomedical applications in material flaw studies, steel and ore analysis, and pathology, respectively. IMAGEP is written in VAX FORTRAN for DEC VAX series computers running VMS. The program requires the use of a Grinnell 274 image processor which can be obtained from Mark McCloud Associates, Campbell, CA. An object library of the required GMR series software is included on the distribution media. IMAGEP requires 1Mb of RAM for execution. The standard distribution medium for this program is a 1600 BPI 9track magnetic tape in VAX FILES-11 format. It is also available on a TK50 tape cartridge in VAX FILES-11 format. This program was developed in 1991. DEC, VAX, VMS, and TK50 are trademarks of Digital Equipment Corporation.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

PubMed Central

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-01-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications. PMID:26525841

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope.

PubMed

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-03

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

NASA Astrophysics Data System (ADS)

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Image encryption algorithm based on multiple mixed hash functions and cyclic shift

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Zhu, Xiaoqiang; Wu, Xiangjun; Zhang, Yingqian

2018-08-01

This paper proposes a new one-time pad scheme for chaotic image encryption that is based on the multiple mixed hash functions and the cyclic-shift function. The initial value is generated using both information of the plaintext image and the chaotic sequences, which are calculated from the SHA1 and MD5 hash algorithms. The scrambling sequences are generated by the nonlinear equations and logistic map. This paper aims to improve the deficiencies of traditional Baptista algorithms and its improved algorithms. We employ the cyclic-shift function and piece-wise linear chaotic maps (PWLCM), which give each shift number the characteristics of chaos, to diffuse the image. Experimental results and security analysis show that the new scheme has better security and can resist common attacks.

Androgen Receptor Functional Analyses by High Throughput Imaging: Determination of Ligand, Cell Cycle, and Mutation-Specific Effects

PubMed Central

Szafran, Adam T.; Szwarc, Maria; Marcelli, Marco; Mancini, Michael A.

2008-01-01

Background Understanding how androgen receptor (AR) function is modulated by exposure to steroids, growth factors or small molecules can have important mechanistic implications for AR-related disease therapies (e.g., prostate cancer, androgen insensitivity syndrome, AIS), and in the analysis of environmental endocrine disruptors. Methodology/Principal Findings We report the development of a high throughput (HT) image-based assay that quantifies AR subcellular and subnuclear distribution, and transcriptional reporter gene activity on a cell-by-cell basis. Furthermore, simultaneous analysis of DNA content allowed determination of cell cycle position and permitted the analysis of cell cycle dependent changes in AR function in unsynchronized cell populations. Assay quality for EC50 coefficients of variation were 5–24%, with Z' values reaching 0.91. This was achieved by the selective analysis of cells expressing physiological levels of AR, important because minor over-expression resulted in elevated nuclear speckling and decreased transcriptional reporter gene activity. A small screen of AR-binding ligands, including known agonists, antagonists, and endocrine disruptors, demonstrated that nuclear translocation and nuclear “speckling” were linked with transcriptional output, and specific ligands were noted to differentially affect measurements for wild type versus mutant AR, suggesting differing mechanisms of action. HT imaging of patient-derived AIS mutations demonstrated a proof-of-principle personalized medicine approach to rapidly identify ligands capable of restoring multiple AR functions. Conclusions/Significance HT imaging-based multiplex screening will provide a rapid, systems-level analysis of compounds/RNAi that may differentially affect wild type AR or clinically relevant AR mutations. PMID:18978937

The role of image registration in brain mapping

PubMed Central

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Image correlation microscopy for uniform illumination.

PubMed

Gaborski, T R; Sealander, M N; Ehrenberg, M; Waugh, R E; McGrath, J L

2010-01-01

Image cross-correlation microscopy is a technique that quantifies the motion of fluorescent features in an image by measuring the temporal autocorrelation function decay in a time-lapse image sequence. Image cross-correlation microscopy has traditionally employed laser-scanning microscopes because the technique emerged as an extension of laser-based fluorescence correlation spectroscopy. In this work, we show that image correlation can also be used to measure fluorescence dynamics in uniform illumination or wide-field imaging systems and we call our new approach uniform illumination image correlation microscopy. Wide-field microscopy is not only a simpler, less expensive imaging modality, but it offers the capability of greater temporal resolution over laser-scanning systems. In traditional laser-scanning image cross-correlation microscopy, lateral mobility is calculated from the temporal de-correlation of an image, where the characteristic length is the illuminating laser beam width. In wide-field microscopy, the diffusion length is defined by the feature size using the spatial autocorrelation function. Correlation function decay in time occurs as an object diffuses from its original position. We show that theoretical and simulated comparisons between Gaussian and uniform features indicate the temporal autocorrelation function depends strongly on particle size and not particle shape. In this report, we establish the relationships between the spatial autocorrelation function feature size, temporal autocorrelation function characteristic time and the diffusion coefficient for uniform illumination image correlation microscopy using analytical, Monte Carlo and experimental validation with particle tracking algorithms. Additionally, we demonstrate uniform illumination image correlation microscopy analysis of adhesion molecule domain aggregation and diffusion on the surface of human neutrophils.

On-orbit point spread function estimation for THEOS imaging system

NASA Astrophysics Data System (ADS)

Khetkeeree, Suphongsa; Liangrocapart, Sompong

2018-03-01

In this paper, we present two approaches for net Point Spread Function (net-PSF) estimation of Thailand Earth Observation System (THEOS) imaging system. In the first approach, we estimate the net- PSF by employing the specification information of the satellite. The analytic model of the net- PSF based on the simple model of push-broom imaging system. This model consists of a scanner, optical system, detector and electronics system. The mathematical PSF model of each component is demonstrated in spatial domain. In the second approach, the specific target images from THEOS imaging system are analyzed to determine the net-PSF. For panchromatic imaging system, the images of the checkerboard target at Salon de Provence airport are used to analysis the net-PSF by slant-edge method. For multispectral imaging system, the new man-made targets are proposed. It is a pier bridge in Lamchabang, Chonburi, Thailand. This place has had a lot of bridges which have several width sizes and orientation. The pulse method is used to analysis the images of this bridge for estimating the net-PSF. Finally, the Full Width at Half Maximums (FWHMs) of the net-PSF of both approaches is compared. The results show that both approaches coincide and all Modulation Transfer Functions (MTFs) at Nyquist of both approaches are better than the requirement. However, the FWHM of multispectral system more deviate than panchromatic system, because the targets are not specially constructed for estimating the characteristics of the satellite imaging system.

Optical coherence tomography imaging based on non-harmonic analysis

NASA Astrophysics Data System (ADS)

Cao, Xu; Hirobayashi, Shigeki; Chong, Changho; Morosawa, Atsushi; Totsuka, Koki; Suzuki, Takuya

2009-11-01

A new processing technique called Non-Harmonic Analysis (NHA) is proposed for OCT imaging. Conventional Fourier-Domain OCT relies on the FFT calculation which depends on the window function and length. Axial resolution is counter proportional to the frame length of FFT that is limited by the swept range of the swept source in SS-OCT, or the pixel counts of CCD in SD-OCT degraded in FD-OCT. However, NHA process is intrinsically free from this trade-offs; NHA can resolve high frequency without being influenced by window function or frame length of sampled data. In this study, NHA process is explained and applied to OCT imaging and compared with OCT images based on FFT. In order to validate the benefit of NHA in OCT, we carried out OCT imaging based on NHA with the three different sample of onion-skin,human-skin and pig-eye. The results show that NHA process can realize practical image resolution that is equivalent to 100nm swept range only with less than half-reduced wavelength range.

FUNSTAT and statistical image representations

NASA Technical Reports Server (NTRS)

Parzen, E.

1983-01-01

General ideas of functional statistical inference analysis of one sample and two samples, univariate and bivariate are outlined. ONESAM program is applied to analyze the univariate probability distributions of multi-spectral image data.

The Assessment of Neurological Systems with Functional Imaging

ERIC Educational Resources Information Center

Eidelberg, David

2007-01-01

In recent years a number of multivariate approaches have been introduced to map neural systems in health and disease. In this review, we focus on spatial covariance methods applied to functional imaging data to identify patterns of regional activity associated with behavior. In the rest state, this form of network analysis can be used to detect…

Methods for scalar-on-function regression.

PubMed

Reiss, Philip T; Goldsmith, Jeff; Shang, Han Lin; Ogden, R Todd

2017-08-01

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.

The Cerefy Neuroradiology Atlas: a Talairach-Tournoux atlas-based tool for analysis of neuroimages available over the internet.

PubMed

Nowinski, Wieslaw L; Belov, Dmitry

2003-09-01

The article introduces an atlas-assisted method and a tool called the Cerefy Neuroradiology Atlas (CNA), available over the Internet for neuroradiology and human brain mapping. The CNA contains an enhanced, extended, and fully segmented and labeled electronic version of the Talairach-Tournoux brain atlas, including parcelated gyri and Brodmann's areas. To our best knowledge, this is the first online, publicly available application with the Talairach-Tournoux atlas. The process of atlas-assisted neuroimage analysis is done in five steps: image data loading, Talairach landmark setting, atlas normalization, image data exploration and analysis, and result saving. Neuroimage analysis is supported by a near-real-time, atlas-to-data warping based on the Talairach transformation. The CNA runs on multiple platforms; is able to process simultaneously multiple anatomical and functional data sets; and provides functions for a rapid atlas-to-data registration, interactive structure labeling and annotating, and mensuration. It is also empowered with several unique features, including interactive atlas warping facilitating fine tuning of atlas-to-data fit, navigation on the triplanar formed by the image data and the atlas, multiple-images-in-one display with interactive atlas-anatomy-function blending, multiple label display, and saving of labeled and annotated image data. The CNA is useful for fast atlas-assisted analysis of neuroimage data sets. It increases accuracy and reduces time in localization analysis of activation regions; facilitates to communicate the information on the interpreted scans from the neuroradiologist to other clinicians and medical students; increases the neuroradiologist's confidence in terms of anatomy and spatial relationships; and serves as a user-friendly, public domain tool for neuroeducation. At present, more than 700 users from five continents have subscribed to the CNA.

Histology image analysis for carcinoma detection and grading

PubMed Central

He, Lei; Long, L. Rodney; Antani, Sameer; Thoma, George R.

2012-01-01

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems. PMID:22436890

[Three-dimensional reconstruction of functional brain images].

PubMed

Inoue, M; Shoji, K; Kojima, H; Hirano, S; Naito, Y; Honjo, I

1999-08-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface model is the most common method of three-dimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

Neural correlates of monocular and binocular depth cues based on natural images: a LORETA analysis.

PubMed

Fischmeister, Florian Ph S; Bauer, Herbert

2006-10-01

Functional imaging studies investigating perception of depth rely solely on one type of depth cue based on non-natural stimulus material. To overcome these limitations and to provide a more realistic and complete set of depth cues natural stereoscopic images were used in this study. Using slow cortical potentials and source localization we aimed to identify the neural correlates of monocular and binocular depth cues. This study confirms and extends functional imaging studies, showing that natural images provide a good, reliable, and more realistic alternative to artificial stimuli, and demonstrates the possibility to separate the processing of different depth cues.

POLYSITE - An interactive package for the selection and refinement of Landsat image training sites

NASA Technical Reports Server (NTRS)

Mack, Marilyn J. P.

1986-01-01

A versatile multifunction package, POLYSITE, developed for Goddard's Land Analysis System, is described which simplifies the process of interactively selecting and correcting the sites used to study Landsat TM and MSS images. Image switching between the zoomed and nonzoomed image, color and shape cursor change and location display, and bit plane erase or color change, are global functions which are active at all times. Local functions possibly include manipulation of intensive study areas, new site definition, mensuration, and new image copying. The program is illustrated with the example of a full TM maser scene of metropolitan Washington, DC.

Multimedia Image Technology and Computer Aided Manufacturing Engineering Analysis

NASA Astrophysics Data System (ADS)

Nan, Song

2018-03-01

Since the reform and opening up, with the continuous development of science and technology in China, more and more advanced science and technology have emerged under the trend of diversification. Multimedia imaging technology, for example, has a significant and positive impact on computer aided manufacturing engineering in China. From the perspective of scientific and technological advancement and development, the multimedia image technology has a very positive influence on the application and development of computer-aided manufacturing engineering, whether in function or function play. Therefore, this paper mainly starts from the concept of multimedia image technology to analyze the application of multimedia image technology in computer aided manufacturing engineering.

Quantitative real-time analysis of collective cancer invasion and dissemination

NASA Astrophysics Data System (ADS)

Ewald, Andrew J.

2015-05-01

A grand challenge in biology is to understand the cellular and molecular basis of tissue and organ level function in mammals. The ultimate goals of such efforts are to explain how organs arise in development from the coordinated actions of their constituent cells and to determine how molecularly regulated changes in cell behavior alter the structure and function of organs during disease processes. Two major barriers stand in the way of achieving these goals: the relative inaccessibility of cellular processes in mammals and the daunting complexity of the signaling environment inside an intact organ in vivo. To overcome these barriers, we have developed a suite of tissue isolation, three dimensional (3D) culture, genetic manipulation, nanobiomaterials, imaging, and molecular analysis techniques to enable the real-time study of cell biology within intact tissues in physiologically relevant 3D environments. This manuscript introduces the rationale for 3D culture, reviews challenges to optical imaging in these cultures, and identifies current limitations in the analysis of complex experimental designs that could be overcome with improved imaging, imaging analysis, and automated classification of the results of experimental interventions.

Imaging Lung Function in Mice Using SPECT/CT and Per-Voxel Analysis

PubMed Central

Jobse, Brian N.; Rhem, Rod G.; McCurry, Cory A. J. R.; Wang, Iris Q.; Labiris, N. Renée

2012-01-01

Chronic lung disease is a major worldwide health concern but better tools are required to understand the underlying pathologies. Ventilation/perfusion (V/Q) single photon emission computed tomography (SPECT) with per-voxel analysis allows for non-invasive measurement of regional lung function. A clinically adapted V/Q methodology was used in healthy mice to investigate V/Q relationships. Twelve week-old mice were imaged to describe normal lung function while 36 week-old mice were imaged to determine how age affects V/Q. Mice were ventilated with Technegas™ and injected with 99mTc-macroaggregated albumin to trace ventilation and perfusion, respectively. For both processes, SPECT and CT images were acquired, co-registered, and quantitatively analyzed. On a per-voxel basis, ventilation and perfusion were moderately correlated (R = 0.58±0.03) in 12 week old animals and a mean log(V/Q) ratio of −0.07±0.01 and standard deviation of 0.36±0.02 were found, defining the extent of V/Q matching. In contrast, 36 week old animals had significantly increased levels of V/Q mismatching throughout the periphery of the lung. Measures of V/Q were consistent across healthy animals and differences were observed with age demonstrating the capability of this technique in quantifying lung function. Per-voxel analysis and the ability to non-invasively assess lung function will aid in the investigation of chronic lung disease models and drug efficacy studies. PMID:22870297

An automated multi-scale network-based scheme for detection and location of seismic sources

NASA Astrophysics Data System (ADS)

Poiata, N.; Aden-Antoniow, F.; Satriano, C.; Bernard, P.; Vilotte, J. P.; Obara, K.

2017-12-01

We present a recently developed method - BackTrackBB (Poiata et al. 2016) - allowing to image energy radiation from different seismic sources (e.g., earthquakes, LFEs, tremors) in different tectonic environments using continuous seismic records. The method exploits multi-scale frequency-selective coherence in the wave field, recorded by regional seismic networks or local arrays. The detection and location scheme is based on space-time reconstruction of the seismic sources through an imaging function built from the sum of station-pair time-delay likelihood functions, projected onto theoretical 3D time-delay grids. This imaging function is interpreted as the location likelihood of the seismic source. A signal pre-processing step constructs a multi-band statistical representation of the non stationary signal, i.e. time series, by means of higher-order statistics or energy envelope characteristic functions. Such signal-processing is designed to detect in time signal transients - of different scales and a priori unknown predominant frequency - potentially associated with a variety of sources (e.g., earthquakes, LFE, tremors), and to improve the performance and the robustness of the detection-and-location location step. The initial detection-location, based on a single phase analysis with the P- or S-phase only, can then be improved recursively in a station selection scheme. This scheme - exploiting the 3-component records - makes use of P- and S-phase characteristic functions, extracted after a polarization analysis of the event waveforms, and combines the single phase imaging functions with the S-P differential imaging functions. The performance of the method is demonstrated here in different tectonic environments: (1) analysis of the one year long precursory phase of 2014 Iquique earthquake in Chile; (2) detection and location of tectonic tremor sources and low-frequency earthquakes during the multiple episodes of tectonic tremor activity in southwestern Japan.

Multifacet structure of observed reconstructed integral images.

PubMed

Martínez-Corral, Manuel; Javidi, Bahram; Martínez-Cuenca, Raúl; Saavedra, Genaro

2005-04-01

Three-dimensional images generated by an integral imaging system suffer from degradations in the form of grid of multiple facets. This multifacet structure breaks the continuity of the observed image and therefore reduces its visual quality. We perform an analysis of this effect and present the guidelines in the design of lenslet imaging parameters for optimization of viewing conditions with respect to the multifacet degradation. We consider the optimization of the system in terms of field of view, observer position and pupil function, lenslet parameters, and type of reconstruction. Numerical tests are presented to verify the theoretical analysis.

Developing Matlab scripts for image analysis and quality assessment

NASA Astrophysics Data System (ADS)

Vaiopoulos, A. D.

2011-11-01

Image processing is a very helpful tool in many fields of modern sciences that involve digital imaging examination and interpretation. Processed images however, often need to be correlated with the original image, in order to ensure that the resulting image fulfills its purpose. Aside from the visual examination, which is mandatory, image quality indices (such as correlation coefficient, entropy and others) are very useful, when deciding which processed image is the most satisfactory. For this reason, a single program (script) was written in Matlab language, which automatically calculates eight indices by utilizing eight respective functions (independent function scripts). The program was tested in both fused hyperspectral (Hyperion-ALI) and multispectral (ALI, Landsat) imagery and proved to be efficient. Indices were found to be in agreement with visual examination and statistical observations.

The optimal algorithm for Multi-source RS image fusion.

PubMed

Fu, Wei; Huang, Shui-Guang; Li, Zeng-Shun; Shen, Hao; Li, Jun-Shuai; Wang, Peng-Yuan

2016-01-01

In order to solve the issue which the fusion rules cannot be self-adaptively adjusted by using available fusion methods according to the subsequent processing requirements of Remote Sensing (RS) image, this paper puts forward GSDA (genetic-iterative self-organizing data analysis algorithm) by integrating the merit of genetic arithmetic together with the advantage of iterative self-organizing data analysis algorithm for multi-source RS image fusion. The proposed algorithm considers the wavelet transform of the translation invariance as the model operator, also regards the contrast pyramid conversion as the observed operator. The algorithm then designs the objective function by taking use of the weighted sum of evaluation indices, and optimizes the objective function by employing GSDA so as to get a higher resolution of RS image. As discussed above, the bullet points of the text are summarized as follows.•The contribution proposes the iterative self-organizing data analysis algorithm for multi-source RS image fusion.•This article presents GSDA algorithm for the self-adaptively adjustment of the fusion rules.•This text comes up with the model operator and the observed operator as the fusion scheme of RS image based on GSDA. The proposed algorithm opens up a novel algorithmic pathway for multi-source RS image fusion by means of GSDA.

iScreen: Image-Based High-Content RNAi Screening Analysis Tools.

PubMed

Zhong, Rui; Dong, Xiaonan; Levine, Beth; Xie, Yang; Xiao, Guanghua

2015-09-01

High-throughput RNA interference (RNAi) screening has opened up a path to investigating functional genomics in a genome-wide pattern. However, such studies are often restricted to assays that have a single readout format. Recently, advanced image technologies have been coupled with high-throughput RNAi screening to develop high-content screening, in which one or more cell image(s), instead of a single readout, were generated from each well. This image-based high-content screening technology has led to genome-wide functional annotation in a wider spectrum of biological research studies, as well as in drug and target discovery, so that complex cellular phenotypes can be measured in a multiparametric format. Despite these advances, data analysis and visualization tools are still largely lacking for these types of experiments. Therefore, we developed iScreen (image-Based High-content RNAi Screening Analysis Tool), an R package for the statistical modeling and visualization of image-based high-content RNAi screening. Two case studies were used to demonstrate the capability and efficiency of the iScreen package. iScreen is available for download on CRAN (http://cran.cnr.berkeley.edu/web/packages/iScreen/index.html). The user manual is also available as a supplementary document. © 2014 Society for Laboratory Automation and Screening.

Toward standardized quantitative image quality (IQ) assessment in computed tomography (CT): A comprehensive framework for automated and comparative IQ analysis based on ICRU Report 87.

PubMed

Pahn, Gregor; Skornitzke, Stephan; Schlemmer, Hans-Peter; Kauczor, Hans-Ulrich; Stiller, Wolfram

2016-01-01

Based on the guidelines from "Report 87: Radiation Dose and Image-quality Assessment in Computed Tomography" of the International Commission on Radiation Units and Measurements (ICRU), a software framework for automated quantitative image quality analysis was developed and its usability for a variety of scientific questions demonstrated. The extendable framework currently implements the calculation of the recommended Fourier image quality (IQ) metrics modulation transfer function (MTF) and noise-power spectrum (NPS), and additional IQ quantities such as noise magnitude, CT number accuracy, uniformity across the field-of-view, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of simulated lesions for a commercially available cone-beam phantom. Sample image data were acquired with different scan and reconstruction settings on CT systems from different manufacturers. Spatial resolution is analyzed in terms of edge-spread function, line-spread-function, and MTF. 3D NPS is calculated according to ICRU Report 87, and condensed to 2D and radially averaged 1D representations. Noise magnitude, CT numbers, and uniformity of these quantities are assessed on large samples of ROIs. Low-contrast resolution (CNR, SNR) is quantitatively evaluated as a function of lesion contrast and diameter. Simultaneous automated processing of several image datasets allows for straightforward comparative assessment. The presented framework enables systematic, reproducible, automated and time-efficient quantitative IQ analysis. Consistent application of the ICRU guidelines facilitates standardization of quantitative assessment not only for routine quality assurance, but for a number of research questions, e.g. the comparison of different scanner models or acquisition protocols, and the evaluation of new technology or reconstruction methods. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Determination of left ventricular volume, ejection fraction, and myocardial mass by real-time three-dimensional echocardiography

NASA Technical Reports Server (NTRS)

Qin, J. X.; Shiota, T.; Thomas, J. D.

2000-01-01

Reconstructed three-dimensional (3-D) echocardiography is an accurate and reproducible method of assessing left ventricular (LV) functions. However, it has limitations for clinical study due to the requirement of complex computer and echocardiographic analysis systems, electrocardiographic/respiratory gating, and prolonged imaging times. Real-time 3-D echocardiography has a major advantage of conveniently visualizing the entire cardiac anatomy in three dimensions and of potentially accurately quantifying LV volumes, ejection fractions, and myocardial mass in patients even in the presence of an LV aneurysm. Although the image quality of the current real-time 3-D echocardiographic methods is not optimal, its widespread clinical application is possible because of the convenient and fast image acquisition. We review real-time 3-D echocardiographic image acquisition and quantitative analysis for the evaluation of LV function and LV mass.

Determination of left ventricular volume, ejection fraction, and myocardial mass by real-time three-dimensional echocardiography.

PubMed

Qin, J X; Shiota, T; Thomas, J D

2000-11-01

Reconstructed three-dimensional (3-D) echocardiography is an accurate and reproducible method of assessing left ventricular (LV) functions. However, it has limitations for clinical study due to the requirement of complex computer and echocardiographic analysis systems, electrocardiographic/respiratory gating, and prolonged imaging times. Real-time 3-D echocardiography has a major advantage of conveniently visualizing the entire cardiac anatomy in three dimensions and of potentially accurately quantifying LV volumes, ejection fractions, and myocardial mass in patients even in the presence of an LV aneurysm. Although the image quality of the current real-time 3-D echocardiographic methods is not optimal, its widespread clinical application is possible because of the convenient and fast image acquisition. We review real-time 3-D echocardiographic image acquisition and quantitative analysis for the evaluation of LV function and LV mass.

Fuzzy membership functions for analysis of high-resolution CT images of diffuse pulmonary diseases.

PubMed

Almeida, Eliana; Rangayyan, Rangaraj M; Azevedo-Marques, Paulo M

2015-08-01

We propose the use of fuzzy membership functions to analyze images of diffuse pulmonary diseases (DPDs) based on fractal and texture features. The features were extracted from preprocessed regions of interest (ROIs) selected from high-resolution computed tomography images. The ROIs represent five different patterns of DPDs and normal lung tissue. A Gaussian mixture model (GMM) was constructed for each feature, with six Gaussians modeling the six patterns. Feature selection was performed and the GMMs of the five significant features were used. From the GMMs, fuzzy membership functions were obtained by a probability-possibility transformation and further statistical analysis was performed. An average classification accuracy of 63.5% was obtained for the six classes. For four of the six classes, the classification accuracy was superior to 65%, and the best classification accuracy was 75.5% for one class. The use of fuzzy membership functions to assist in pattern classification is an alternative to deterministic approaches to explore strategies for medical diagnosis.

Simultaneous water activation and glucose metabolic rate imaging with PET

NASA Astrophysics Data System (ADS)

Verhaeghe, Jeroen; Reader, Andrew J.

2013-02-01

A novel imaging and signal separation strategy is proposed to be able to separate [18F]FDG and multiple [15O]H2O signals from a simultaneously acquired dynamic PET acquisition of the two tracers. The technique is based on the fact that the dynamics of the two tracers are very distinct. By adopting an appropriate bolus injection strategy and by defining tailored sets of basis functions that model either the FDG or water component, it is possible to separate the FDG and water signal. The basis functions are inspired from the spectral analysis description of dynamic PET studies and are defined as the convolution of estimated generating functions (GFs) with a set of decaying exponential functions. The GFs are estimated from the overall measured head curve, while the decaying exponential functions are pre-determined. In this work, the time activity curves (TACs) are modelled post-reconstruction but the model can be incorporated in a global 4D reconstruction strategy. Extensive PET simulation studies are performed considering single [18F]FDG and 6 [15O]H2O bolus injections for a total acquisition time of 75 min. The proposed method is evaluated at multiple noise levels and different parameters were estimated such as [18F]FDG uptake and blood flow estimated from the [15O]H2O component, requiring a full dynamic analysis of the two components, static images of [18F]FDG and the water components as well as [15O]H2O activation. It is shown that the resulting images and parametric values in ROIs are comparable to images obtained from separate imaging, illustrating the feasibility of simultaneous imaging of [18F]FDG and [15O]H2O components. For more information on this article, see medicalphysicsweb.org

The Relationship Between Body Image and Domains of Sexual Functioning Among Heterosexual, Emerging Adult Women.

PubMed

Quinn-Nilas, Christopher; Benson, Lindsay; Milhausen, Robin R; Buchholz, Andrea C; Goncalves, Melissa

2016-09-01

Research suggests that body image affects sexual functioning, but the relationship between specific types of body image (evaluative, affective, and behavioral) and domains of sexual functioning (desire, arousal, and orgasm) has not been investigated. To determine whether, and to what degree, body image concerns (evaluative, affective, and behavioral) influence aspects of women's sexual functioning (desire, arousal, and orgasm). Eighty-eight sexually active women in heterosexual romantic relationships completed surveys assessing evaluative, affective, and behavioral body image and sexual functioning. Body composition data also were collected using dual energy x-ray absorptiometry. Sexual functioning was assessed using the desire, arousal, and orgasm subscales of the Female Sexual Functioning Index. Hierarchical multiple regression analysis indicated that poor evaluative, affective, and behavioral body image were detrimental to women's sexual functioning. Specifically, dissatisfaction with one's body predicted decrements in desire (β = -0.31, P < .05) and arousal (β = -0.35, P < .01). Similarly, feeling that others evaluate one's body negatively predicted decrements in desire (β = 0.22, P < .05) and arousal (β = 0.35, P < .01). Feeling negatively about one's appearance predicted decrements in arousal (β = 0.26, P < .05). Negative thoughts and feelings about one's body during a sexual encounter (body image self-consciousness) predicted decrements in arousal (β = -0.37, P < .01) and orgasm (β = -0.25, P < .05). Findings from this study suggest important linkages between body image and sexual functioning constructs and indicates that interventions to improve body image could have concomitant benefits related to sexual experience. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Neuroimaging of the Periaqueductal Gray: State of the Field

PubMed Central

Linnman, Clas; Moulton, Eric A.; Barmettler, Gabi; Becerra, Lino; Borsook, David

2011-01-01

This review and meta-analysis aims at summarizing and integrating the human neuroimaging studies that report periaqueductal gray (PAG) involvement; 250 original manuscripts on human neuroimaging of the PAG were identified. A narrative review and meta-analysis using activation likelihood estimates is included. Behaviors covered include pain and pain modulation, anxiety, bladder and bowel function and autonomic regulation. Methods include structural and functional magnetic resonance imaging, functional connectivity measures, diffusion weighted imaging and positron emission tomography. Human neuroimaging studies in healthy and clinical populations largely confirm the animal literature indicating that the PAG is involved in homeostatic regulation of salient functions such as pain, anxiety and autonomic function. Methodological concerns in the current literature, including resolution constraints, imaging artifacts and imprecise neuroanatomical labeling are discussed, and future directions are proposed. A general conclusion is that PAG neuroimaging is a field with enormous potential to translate animal data onto human behaviors, but with some growing pains that can and need to be addressed in order to add to our understanding of the neurobiology of this key region. PMID:22197740

THz optical design considerations and optimization for medical imaging applications

NASA Astrophysics Data System (ADS)

Sung, Shijun; Garritano, James; Bajwa, Neha; Nowroozi, Bryan; Llombart, Nuria; Grundfest, Warren; Taylor, Zachary D.

2014-09-01

THz imaging system design will play an important role making possible imaging of targets with arbitrary properties and geometries. This study discusses design consideration and imaging performance optimization techniques in THz quasioptical imaging system optics. Analysis of field and polarization distortion by off-axis parabolic (OAP) mirrors in THz imaging optics shows how distortions are carried in a series of mirrors while guiding the THz beam. While distortions of the beam profile by individual mirrors are not significant, these effects are compounded by a series of mirrors in antisymmetric orientation. It is shown that symmetric orientation of the OAP mirror effectively cancels this distortion to recover the original beam profile. Additionally, symmetric orientation can correct for some geometrical off-focusing due to misalignment. We also demonstrate an alternative method to test for overall system optics alignment by investigating the imaging performance of the tilted target plane. Asymmetric signal profile as a function of the target plane's tilt angle indicates when one or more imaging components are misaligned, giving a preferred tilt direction. Such analysis can offer additional insight into often elusive source device misalignment at an integrated system. Imaging plane tilting characteristics are representative of a 3-D modulation transfer function of the imaging system. A symmetric tilted plane is preferred to optimize imaging performance.

Fuzzy geometry, entropy, and image information

NASA Technical Reports Server (NTRS)

Pal, Sankar K.

1991-01-01

Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described.

Technical Advance: Live-imaging analysis of human dendritic cell migrating behavior under the influence of immune-stimulating reagents in an organotypic model of lung

PubMed Central

Nguyen Hoang, Anh Thu; Chen, Puran; Björnfot, Sofia; Högstrand, Kari; Lock, John G.; Grandien, Alf; Coles, Mark; Svensson, Mattias

2014-01-01

This manuscript describes technical advances allowing manipulation and quantitative analyses of human DC migratory behavior in lung epithelial tissue. DCs are hematopoietic cells essential for the maintenance of tissue homeostasis and the induction of tissue-specific immune responses. Important functions include cytokine production and migration in response to infection for the induction of proper immune responses. To design appropriate strategies to exploit human DC functional properties in lung tissue for the purpose of clinical evaluation, e.g., candidate vaccination and immunotherapy strategies, we have developed a live-imaging assay based on our previously described organotypic model of the human lung. This assay allows provocations and subsequent quantitative investigations of DC functional properties under conditions mimicking morphological and functional features of the in vivo parental tissue. We present protocols to set up and prepare tissue models for 4D (x, y, z, time) fluorescence-imaging analysis that allow spatial and temporal studies of human DCs in live epithelial tissue, followed by flow cytometry analysis of DCs retrieved from digested tissue models. This model system can be useful for elucidating incompletely defined pathways controlling DC functional responses to infection and inflammation in lung epithelial tissue, as well as the efficacy of locally administered candidate interventions. PMID:24899587

Changes in Regional Ventilation During Treatment and Dosimetric Advantages of CT Ventilation Image Guided Radiation Therapy for Locally Advanced Lung Cancer.

PubMed

Yamamoto, Tokihiro; Kabus, Sven; Bal, Matthieu; Bzdusek, Karl; Keall, Paul J; Wright, Cari; Benedict, Stanley H; Daly, Megan E

2018-05-04

Lung functional image guided radiation therapy (RT) that avoids irradiating highly functional regions has potential to reduce pulmonary toxicity following RT. Tumor regression during RT is common, leading to recovery of lung function. We hypothesized that computed tomography (CT) ventilation image-guided treatment planning reduces the functional lung dose compared to standard anatomic image-guided planning in 2 different scenarios with or without plan adaptation. CT scans were acquired before RT and during RT at 2 time points (16-20 Gy and 30-34 Gy) for 14 patients with locally advanced lung cancer. Ventilation images were calculated by deformable image registration of four-dimensional CT image data sets and image analysis. We created 4 treatment plans at each time point for each patient: functional adapted, anatomic adapted, functional unadapted, and anatomic unadapted plans. Adaptation was performed at 2 time points. Deformable image registration was used for accumulating dose and calculating a composite of dose-weighted ventilation used to quantify the lung accumulated dose-function metrics. The functional plans were compared with the anatomic plans for each scenario separately to investigate the hypothesis at a significance level of 0.05. Tumor volume was significantly reduced by 20% after 16 to 20 Gy (P = .02) and by 32% after 30 to 34 Gy (P < .01) on average. In both scenarios, the lung accumulated dose-function metrics were significantly lower in the functional plans than in the anatomic plans without compromising target volume coverage and adherence to constraints to critical structures. For example, functional planning significantly reduced the functional mean lung dose by 5.0% (P < .01) compared to anatomic planning in the adapted scenario and by 3.6% (P = .03) in the unadapted scenario. This study demonstrated significant reductions in the accumulated dose to the functional lung with CT ventilation image-guided planning compared to anatomic image-guided planning for patients showing tumor regression and changes in regional ventilation during RT. Copyright © 2018 Elsevier Inc. All rights reserved.

Correspondence of the brain's functional architecture during activation and rest

PubMed Central

Smith, Stephen M.; Fox, Peter T.; Miller, Karla L.; Glahn, David C.; Fox, P. Mickle; Mackay, Clare E.; Filippini, Nicola; Watkins, Kate E.; Toro, Roberto; Laird, Angela R.; Beckmann, Christian F.

2009-01-01

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.” PMID:19620724

Partial differential equation transform — Variational formulation and Fourier analysis

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

Nonlinear partial differential equation (PDE) models are established approaches for image/signal processing, data analysis and surface construction. Most previous geometric PDEs are utilized as low-pass filters which give rise to image trend information. In an earlier work, we introduced mode decomposition evolution equations (MoDEEs), which behave like high-pass filters and are able to systematically provide intrinsic mode functions (IMFs) of signals and images. Due to their tunable time-frequency localization and perfect reconstruction, the operation of MoDEEs is called a PDE transform. By appropriate selection of PDE transform parameters, we can tune IMFs into trends, edges, textures, noise etc., which can be further utilized in the secondary processing for various purposes. This work introduces the variational formulation, performs the Fourier analysis, and conducts biomedical and biological applications of the proposed PDE transform. The variational formulation offers an algorithm to incorporate two image functions and two sets of low-pass PDE operators in the total energy functional. Two low-pass PDE operators have different signs, leading to energy disparity, while a coupling term, acting as a relative fidelity of two image functions, is introduced to reduce the disparity of two energy components. We construct variational PDE transforms by using Euler-Lagrange equation and artificial time propagation. Fourier analysis of a simplified PDE transform is presented to shed light on the filter properties of high order PDE transforms. Such an analysis also offers insight on the parameter selection of the PDE transform. The proposed PDE transform algorithm is validated by numerous benchmark tests. In one selected challenging example, we illustrate the ability of PDE transform to separate two adjacent frequencies of sin(x) and sin(1.1x). Such an ability is due to PDE transform’s controllable frequency localization obtained by adjusting the order of PDEs. The frequency selection is achieved either by diffusion coefficients or by propagation time. Finally, we explore a large number of practical applications to further demonstrate the utility of proposed PDE transform. PMID:22207904

Cardiovascular imaging environment: will the future be cloud-based?

PubMed

Kawel-Boehm, Nadine; Bluemke, David A

2017-07-01

In cardiovascular CT and MR imaging large datasets have to be stored, post-processed, analyzed and distributed. Beside basic assessment of volume and function in cardiac magnetic resonance imaging e.g., more sophisticated quantitative analysis is requested requiring specific software. Several institutions cannot afford various types of software and provide expertise to perform sophisticated analysis. Areas covered: Various cloud services exist related to data storage and analysis specifically for cardiovascular CT and MR imaging. Instead of on-site data storage, cloud providers offer flexible storage services on a pay-per-use basis. To avoid purchase and maintenance of specialized software for cardiovascular image analysis, e.g. to assess myocardial iron overload, MR 4D flow and fractional flow reserve, evaluation can be performed with cloud based software by the consumer or complete analysis is performed by the cloud provider. However, challenges to widespread implementation of cloud services include regulatory issues regarding patient privacy and data security. Expert commentary: If patient privacy and data security is guaranteed cloud imaging is a valuable option to cope with storage of large image datasets and offer sophisticated cardiovascular image analysis for institutions of all sizes.

Performance quantification of a millimeter-wavelength imaging system based on inexpensive glow-discharge-detector focal-plane array.

PubMed

Shilemay, Moshe; Rozban, Daniel; Levanon, Assaf; Yitzhaky, Yitzhak; Kopeika, Natan S; Yadid-Pecht, Orly; Abramovich, Amir

2013-03-01

Inexpensive millimeter-wavelength (MMW) optical digital imaging raises a challenge of evaluating the imaging performance and image quality because of the large electromagnetic wavelengths and pixel sensor sizes, which are 2 to 3 orders of magnitude larger than those of ordinary thermal or visual imaging systems, and also because of the noisiness of the inexpensive glow discharge detectors that compose the focal-plane array. This study quantifies the performances of this MMW imaging system. Its point-spread function and modulation transfer function were investigated. The experimental results and the analysis indicate that the image quality of this MMW imaging system is limited mostly by the noise, and the blur is dominated by the pixel sensor size. Therefore, the MMW image might be improved by oversampling, given that noise reduction is achieved. Demonstration of MMW image improvement through oversampling is presented.

IMAGING OF BRAIN FUNCTION BASED ON THE ANALYSIS OF FUNCTIONAL CONNECTIVITY - IMAGING ANALYSIS OF BRAIN FUNCTION BY FMRI AFTER ACUPUNCTURE AT LR3 IN HEALTHY INDIVIDUALS.

PubMed

Zheng, Yu; Wang, Yuying; Lan, Yujun; Qu, Xiaodong; Lin, Kelin; Zhang, Jiping; Qu, Shanshan; Wang, Yanjie; Tang, Chunzhi; Huang, Yong

2016-01-01

This Study observed the relevant brain areas activated by acupuncture at the Taichong acupoint (LR3) and analyzed the functional connectivity among brain areas using resting state functional magnetic resonance imaging (fMRI) to explore the acupoint specificity of the Taichong acupoint. A total of 45 healthy subjects were randomly divided into the Taichong (LR3) group, sham acupuncture group and sham acupoint group. Subjects received resting state fMRI before acupuncture, after true (sham) acupuncture in each group. Analysis of changes in connectivity among the brain areas was performed using the brain functional connectivity method. The right cerebrum temporal lobe was selected as the seed point to analyze the functional connectivity. It had a functional connectivity with right cerebrum superior frontal gyrus, limbic lobe cingulate gyrus and left cerebrum inferior temporal gyrus (BA 37), inferior parietal lobule compared by before vs. after acupuncture at LR3, and right cerebrum sub-lobar insula and left cerebrum middle frontal gyrus, medial frontal gyrus compared by true vs. sham acupuncture at LR3, and right cerebrum occipital lobe cuneus, occipital lobe sub-gyral, parietal lobe precuneus and left cerebellum anterior lobe culmen by acupuncture at LR3 vs. sham acupoint. Acupuncture at LR3 mainly specifically activated the brain functional network that participates in visual function, associative function, and emotion cognition, which are similar to the features on LR3 in tradition Chinese medicine. These brain areas constituted a neural network structure with specific functions that had specific reference values for the interpretation of the acupoint specificity of the Taichong acupoint.

Inferring consistent functional interaction patterns from natural stimulus FMRI data

PubMed Central

Sun, Jiehuan; Hu, Xintao; Huang, Xiu; Liu, Yang; Li, Kaiming; Li, Xiang; Han, Junwei; Guo, Lei

2014-01-01

There has been increasing interest in how the human brain responds to natural stimulus such as video watching in the neuroimaging field. Along this direction, this paper presents our effort in inferring consistent and reproducible functional interaction patterns under natural stimulus of video watching among known functional brain regions identified by task-based fMRI. Then, we applied and compared four statistical approaches, including Bayesian network modeling with searching algorithms: greedy equivalence search (GES), Peter and Clark (PC) analysis, independent multiple greedy equivalence search (IMaGES), and the commonly used Granger causality analysis (GCA), to infer consistent and reproducible functional interaction patterns among these brain regions. It is interesting that a number of reliable and consistent functional interaction patterns were identified by the GES, PC and IMaGES algorithms in different participating subjects when they watched multiple video shots of the same semantic category. These interaction patterns are meaningful given current neuroscience knowledge and are reasonably reproducible across different brains and video shots. In particular, these consistent functional interaction patterns are supported by structural connections derived from diffusion tensor imaging (DTI) data, suggesting the structural underpinnings of consistent functional interactions. Our work demonstrates that specific consistent patterns of functional interactions among relevant brain regions might reflect the brain's fundamental mechanisms of online processing and comprehension of video messages. PMID:22440644

A Variational Level Set Approach Based on Local Entropy for Image Segmentation and Bias Field Correction.

PubMed

Tang, Jian; Jiang, Xiaoliang

2017-01-01

Image segmentation has always been a considerable challenge in image analysis and understanding due to the intensity inhomogeneity, which is also commonly known as bias field. In this paper, we present a novel region-based approach based on local entropy for segmenting images and estimating the bias field simultaneously. Firstly, a local Gaussian distribution fitting (LGDF) energy function is defined as a weighted energy integral, where the weight is local entropy derived from a grey level distribution of local image. The means of this objective function have a multiplicative factor that estimates the bias field in the transformed domain. Then, the bias field prior is fully used. Therefore, our model can estimate the bias field more accurately. Finally, minimization of this energy function with a level set regularization term, image segmentation, and bias field estimation can be achieved. Experiments on images of various modalities demonstrated the superior performance of the proposed method when compared with other state-of-the-art approaches.

Calculation of Cardiac Kinetic Energy Index from PET images.

PubMed

Sims, John; Oliveira, Marco Antônio; Meneghetti, José Claudio; Gutierrez, Marco Antônio

2015-01-01

Cardiac function can be assessed from displacement measurements in imaging modalities from nuclear medicine Using positron emission tomography (PET) image sequences with Rubidium-82, we propose and estimate the total Kinetic Energy Index (KEf) obtained from the velocity field, which was calculated using 3D optical flow(OF) methods applied over the temporal image sequence. However, it was found that the brightness of the image varied unexpectedly between frames, violating the constant brightness assumption of the OF method and causing large errors in estimating the velocity field. Therefore total brightness was equalized across image frames and the adjusted configuration tested with rest perfusion images acquired from individuals with normal (n=30) and low (n=33) cardiac function. For these images KEf was calculated as 0.5731±0.0899 and 0.3812±0.1146 for individuals with normal and low cardiac function respectively. The ability of KEf to properly classify patients into the two groups was tested with a ROC analysis, with area under the curve estimated as 0.906. To our knowledge this is the first time that KEf has been applied to PET images.

Functional MR imaging or Wada test: which is the better predictor of individual postoperative memory outcome?

PubMed

Dupont, Sophie; Duron, Emmanuelle; Samson, Séverine; Denos, Marisa; Volle, Emmanuelle; Delmaire, Christine; Navarro, Vincent; Chiras, Jacques; Lehéricy, Stéphane; Samson, Yves; Baulac, Michel

2010-04-01

To retrospectively determine whether blood oxygen level-dependent functional magnetic resonance (MR) imaging can aid prediction of postoperative memory changes in epileptic patients after temporal lobe surgery. This study was approved by the local ethics committee, and informed consent was obtained from all patients. Data were analyzed from 25 patients (12 women, 13 men; age range, 19-52 years) with refractory epilepsy in whom temporal lobe surgery was performed after they underwent preoperative functional MR imaging, the Wada test, and neuropsychological testing. The functional MR imaging protocol included three different memory tasks (24-hour delayed recognition, encoding, and immediate recognition). Individual activations were measured in medial temporal lobe (MTL) regions of both hemispheres. The prognostic accuracy of functional MR imaging for prediction of postoperative memory changes was compared with the accuracy of the Wada test and preoperative neuropsychological testing by using a backward multiple regression analysis. An equation that was based on left functional MR imaging MTL activation during delayed recognition, side of the epileptic focus, and preoperative global verbal memory score was used to correctly predict worsening of verbal memory in 90% of patients. The right functional MR imaging MTL activation did not substantially correlate with the nonverbal memory outcome, which was only predicted by using the preoperative nonverbal global score. Wada test data were not good predictors of changes in either verbal or nonverbal memory. Findings suggest that functional MR imaging activation during a delayed-recognition task is a better predictor of individual postoperative verbal memory outcome than is the Wada test. RSNA, 2010

A framework for optimal kernel-based manifold embedding of medical image data.

PubMed

Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

2015-04-01

Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

SIMA: Python software for analysis of dynamic fluorescence imaging data.

PubMed

Kaifosh, Patrick; Zaremba, Jeffrey D; Danielson, Nathan B; Losonczy, Attila

2014-01-01

Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic fluorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to fluorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at http://www.losonczylab.org/sima/.

On the importance of mathematical methods for analysis of MALDI-imaging mass spectrometry data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-21

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 10⁸ to 10⁹ intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

On the Importance of Mathematical Methods for Analysis of MALDI-Imaging Mass Spectrometry Data.

PubMed

Trede, Dennis; Kobarg, Jan Hendrik; Oetjen, Janina; Thiele, Herbert; Maass, Peter; Alexandrov, Theodore

2012-03-01

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

OpenComet: An automated tool for comet assay image analysis

PubMed Central

Gyori, Benjamin M.; Venkatachalam, Gireedhar; Thiagarajan, P.S.; Hsu, David; Clement, Marie-Veronique

2014-01-01

Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time. PMID:24624335

OpenComet: an automated tool for comet assay image analysis.

PubMed

Gyori, Benjamin M; Venkatachalam, Gireedhar; Thiagarajan, P S; Hsu, David; Clement, Marie-Veronique

2014-01-01

Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.

A learning tool for optical and microwave satellite image processing and analysis

NASA Astrophysics Data System (ADS)

Dashondhi, Gaurav K.; Mohanty, Jyotirmoy; Eeti, Laxmi N.; Bhattacharya, Avik; De, Shaunak; Buddhiraju, Krishna M.

2016-04-01

This paper presents a self-learning tool, which contains a number of virtual experiments for processing and analysis of Optical/Infrared and Synthetic Aperture Radar (SAR) images. The tool is named Virtual Satellite Image Processing and Analysis Lab (v-SIPLAB) Experiments that are included in Learning Tool are related to: Optical/Infrared - Image and Edge enhancement, smoothing, PCT, vegetation indices, Mathematical Morphology, Accuracy Assessment, Supervised/Unsupervised classification etc.; Basic SAR - Parameter extraction and range spectrum estimation, Range compression, Doppler centroid estimation, Azimuth reference function generation and compression, Multilooking, image enhancement, texture analysis, edge and detection. etc.; SAR Interferometry - BaseLine Calculation, Extraction of single look SAR images, Registration, Resampling, and Interferogram generation; SAR Polarimetry - Conversion of AirSAR or Radarsat data to S2/C3/T3 matrix, Speckle Filtering, Power/Intensity image generation, Decomposition of S2/C3/T3, Classification of S2/C3/T3 using Wishart Classifier [3]. A professional quality polarimetric SAR software can be found at [8], a part of whose functionality can be found in our system. The learning tool also contains other modules, besides executable software experiments, such as aim, theory, procedure, interpretation, quizzes, link to additional reading material and user feedback. Students can have understanding of Optical and SAR remotely sensed images through discussion of basic principles and supported by structured procedure for running and interpreting the experiments. Quizzes for self-assessment and a provision for online feedback are also being provided to make this Learning tool self-contained. One can download results after performing experiments.

Cell edge detection in JPEG2000 wavelet domain - analysis on sigmoid function edge model.

PubMed

Punys, Vytenis; Maknickas, Ramunas

2011-01-01

Big virtual microscopy images (80K x 60K pixels and larger) are usually stored using the JPEG2000 image compression scheme. Diagnostic quantification, based on image analysis, might be faster if performed on compressed data (approx. 20 times less the original amount), representing the coefficients of the wavelet transform. The analysis of possible edge detection without reverse wavelet transform is presented in the paper. Two edge detection methods, suitable for JPEG2000 bi-orthogonal wavelets, are proposed. The methods are adjusted according calculated parameters of sigmoid edge model. The results of model analysis indicate more suitable method for given bi-orthogonal wavelet.

An Optical Method for the In-Vivo Characterization of the Biomechanical Response of the Right Ventricle.

PubMed

Soltani, A; Lahti, J; Järvelä, K; Curtze, S; Laurikka, J; Hokka, M; Kuokkala, V-T

2018-05-01

The intraoperative in-vivo mechanical function of the left ventricle has been studied thoroughly using echocardiography in the past. However, due to technical and anatomical issues, the ultrasound technology cannot easily be focused on the right side of the heart during open-heart surgery, and the function of the right ventricle during the intervention remains largely unexplored. We used optical imaging and digital image correlation for the characterization of the right ventricle motion and deformation during open-heart surgery. This work is a pilot study focusing on one patient only with the aim of establishing the framework for long term research. These experiments show that optical imaging and the analysis of the images can be used to obtain similar parameters, and partly at higher accuracy, for describing the mechanical functioning of the heart as the ultrasound technology. This work describes the optical imaging based method to characterize the mechanical response of the heart in-vivo, and offers new insight into the mechanical function of the right ventricle.

Imaging challenges in biomaterials and tissue engineering

PubMed Central

Appel, Alyssa A.; Anastasio, Mark A.; Larson, Jeffery C.; Brey, Eric M.

2013-01-01

Biomaterials are employed in the fields of tissue engineering and regenerative medicine (TERM) in order to enhance the regeneration or replacement of tissue function and/or structure. The unique environments resulting from the presence of biomaterials, cells, and tissues result in distinct challenges in regards to monitoring and assessing the results of these interventions. Imaging technologies for three-dimensional (3D) analysis have been identified as a strategic priority in TERM research. Traditionally, histological and immunohistochemical techniques have been used to evaluate engineered tissues. However, these methods do not allow for an accurate volume assessment, are invasive, and do not provide information on functional status. Imaging techniques are needed that enable non-destructive, longitudinal, quantitative, and three-dimensional analysis of TERM strategies. This review focuses on evaluating the application of available imaging modalities for assessment of biomaterials and tissue in TERM applications. Included is a discussion of limitations of these techniques and identification of areas for further development. PMID:23768903

Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent.

PubMed

Canuto, Holly C; McLachlan, Charles; Kettunen, Mikko I; Velic, Marko; Krishnan, Anant S; Neves, Andre' A; de Backer, Maaike; Hu, D-E; Hobson, Michael P; Brindle, Kevin M

2009-05-01

A targeted Gd(3+)-based contrast agent has been developed that detects tumor cell death by binding to the phosphatidylserine (PS) exposed on the plasma membrane of dying cells. Although this agent has been used to detect tumor cell death in vivo, the differences in signal intensity between treated and untreated tumors was relatively small. As cell death is often spatially heterogeneous within tumors, we investigated whether an image analysis technique that parameterizes heterogeneity could be used to increase the sensitivity of detection of this targeted contrast agent. Two-dimensional (2D) Minkowski functionals (MFs) provided an automated and reliable method for parameterization of image heterogeneity, which does not require prior assumptions about the number of regions or features in the image, and were shown to increase the sensitivity of detection of the contrast agent as compared to simple signal intensity analysis. (c) 2009 Wiley-Liss, Inc.

[Present status and trend of heart fluid mechanics research based on medical image analysis].

PubMed

Gan, Jianhong; Yin, Lixue; Xie, Shenghua; Li, Wenhua; Lu, Jing; Luo, Anguo

2014-06-01

With introduction of current main methods for heart fluid mechanics researches, we studied the characteristics and weakness for three primary analysis methods based on magnetic resonance imaging, color Doppler ultrasound and grayscale ultrasound image, respectively. It is pointed out that particle image velocity (PIV), speckle tracking and block match have the same nature, and three algorithms all adopt block correlation. The further analysis shows that, with the development of information technology and sensor, the research for cardiac function and fluid mechanics will focus on energy transfer process of heart fluid, characteristics of Chamber wall related to blood fluid and Fluid-structure interaction in the future heart fluid mechanics fields.

Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging.

PubMed

Mori, Yasuo; Miyata, Jun; Isobe, Masanori; Son, Shuraku; Yoshihara, Yujiro; Aso, Toshihiko; Kouchiyama, Takanori; Murai, Toshiya; Takahashi, Hidehiko

2018-05-17

Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI), however it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relationship, the majority of neuroimaging studies have not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia. Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with schizophrenia (SC) and 37 matched healthy controls (HC). We conducted a functional connectivity analysis using independent component analysis and performed three group comparisons: A-P vs. P-A (all participants), SC vs. HC for the A-P and P-A datasets, and the interaction between phase-encoding direction and participant group. The estimated functional connectivity differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although functional connectivity in the SC group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporo-parietal junction and left fusiform gyrus. Phase-encoding direction can influence the results of functional connectivity studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Quantitative Analysis Tools and Digital Phantoms for Deformable Image Registration Quality Assurance.

PubMed

Kim, Haksoo; Park, Samuel B; Monroe, James I; Traughber, Bryan J; Zheng, Yiran; Lo, Simon S; Yao, Min; Mansur, David; Ellis, Rodney; Machtay, Mitchell; Sohn, Jason W

2015-08-01

This article proposes quantitative analysis tools and digital phantoms to quantify intrinsic errors of deformable image registration (DIR) systems and establish quality assurance (QA) procedures for clinical use of DIR systems utilizing local and global error analysis methods with clinically realistic digital image phantoms. Landmark-based image registration verifications are suitable only for images with significant feature points. To address this shortfall, we adapted a deformation vector field (DVF) comparison approach with new analysis techniques to quantify the results. Digital image phantoms are derived from data sets of actual patient images (a reference image set, R, a test image set, T). Image sets from the same patient taken at different times are registered with deformable methods producing a reference DVFref. Applying DVFref to the original reference image deforms T into a new image R'. The data set, R', T, and DVFref, is from a realistic truth set and therefore can be used to analyze any DIR system and expose intrinsic errors by comparing DVFref and DVFtest. For quantitative error analysis, calculating and delineating differences between DVFs, 2 methods were used, (1) a local error analysis tool that displays deformation error magnitudes with color mapping on each image slice and (2) a global error analysis tool that calculates a deformation error histogram, which describes a cumulative probability function of errors for each anatomical structure. Three digital image phantoms were generated from three patients with a head and neck, a lung and a liver cancer. The DIR QA was evaluated using the case with head and neck. © The Author(s) 2014.

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

PubMed

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

2003-04-01

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

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

Alignment error envelopes for single particle analysis.

PubMed

Jensen, G J

2001-01-01

To determine the structure of a biological particle to high resolution by electron microscopy, image averaging is required to combine information from different views and to increase the signal-to-noise ratio. Starting from the number of noiseless views necessary to resolve features of a given size, four general factors are considered that increase the number of images actually needed: (1) the physics of electron scattering introduces shot noise, (2) thermal motion and particle inhomogeneity cause the scattered electrons to describe a mixture of structures, (3) the microscope system fails to usefully record all the information carried by the scattered electrons, and (4) image misalignment leads to information loss through incoherent averaging. The compound effect of factors 2-4 is approximated by the product of envelope functions. The problem of incoherent image averaging is developed in detail through derivation of five envelope functions that account for small errors in 11 "alignment" parameters describing particle location, orientation, defocus, magnification, and beam tilt. The analysis provides target error tolerances for single particle analysis to near-atomic (3.5 A) resolution, and this prospect is shown to depend critically on image quality, defocus determination, and microscope alignment. Copyright 2001 Academic Press.

Quantitative analysis for peripheral vascularity assessment based on clinical photoacoustic and ultrasound images

NASA Astrophysics Data System (ADS)

Murakoshi, Dai; Hirota, Kazuhiro; Ishii, Hiroyasu; Hashimoto, Atsushi; Ebata, Tetsurou; Irisawa, Kaku; Wada, Takatsugu; Hayakawa, Toshiro; Itoh, Kenji; Ishihara, Miya

2018-02-01

Photoacoustic (PA) imaging technology is expected to be applied to clinical assessment for peripheral vascularity. We started a clinical evaluation with the prototype PA imaging system we recently developed. Prototype PA imaging system was composed with in-house Q-switched Alexandrite laser system which emits short-pulsed laser with 750 nm wavelength, handheld ultrasound transducer where illumination optics were integrated and signal processing for PA image reconstruction implemented in the clinical ultrasound (US) system. For the purpose of quantitative assessment of PA images, an image analyzing function has been developed and applied to clinical PA images. In this analyzing function, vascularity derived from PA signal intensity ranged for prescribed threshold was defined as a numerical index of vessel fulfillment and calculated for the prescribed region of interest (ROI). Skin surface was automatically detected by utilizing B-mode image acquired simultaneously with PA image. Skinsurface position is utilized to place the ROI objectively while avoiding unwanted signals such as artifacts which were imposed due to melanin pigment in the epidermal layer which absorbs laser emission and generates strong PA signals. Multiple images were available to support the scanned image set for 3D viewing. PA images for several fingers of patients with systemic sclerosis (SSc) were quantitatively assessed. Since the artifact region is trimmed off in PA images, the visibility of vessels with rather low PA signal intensity on the 3D projection image was enhanced and the reliability of the quantitative analysis was improved.

Stripe nonuniformity correction for infrared imaging system based on single image optimization

NASA Astrophysics Data System (ADS)

Hua, Weiping; Zhao, Jufeng; Cui, Guangmang; Gong, Xiaoli; Ge, Peng; Zhang, Jiang; Xu, Zhihai

2018-06-01

Infrared imaging is often disturbed by stripe nonuniformity noise. Scene-based correction method can effectively reduce the impact of stripe noise. In this paper, a stripe nonuniformity correction method based on differential constraint is proposed. Firstly, the gray distribution of stripe nonuniformity is analyzed and the penalty function is constructed by the difference of horizontal gradient and vertical gradient. With the weight function, the penalty function is optimized to obtain the corrected image. Comparing with other single-frame approaches, experiments show that the proposed method performs better in both subjective and objective analysis, and does less damage to edge and detail. Meanwhile, the proposed method runs faster. We have also discussed the differences between the proposed idea and multi-frame methods. Our method is finally well applied in hardware system.

Factor analysis for delineation of organ structures, creation of in- and output functions, and standardization of multicenter kinetic modeling

NASA Astrophysics Data System (ADS)

Schiepers, Christiaan; Hoh, Carl K.; Dahlbom, Magnus; Wu, Hsiao-Ming; Phelps, Michael E.

1999-05-01

PET imaging can quantify metabolic processes in-vivo; this requires the measurement of an input function which is invasive and labor intensive. A non-invasive, semi-automated, image based method of input function generation would be efficient, patient friendly, and allow quantitative PET to be applied routinely. A fully automated procedure would be ideal for studies across institutions. Factor analysis (FA) was applied as processing tool for definition of temporally changing structures in the field of view. FA has been proposed earlier, but the perceived mathematical difficulty has prevented widespread use. FA was utilized to delineate structures and extract blood and tissue time-activity-curves (TACs). These TACs were used as input and output functions for tracer kinetic modeling, the results of which were compared with those from an input function obtained with serial blood sampling. Dynamic image data of myocardial perfusion studies with N-13 ammonia, O-15 water, or Rb-82, cancer studies with F-18 FDG, and skeletal studies with F-18 fluoride were evaluated. Correlation coefficients of kinetic parameters obtained with factor and plasma input functions were high. Linear regression usually furnished a slope near unity. Processing time was 7 min/patient on an UltraSPARC. Conclusion: FA can non-invasively generate input functions from image data eliminating the need for blood sampling. Output (tissue) functions can be simultaneously generated. The method is simple, requires no sophisticated operator interaction and has little inter-operator variability. FA is well suited for studies across institutions and standardized evaluations.

Automated CT Scan Scores of Bronchiectasis and Air Trapping in Cystic Fibrosis

PubMed Central

Swiercz, Waldemar; Heltshe, Sonya L.; Anthony, Margaret M.; Szefler, Paul; Klein, Rebecca; Strain, John; Brody, Alan S.; Sagel, Scott D.

2014-01-01

Background: Computer analysis of high-resolution CT (HRCT) scans may improve the assessment of structural lung injury in children with cystic fibrosis (CF). The goal of this cross-sectional pilot study was to validate automated, observer-independent image analysis software to establish objective, simple criteria for bronchiectasis and air trapping. Methods: HRCT scans of the chest were performed in 35 children with CF and compared with scans from 12 disease control subjects. Automated image analysis software was developed to count visible airways on inspiratory images and to measure a low attenuation density (LAD) index on expiratory images. Among the children with CF, relationships among automated measures, Brody HRCT scanning scores, lung function, and sputum markers of inflammation were assessed. Results: The number of total, central, and peripheral airways on inspiratory images and LAD (%) on expiratory images were significantly higher in children with CF compared with control subjects. Among subjects with CF, peripheral airway counts correlated strongly with Brody bronchiectasis scores by two raters (r = 0.86, P < .0001; r = 0.91, P < .0001), correlated negatively with lung function, and were positively associated with sputum free neutrophil elastase activity. LAD (%) correlated with Brody air trapping scores (r = 0.83, P < .0001; r = 0.69, P < .0001) but did not correlate with lung function or sputum inflammatory markers. Conclusions: Quantitative airway counts and LAD (%) on HRCT scans appear to be useful surrogates for bronchiectasis and air trapping in children with CF. Our automated methodology provides objective quantitative measures of bronchiectasis and air trapping that may serve as end points in CF clinical trials. PMID:24114359

The association between sexual satisfaction and body image in women.

PubMed

Pujols, Yasisca; Seal, Brooke N; Meston, Cindy M

2010-02-01

Although sexual functioning has been linked to sexual satisfaction, it only partially explains the degree to which women report being sexually satisfied. Other factors include quality of life, relational variables, and individual factors such as body image. Of the few studies that have investigated the link between body image and sexual satisfaction, most have considered body image to be a single construct and have shown mixed results. The present study assessed multiple body image variables in order to better understand which aspects of body image influence multiple domains of sexual satisfaction, including sexual communication, compatibility, contentment, personal concern, and relational concern in a community sample of women. Women between the ages of 18 and 49 years in sexual relationships (N = 154) participated in an Internet survey that assessed sexual functioning, five domains of sexual satisfaction, and several body image variables. Body image variables included the sexual attractiveness, weight concern, and physical condition subscales of the Body Esteem Scale, the appearance-based subscale of the Cognitive Distractions During Sexual Activity Scale, and body mass index. Total score of the Sexual Satisfaction Scale for Women was the main outcome measure. Sexual functioning was measured by a modified Female Sexual Function Index. Consistent with expectations, correlations indicated significant positive relationships between sexual functioning, sexual satisfaction, and all body image variables. A multiple regression analysis revealed that sexual satisfaction was predicted by high body esteem and low frequency of appearance-based distracting thoughts during sexual activity, even after controlling for sexual functioning status. Several aspects of body image, including weight concern, physical condition, sexual attractiveness, and thoughts about the body during sexual activity predict sexual satisfaction in women. The findings suggest that women who experience low sexual satisfaction may benefit from treatments that target these specific aspects of body image.

Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish

PubMed Central

Yang, Zhe; Mei, Li; Xia, Fei; Luo, Qingming; Fu, Ling; Gong, Hui

2015-01-01

In vivo functional imaging at single-neuron resolution is an important approach to visualize biological processes in neuroscience. Light sheet microscopy (LSM) is a cutting edge in vivo imaging technique that provides micron-scale spatial resolution at high frame rate. Due to the scattering and absorption of tissue, however, conventional LSM is inadequate to resolve cells because of the attenuated signal to noise ratio (SNR). Using dual-beam illumination and confocal dual-slit detection, here a dual-slit confocal LSM is demonstrated to obtain the SNR enhanced images with frame rate twice as high as line confocal LSM method. Through theoretical calculations and experiments, the correlation between the slit’s width and SNR was determined to optimize the image quality. In vivo whole brain structural imaging stacks and the functional imaging sequences of single slice were obtained for analysis of calcium activities at single-cell resolution. A two-fold increase in imaging speed of conventional confocal LSM makes it possible to capture the sequence of the neurons’ activities and help reveal the potential functional connections in the whole zebrafish’s brain. PMID:26137381

Image processing and pattern recognition with CVIPtools MATLAB toolbox: automatic creation of masks for veterinary thermographic images

NASA Astrophysics Data System (ADS)

Mishra, Deependra K.; Umbaugh, Scott E.; Lama, Norsang; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

CVIPtools is a software package for the exploration of computer vision and image processing developed in the Computer Vision and Image Processing Laboratory at Southern Illinois University Edwardsville. CVIPtools is available in three variants - a) CVIPtools Graphical User Interface, b) CVIPtools C library and c) CVIPtools MATLAB toolbox, which makes it accessible to a variety of different users. It offers students, faculty, researchers and any user a free and easy way to explore computer vision and image processing techniques. Many functions have been implemented and are updated on a regular basis, the library has reached a level of sophistication that makes it suitable for both educational and research purposes. In this paper, the detail list of the functions available in the CVIPtools MATLAB toolbox are presented and how these functions can be used in image analysis and computer vision applications. The CVIPtools MATLAB toolbox allows the user to gain practical experience to better understand underlying theoretical problems in image processing and pattern recognition. As an example application, the algorithm for the automatic creation of masks for veterinary thermographic images is presented.

Grey Matter Alterations Co-Localize with Functional Abnormalities in Developmental Dyslexia: An ALE Meta-Analysis

PubMed Central

Linkersdörfer, Janosch; Lonnemann, Jan; Lindberg, Sven; Hasselhorn, Marcus; Fiebach, Christian J.

2012-01-01

The neural correlates of developmental dyslexia have been investigated intensively over the last two decades and reliable evidence for a dysfunction of left-hemispheric reading systems in dyslexic readers has been found in functional neuroimaging studies. In addition, structural imaging studies using voxel-based morphometry (VBM) demonstrated grey matter reductions in dyslexics in several brain regions. To objectively assess the consistency of these findings, we performed activation likelihood estimation (ALE) meta-analysis on nine published VBM studies reporting 62 foci of grey matter reduction in dyslexic readers. We found six significant clusters of convergence in bilateral temporo-parietal and left occipito-temporal cortical regions and in the cerebellum bilaterally. To identify possible overlaps between structural and functional deviations in dyslexic readers, we conducted additional ALE meta-analyses of imaging studies reporting functional underactivations (125 foci from 24 studies) or overactivations (95 foci from 11 studies ) in dyslexics. Subsequent conjunction analyses revealed overlaps between the results of the VBM meta-analysis and the meta-analysis of functional underactivations in the fusiform and supramarginal gyri of the left hemisphere. An overlap between VBM results and the meta-analysis of functional overactivations was found in the left cerebellum. The results of our study provide evidence for consistent grey matter variations bilaterally in the dyslexic brain and substantial overlap of these structural variations with functional abnormalities in left hemispheric regions. PMID:22916214

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards an Analysis of Visual Images in School Science Textbooks and Press Articles about Science and Technology.

ERIC Educational Resources Information Center

Dimopoulos, Kostas; Koulaidis, Vasilis; Sklaveniti, Spyridoula

2003-01-01

Analyzes the pedagogic functions of visual images included in school science textbooks and daily press articles about science and technology. Indicates that the science textbooks (a) use 10 times more images, (b) use more images so as to familiarize their readers with the specialized techno-scientific content and codes, and (c) tend to create a…

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

PubMed

Sojoudi, Alireza; Goodyear, Bradley G

2016-12-01

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Effective structural descriptors for natural and engineered radioactive waste confinement barriers

NASA Astrophysics Data System (ADS)

Lemmens, Laurent; Rogiers, Bart; De Craen, Mieke; Laloy, Eric; Jacques, Diederik; Huysmans, Marijke; Swennen, Rudy; Urai, Janos L.; Desbois, Guillaume

2017-04-01

The microstructure of a radioactive waste confinement barrier strongly influences its flow and transport properties. Numerical flow and transport simulations for these porous media at the pore scale therefore require input data that describe the microstructure as accurately as possible. To date, no imaging method can resolve all heterogeneities within important radioactive waste confinement barrier materials as hardened cement paste and natural clays at the micro scale (nm-cm). Therefore, it is necessary to merge information from different 2D and 3D imaging methods using porous media reconstruction techniques. To qualitatively compare the results of different reconstruction techniques, visual inspection might suffice. To quantitatively compare training-image based algorithms, Tan et al. (2014) proposed an algorithm using an analysis of distance. However, the ranking of the algorithm depends on the choice of the structural descriptor, in their case multiple-point or cluster-based histograms. We present here preliminary work in which we will review different structural descriptors and test their effectiveness, for capturing the main structural characteristics of radioactive waste confinement barrier materials, to determine the descriptors to use in the analysis of distance. The investigated descriptors are particle size distributions, surface area distributions, two point probability functions, multiple point histograms, linear functions and two point cluster functions. The descriptor testing consists of stochastically generating realizations from a reference image using the simulated annealing optimization procedure introduced by Karsanina et al. (2015). This procedure basically minimizes the differences between pre-specified descriptor values associated with the training image and the image being produced. The most efficient descriptor set can therefore be identified by comparing the image generation quality among the tested descriptor combinations. The assessment of the quality of the simulations will be made by combining all considered descriptors. Once the set of the most efficient descriptors is determined, they can be used in the analysis of distance, to rank different reconstruction algorithms in a more objective way in future work. Karsanina MV, Gerke KM, Skvortsova EB, Mallants D (2015) Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure. PLoS ONE 10(5): e0126515. doi:10.1371/journal.pone.0126515 Tan, Xiaojin, Pejman Tahmasebi, and Jef Caers. "Comparing training-image based algorithms using an analysis of distance." Mathematical Geosciences 46.2 (2014): 149-169.

Quantification of video-taped images in microcirculation research using inexpensive imaging software (Adobe Photoshop).

PubMed

Brunner, J; Krummenauer, F; Lehr, H A

2000-04-01

Study end-points in microcirculation research are usually video-taped images rather than numeric computer print-outs. Analysis of these video-taped images for the quantification of microcirculatory parameters usually requires computer-based image analysis systems. Most software programs for image analysis are custom-made, expensive, and limited in their applicability to selected parameters and study end-points. We demonstrate herein that an inexpensive, commercially available computer software (Adobe Photoshop), run on a Macintosh G3 computer with inbuilt graphic capture board provides versatile, easy to use tools for the quantification of digitized video images. Using images obtained by intravital fluorescence microscopy from the pre- and postischemic muscle microcirculation in the skinfold chamber model in hamsters, Photoshop allows simple and rapid quantification (i) of microvessel diameters, (ii) of the functional capillary density and (iii) of postischemic leakage of FITC-labeled high molecular weight dextran from postcapillary venules. We present evidence of the technical accuracy of the software tools and of a high degree of interobserver reliability. Inexpensive commercially available imaging programs (i.e., Adobe Photoshop) provide versatile tools for image analysis with a wide range of potential applications in microcirculation research.

Image velocimetry and spectral analysis enable quantitative characterization of larval zebrafish gut motility.

PubMed

Ganz, J; Baker, R P; Hamilton, M K; Melancon, E; Diba, P; Eisen, J S; Parthasarathy, R

2018-05-02

Normal gut function requires rhythmic and coordinated movements that are affected by developmental processes, physical and chemical stimuli, and many debilitating diseases. The imaging and characterization of gut motility, especially regarding periodic, propagative contractions driving material transport, are therefore critical goals. Previous image analysis approaches have successfully extracted properties related to the temporal frequency of motility modes, but robust measures of contraction magnitude, especially from in vivo image data, remain challenging to obtain. We developed a new image analysis method based on image velocimetry and spectral analysis that reveals temporal characteristics such as frequency and wave propagation speed, while also providing quantitative measures of the amplitude of gut motion. We validate this approach using several challenges to larval zebrafish, imaged with differential interference contrast microscopy. Both acetylcholine exposure and feeding increase frequency and amplitude of motility. Larvae lacking enteric nervous system gut innervation show the same average motility frequency, but reduced and less variable amplitude compared to wild types. Our image analysis approach enables insights into gut dynamics in a wide variety of developmental and physiological contexts and can also be extended to analyze other types of cell movements. © 2018 John Wiley & Sons Ltd.

Dynamic Functional Connectivity States Between the Dorsal and Ventral Sensorimotor Networks Revealed by Dynamic Conditional Correlation Analysis of Resting-State Functional Magnetic Resonance Imaging.

PubMed

Syed, Maleeha F; Lindquist, Martin A; Pillai, Jay J; Agarwal, Shruti; Gujar, Sachin K; Choe, Ann S; Caffo, Brian; Sair, Haris I

2017-12-01

Functional connectivity in resting-state functional magnetic resonance imaging (rs-fMRI) has received substantial attention since the initial findings of Biswal et al. Traditional network correlation metrics assume that the functional connectivity in the brain remains stationary over time. However, recent studies have shown that robust temporal fluctuations of functional connectivity among as well as within functional networks exist, challenging this assumption. In this study, these dynamic correlation differences were investigated between the dorsal and ventral sensorimotor networks by applying the dynamic conditional correlation model to rs-fMRI data of 20 healthy subjects. k-Means clustering was used to determine an optimal number of discrete connectivity states (k = 10) of the sensorimotor system across all subjects. Our analysis confirms the existence of differences in dynamic correlation between the dorsal and ventral networks, with highest connectivity found within the ventral motor network.

Clock Scan Protocol for Image Analysis: ImageJ Plugins.

PubMed

Dobretsov, Maxim; Petkau, Georg; Hayar, Abdallah; Petkau, Eugen

2017-06-19

The clock scan protocol for image analysis is an efficient tool to quantify the average pixel intensity within, at the border, and outside (background) a closed or segmented convex-shaped region of interest, leading to the generation of an averaged integral radial pixel-intensity profile. This protocol was originally developed in 2006, as a visual basic 6 script, but as such, it had limited distribution. To address this problem and to join similar recent efforts by others, we converted the original clock scan protocol code into two Java-based plugins compatible with NIH-sponsored and freely available image analysis programs like ImageJ or Fiji ImageJ. Furthermore, these plugins have several new functions, further expanding the range of capabilities of the original protocol, such as analysis of multiple regions of interest and image stacks. The latter feature of the program is especially useful in applications in which it is important to determine changes related to time and location. Thus, the clock scan analysis of stacks of biological images may potentially be applied to spreading of Na + or Ca ++ within a single cell, as well as to the analysis of spreading activity (e.g., Ca ++ waves) in populations of synaptically-connected or gap junction-coupled cells. Here, we describe these new clock scan plugins and show some examples of their applications in image analysis.

[Research Progress of Multi-Model Medical Image Fusion at Feature Level].

PubMed

Zhang, Junjie; Zhou, Tao; Lu, Huiling; Wang, Huiqun

2016-04-01

Medical image fusion realizes advantage integration of functional images and anatomical images.This article discusses the research progress of multi-model medical image fusion at feature level.We firstly describe the principle of medical image fusion at feature level.Then we analyze and summarize fuzzy sets,rough sets,D-S evidence theory,artificial neural network,principal component analysis and other fusion methods’ applications in medical image fusion and get summery.Lastly,we in this article indicate present problems and the research direction of multi-model medical images in the future.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease.

PubMed

de Schipper, Laura J; Hafkemeijer, Anne; van der Grond, Jeroen; Marinus, Johan; Henselmans, Johanna M L; van Hilten, Jacobus J

2018-01-01

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks. Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease. Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients ( n = 107) with control subjects ( n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms. Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found. Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Comparing diffuse optical tomography and functional magnetic resonance imaging signals during a cognitive task: pilot study

PubMed Central

Hernández-Martin, Estefania; Marcano, Francisco; Casanova, Oscar; Modroño, Cristian; Plata-Bello, Julio; González-Mora, Jose Luis

2017-01-01

Abstract. Diffuse optical tomography (DOT) measures concentration changes in both oxy- and deoxyhemoglobin providing three-dimensional images of local brain activations. A pilot study, which compares both DOT and functional magnetic resonance imaging (fMRI) volumes through t-maps given by canonical statistical parametric mapping (SPM) processing for both data modalities, is presented. The DOT series were processed using a method that is based on a Bayesian filter application on raw DOT data to remove physiological changes and minimum description length application index to select a number of singular values, which reduce the data dimensionality during image reconstruction and adaptation of DOT volume series to normalized standard space. Therefore, statistical analysis is performed with canonical SPM software in the same way as fMRI analysis is done, accepting DOT volumes as if they were fMRI volumes. The results show the reproducibility and ruggedness of the method to process DOT series on group analysis using cognitive paradigms on the prefrontal cortex. Difficulties such as the fact that scalp–brain distances vary between subjects or cerebral activations are difficult to reproduce due to strategies used by the subjects to solve arithmetic problems are considered. T-images given by fMRI and DOT volume series analyzed in SPM show that at the functional level, both DOT and fMRI measures detect the same areas, although DOT provides complementary information to fMRI signals about cerebral activity. PMID:28386575

Technical advance: live-imaging analysis of human dendritic cell migrating behavior under the influence of immune-stimulating reagents in an organotypic model of lung.

PubMed

Nguyen Hoang, Anh Thu; Chen, Puran; Björnfot, Sofia; Högstrand, Kari; Lock, John G; Grandien, Alf; Coles, Mark; Svensson, Mattias

2014-09-01

This manuscript describes technical advances allowing manipulation and quantitative analyses of human DC migratory behavior in lung epithelial tissue. DCs are hematopoietic cells essential for the maintenance of tissue homeostasis and the induction of tissue-specific immune responses. Important functions include cytokine production and migration in response to infection for the induction of proper immune responses. To design appropriate strategies to exploit human DC functional properties in lung tissue for the purpose of clinical evaluation, e.g., candidate vaccination and immunotherapy strategies, we have developed a live-imaging assay based on our previously described organotypic model of the human lung. This assay allows provocations and subsequent quantitative investigations of DC functional properties under conditions mimicking morphological and functional features of the in vivo parental tissue. We present protocols to set up and prepare tissue models for 4D (x, y, z, time) fluorescence-imaging analysis that allow spatial and temporal studies of human DCs in live epithelial tissue, followed by flow cytometry analysis of DCs retrieved from digested tissue models. This model system can be useful for elucidating incompletely defined pathways controlling DC functional responses to infection and inflammation in lung epithelial tissue, as well as the efficacy of locally administered candidate interventions. © 2014 Society for Leukocyte Biology.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography.

PubMed

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-09-01

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography leading to underestimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multiresolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low-resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model, which may introduce artifacts in regions where no significant correlation exists between anatomical and functional details. A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present, the new model outperformed the 2D global approach, avoiding artifacts and significantly improving quality of the corrected images and their quantitative accuracy. A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multiresolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information.

Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

PubMed Central

Le Pogam, Adrien; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico E.; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

2011-01-01

Purpose Partial volume effects (PVE) are consequences of the limited spatial resolution in emission tomography leading to under-estimation of uptake in tissues of size similar to the point spread function (PSF) of the scanner as well as activity spillover between adjacent structures. Among PVE correction methodologies, a voxel-wise mutual multi-resolution analysis (MMA) was recently introduced. MMA is based on the extraction and transformation of high resolution details from an anatomical image (MR/CT) and their subsequent incorporation into a low resolution PET image using wavelet decompositions. Although this method allows creating PVE corrected images, it is based on a 2D global correlation model which may introduce artefacts in regions where no significant correlation exists between anatomical and functional details. Methods A new model was designed to overcome these two issues (2D only and global correlation) using a 3D wavelet decomposition process combined with a local analysis. The algorithm was evaluated on synthetic, simulated and patient images, and its performance was compared to the original approach as well as the geometric transfer matrix (GTM) method. Results Quantitative performance was similar to the 2D global model and GTM in correlated cases. In cases where mismatches between anatomical and functional information were present the new model outperformed the 2D global approach, avoiding artefacts and significantly improving quality of the corrected images and their quantitative accuracy. Conclusions A new 3D local model was proposed for a voxel-wise PVE correction based on the original mutual multi-resolution analysis approach. Its evaluation demonstrated an improved and more robust qualitative and quantitative accuracy compared to the original MMA methodology, particularly in the absence of full correlation between anatomical and functional information. PMID:21978037

Identification and characterisation of midbrain nuclei using optimised functional magnetic resonance imaging

PubMed Central

Limbrick-Oldfield, Eve H.; Brooks, Jonathan C.W.; Wise, Richard J.S.; Padormo, Francesco; Hajnal, Jo V.; Beckmann, Christian F.; Ungless, Mark A.

2012-01-01

Localising activity in the human midbrain with conventional functional MRI (fMRI) is challenging because the midbrain nuclei are small and located in an area that is prone to physiological artefacts. Here we present a replicable and automated method to improve the detection and localisation of midbrain fMRI signals. We designed a visual fMRI task that was predicted would activate the superior colliculi (SC) bilaterally. A limited number of coronal slices were scanned, orientated along the long axis of the brainstem, whilst simultaneously recording cardiac and respiratory traces. A novel anatomical registration pathway was used to optimise the localisation of the small midbrain nuclei in stereotactic space. Two additional structural scans were used to improve registration between functional and structural T1-weighted images: an echo-planar image (EPI) that matched the functional data but had whole-brain coverage, and a whole-brain T2-weighted image. This pathway was compared to conventional registration pathways, and was shown to significantly improve midbrain registration. To reduce the physiological artefacts in the functional data, we estimated and removed structured noise using a modified version of a previously described physiological noise model (PNM). Whereas a conventional analysis revealed only unilateral SC activity, the PNM analysis revealed the predicted bilateral activity. We demonstrate that these methods improve the measurement of a biologically plausible fMRI signal. Moreover they could be used to investigate the function of other midbrain nuclei. PMID:21867762

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.

Sensitivity analysis of hybrid thermoelastic techniques

Treesearch

W.A. Samad; J.M. Considine

2017-01-01

Stress functions have been used as a complementary tool to support experimental techniques, such as thermoelastic stress analysis (TSA) and digital image correlation (DIC), in an effort to evaluate the complete and separate full-field stresses of loaded structures. The need for such coupling between experimental data and stress functions is due to the fact that...

Resting-state functional magnetic resonance imaging: the impact of regression analysis.

PubMed

Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

2015-01-01

To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI.

PubMed

Chao-Gan, Yan; Yu-Feng, Zang

2010-01-01

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Interaction of amino acid-functionalized silver nanoparticles and Candida albicans polymorphs: A deep-UV fluorescence imaging study.

PubMed

Dojčilović, Radovan; Pajović, Jelena D; Božanić, Dušan K; Bogdanović, Una; Vodnik, Vesna V; Dimitrijević-Branković, Suzana; Miljković, Miona G; Kaščaková, Slavka; Réfrégiers, Matthieu; Djoković, Vladimir

2017-07-01

The interaction of the tryptophan functionalized Ag nanoparticles and live Candida albicans cells was studied by synchrotron excitation deep-ultraviolet (DUV) fluorescence imaging at the DISCO beamline of Synchrotron SOLEIL. DUV imaging showed that incubation of the fungus with functionalized nanoparticles results in significant increase in the fluorescence signal. The analysis of the images revealed that the interaction of the nanoparticles with (pseudo)hyphae polymorphs of the diploid fungus was less pronounced than in the case of yeast cells or budding spores. The changes in the intensity of the fluorescence signals of the cells after incubation were followed in [327-353nm] and [370-410nm] spectral ranges that correspond to the fluorescence of tryptophan in non-polar and polar environment, respectively. As a consequence of the environmental sensitivity of the silver-tryptophan fluorescent nanoprobe, we were able to determine the possible accumulation sites of the nanoparticles. The analysis of the intensity decay kinetics showed that the photobleaching effects were more pronounced in the case of the functionalized nanoparticle treated cells. The results of time-integrated emission in the mentioned spectral ranges suggested that the nanoparticles penetrate the cells, but that the majority of the nanoparticles attach to the cells' surfaces. Copyright © 2017 Elsevier B.V. All rights reserved.

Functional cardiac magnetic resonance microscopy

NASA Astrophysics Data System (ADS)

Brau, Anja Christina Sophie

2003-07-01

The study of small animal models of human cardiovascular disease is critical to our understanding of the origin, progression, and treatment of this pervasive disease. Complete analysis of disease pathophysiology in these animal models requires measuring structural and functional changes at the level of the whole heart---a task for which an appropriate non-invasive imaging method is needed. The purpose of this work was thus to develop an imaging technique to support in vivo characterization of cardiac structure and function in rat and mouse models of cardiovascular disease. Whereas clinical cardiac magnetic resonance imaging (MRI) provides accurate assessment of the human heart, the extension of cardiac MRI from humans to rodents presents several formidable scaling challenges. Acquiring images of the mouse heart with organ definition and fluidity of contraction comparable to that achieved in humans requires an increase in spatial resolution by a factor of 3000 and an increase in temporal resolution by a factor of ten. No single technical innovation can meet the demanding imaging requirements imposed by the small animal. A functional cardiac magnetic resonance microscopy technique was developed by integrating improvements in physiological control, imaging hardware, biological synchronization of imaging, and pulse sequence design to achieve high-quality images of the murine heart with high spatial and temporal resolution. The specific methods and results from three different sets of imaging experiments are presented: (1) 2D functional imaging in the rat with spatial resolution of 175 mum2 x 1 mm and temporal resolution of 10 ms; (2) 3D functional imaging in the rat with spatial resolution of 100 mum 2 x 500 mum and temporal resolution of 30 ms; and (3) 2D functional imaging in the mouse with spatial resolution down to 100 mum2 x 1 mm and temporal resolution of 10 ms. The cardiac microscopy technique presented here represents a novel collection of technologies capable of acquiring routine high-quality images of murine cardiac structure and function with minimal artifacts and markedly higher spatial resolution compared to conventional techniques. This work is poised to serve a valuable role in the evaluation of cardiovascular disease and should find broad application in studies ranging from basic pathophysiology to drug discovery.

Cardiac function and perfusion dynamics measured on a beat-by-beat basis in the live mouse using ultra-fast 4D optoacoustic imaging

NASA Astrophysics Data System (ADS)

Ford, Steven J.; Deán-Ben, Xosé L.; Razansky, Daniel

2015-03-01

The fast heart rate (~7 Hz) of the mouse makes cardiac imaging and functional analysis difficult when studying mouse models of cardiovascular disease, and cannot be done truly in real-time and 3D using established imaging modalities. Optoacoustic imaging, on the other hand, provides ultra-fast imaging at up to 50 volumetric frames per second, allowing for acquisition of several frames per mouse cardiac cycle. In this study, we combined a recently-developed 3D optoacoustic imaging array with novel analytical techniques to assess cardiac function and perfusion dynamics of the mouse heart at high, 4D spatiotemporal resolution. In brief, the heart of an anesthetized mouse was imaged over a series of multiple volumetric frames. In another experiment, an intravenous bolus of indocyanine green (ICG) was injected and its distribution was subsequently imaged in the heart. Unique temporal features of the cardiac cycle and ICG distribution profiles were used to segment the heart from background and to assess cardiac function. The 3D nature of the experimental data allowed for determination of cardiac volumes at ~7-8 frames per mouse cardiac cycle, providing important cardiac function parameters (e.g., stroke volume, ejection fraction) on a beat-by-beat basis, which has been previously unachieved by any other cardiac imaging modality. Furthermore, ICG distribution dynamics allowed for the determination of pulmonary transit time and thus additional quantitative measures of cardiovascular function. This work demonstrates the potential for optoacoustic cardiac imaging and is expected to have a major contribution toward future preclinical studies of animal models of cardiovascular health and disease.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

WE-AB-202-04: Statistical Evaluation of Lung Function Using 4DCT Ventilation Imaging: Proton Therapy VS IMRT

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

Huang, Q; Zhang, M; Chen, T

Purpose: Variation in function of different lung regions has been ignored so far for conventional lung cancer treatment planning, which may lead to higher risk of radiation induced lung disease. 4DCT based lung ventilation imaging provides a novel yet convenient approach for lung functional imaging as 4DCT is taken as routine for lung cancer treatment. Our work aims to evaluate the impact of accounting for spatial heterogeneity in lung function using 4DCT based lung ventilation imaging for proton and IMRT plans. Methods: Six patients with advanced stage lung cancer of various tumor locations were retrospectively evaluated for the study. Protonmore » and IMRT plans were designed following identical planning objective and constrains for each patient. Ventilation images were calculated from patients’ 4DCT using deformable image registration implemented by Velocity AI software based on Jacobian-metrics. Lung was delineated into two function level regions based on ventilation (low and high functional area). High functional region was defined as lung ventilation greater than 30%. Dose distribution and statistics in different lung function area was calculated for patients. Results: Variation in dosimetric statistics of different function lung region was observed between proton and IMRT plans. In all proton plans, high function lung regions receive lower maximum dose (100.2%–108.9%), compared with IMRT plans (106.4%–119.7%). Interestingly, three out of six proton plans gave higher mean dose by up to 2.2% than IMRT to high function lung region. Lower mean dose (lower by up to 14.1%) and maximum dose (lower by up to 9%) were observed in low function lung for proton plans. Conclusion: A systematic approach was developed to generate function lung ventilation imaging and use it to evaluate plans. This method hold great promise in function analysis of lung during planning. We are currently studying more subjects to evaluate this tool.« less

An Ibm PC/AT-Based Image Acquisition And Processing System For Quantitative Image Analysis

NASA Astrophysics Data System (ADS)

Kim, Yongmin; Alexander, Thomas

1986-06-01

In recent years, a large number of applications have been developed for image processing systems in the area of biological imaging. We have already finished the development of a dedicated microcomputer-based image processing and analysis system for quantitative microscopy. The system's primary function has been to facilitate and ultimately automate quantitative image analysis tasks such as the measurement of cellular DNA contents. We have recognized from this development experience, and interaction with system users, biologists and technicians, that the increasingly widespread use of image processing systems, and the development and application of new techniques for utilizing the capabilities of such systems, would generate a need for some kind of inexpensive general purpose image acquisition and processing system specially tailored for the needs of the medical community. We are currently engaged in the development and testing of hardware and software for a fairly high-performance image processing computer system based on a popular personal computer. In this paper, we describe the design and development of this system. Biological image processing computer systems have now reached a level of hardware and software refinement where they could become convenient image analysis tools for biologists. The development of a general purpose image processing system for quantitative image analysis that is inexpensive, flexible, and easy-to-use represents a significant step towards making the microscopic digital image processing techniques more widely applicable not only in a research environment as a biologist's workstation, but also in clinical environments as a diagnostic tool.

Malware analysis using visualized image matrices.

PubMed

Han, KyoungSoo; Kang, BooJoong; Im, Eul Gyu

2014-01-01

This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively.

Scaling Analysis of Ocean Surface Turbulent Heterogeneities from Satellite Remote Sensing: Use of 2D Structure Functions.

PubMed

Renosh, P R; Schmitt, Francois G; Loisel, Hubert

2015-01-01

Satellite remote sensing observations allow the ocean surface to be sampled synoptically over large spatio-temporal scales. The images provided from visible and thermal infrared satellite observations are widely used in physical, biological, and ecological oceanography. The present work proposes a method to understand the multi-scaling properties of satellite products such as the Chlorophyll-a (Chl-a), and the Sea Surface Temperature (SST), rarely studied. The specific objectives of this study are to show how the small scale heterogeneities of satellite images can be characterised using tools borrowed from the fields of turbulence. For that purpose, we show how the structure function, which is classically used in the frame of scaling time series analysis, can be used also in 2D. The main advantage of this method is that it can be applied to process images which have missing data. Based on both simulated and real images, we demonstrate that coarse-graining (CG) of a gradient modulus transform of the original image does not provide correct scaling exponents. We show, using a fractional Brownian simulation in 2D, that the structure function (SF) can be used with randomly sampled couple of points, and verify that 1 million of couple of points provides enough statistics.

Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms

PubMed Central

Kandaswamy, Umasankar; Rotman, Ziv; Watt, Dana; Schillebeeckx, Ian; Cavalli, Valeria; Klyachko, Vitaly

2013-01-01

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation. PMID:23261652

Automatic Segmenting Structures in MRI's Based on Texture Analysis and Fuzzy Logic

NASA Astrophysics Data System (ADS)

Kaur, Mandeep; Rattan, Munish; Singh, Pushpinder

2017-12-01

The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor's size and tissue's abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI's.

Quantification of oxygen changes in the placenta from BOLD MR image sequences

NASA Astrophysics Data System (ADS)

Porras, Antonio R.; Piella, Gemma; You, Wonsang; Limperopoulos, Catherine; Linguraru, Marius George

2017-03-01

Functional analysis of the placenta is important to analyze and understand its role in fetal growth and development. BOLD MR is a non-invasive technique that has been extensively used for functional analysis of the brain. During the last years, several studies have shown that this dynamic image modality is also useful to extract functional information of the placenta. We propose in this paper a method to track the placenta from a sequence of BOLD MR images acquired under normoxia and hyperoxia conditions with the goal of quantifying how the placenta adapts to oxygenation changes. The method is based on a spatiotemporal transformation model that ensures temporal coherence of the tracked structures. The method was initially applied to four patients with healthy pregnancies. An average MR signal increase of 16.96+/-8.39% during hyperoxia was observed. These automated results are in line with state-of-the-art reports using time-consuming manual segmentations subject to inter-observer errors.

Evaluation of liver function using gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid enhanced magnetic resonance imaging based on a three-dimensional volumetric analysis system.

PubMed

Kudo, Masashi; Gotohda, Naoto; Sugimoto, Motokazu; Kobayashi, Tatsushi; Kojima, Motohiro; Takahashi, Shinichiro; Konishi, Masaru; Hayashi, Ryuichi

2018-06-02

Magnetic resonance imaging with gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (EOB-MRI) is a diagnostic modality for liver tumors. Three-dimensional (3D) volumetric analysis systems using EOB-MRI data are used to simulate liver anatomy for surgery. This study was conducted to investigate clinical utility of a 3D volumetric analysis system on EOB-MRI to evaluate liver function. Between August 2014 and December 2015, 181 patients underwent laboratory and radiological exams as standardized preoperative evaluation for liver surgery. The liver-spleen contrast-enhanced ratio (LSR) was measured by a semi-automated 3D volumetric analysis system on EOB-MRI. First, the inter-evaluator variability of the calculated LSR was evaluated. Additionally, a subset of liver surgical specimens was evaluated histologically by using immunohistochemical staining. Finally, the correlations between the LSR and grading systems of liver function, laboratory data, or histological findings were analyzed. The inter-evaluator correlation coefficient of the measured LSR was 0.986. The mean LSR was significantly correlated with the Child-Pugh score (p = 0.014) and the ALBI score (p < 0.001). Significant correlations were also observed between the LSR and indocyanine green retention rate at 15 min (r = - 0.601, p < 0.001), between the LSR and liver fibrosis stage (r = - 0.556, p < 0.001), and between the LSR and liver steatosis grade (r = - 0.396, p < 0.001). The LSR calculated by a 3D volumetric analysis system on EOB-MRI was highly reproducible and was shown to be correlated with liver function parameters and liver histology. These data suggest that this imaging modality can be a reliable tool to evaluate liver function.

The neuronal correlates of intranasal trigeminal function – An ALE meta-analysis of human functional brain imaging data

PubMed Central

Albrecht, Jessica; Kopietz, Rainer; Frasnelli, Johannes; Wiesmann, Martin; Hummel, Thomas; Lundström, Johan N.

2009-01-01

Almost every odor we encounter in daily life has the capacity to produce a trigeminal sensation. Surprisingly, few functional imaging studies exploring human neuronal correlates of intranasal trigeminal function exist, and results are to some degree inconsistent. We utilized activation likelihood estimation (ALE), a quantitative voxel-based meta-analysis tool, to analyze functional imaging data (fMRI/PET) following intranasal trigeminal stimulation with carbon dioxide (CO2), a stimulus known to exclusively activate the trigeminal system. Meta-analysis tools are able to identify activations common across studies, thereby enabling activation mapping with higher certainty. Activation foci of nine studies utilizing trigeminal stimulation were included in the meta-analysis. We found significant ALE scores, thus indicating consistent activation across studies, in the brainstem, ventrolateral posterior thalamic nucleus, anterior cingulate cortex, insula, precentral gyrus, as well as in primary and secondary somatosensory cortices – a network known for the processing of intranasal nociceptive stimuli. Significant ALE values were also observed in the piriform cortex, insula, and the orbitofrontal cortex, areas known to process chemosensory stimuli, and in association cortices. Additionally, the trigeminal ALE statistics were directly compared with ALE statistics originating from olfactory stimulation, demonstrating considerable overlap in activation. In conclusion, the results of this meta-analysis map the human neuronal correlates of intranasal trigeminal stimulation with high statistical certainty and demonstrate that the cortical areas recruited during the processing of intranasal CO2 stimuli include those outside traditional trigeminal areas. Moreover, through illustrations of the considerable overlap between brain areas that process trigeminal and olfactory information; these results demonstrate the interconnectivity of flavor processing. PMID:19913573

Imaging quality analysis of multi-channel scanning radiometer

NASA Astrophysics Data System (ADS)

Fan, Hong; Xu, Wujun; Wang, Chengliang

2008-03-01

Multi-channel scanning radiometer, on boarding FY-2 geostationary meteorological satellite, plays a key role in remote sensing because of its wide field of view and continuous multi-spectral images acquirements. It is significant to evaluate image quality after performance parameters of the imaging system are validated. Several methods of evaluating imaging quality are discussed. Of these methods, the most fundamental is the MTF. The MTF of photoelectric scanning remote instrument, in the scanning direction, is the multiplication of optics transfer function (OTF), detector transfer function (DTF) and electronics transfer function (ETF). For image motion compensation, moving speed of scanning mirror should be considered. The optical MTF measurement is performed in both the EAST/WEST and NORTH/SOUTH direction, whose values are used for alignment purposes and are used to determine the general health of the instrument during integration and testing. Imaging systems cannot perfectly reproduce what they see and end up "blurring" the image. Many parts of the imaging system can cause blurring. Among these are the optical elements, the sampling of the detector itself, post-processing, or the earth's atmosphere for systems that image through it. Through theory calculation and actual measurement, it is proved that DTF and ETF are the main factors of system MTF and the imaging quality can satisfy the requirement of instrument design.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

NASA Astrophysics Data System (ADS)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [11C]SCH23390 data, showing promising results.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.

PubMed

Novosad, Philip; Reader, Andrew J

2016-06-21

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [(11)C]SCH23390 data, showing promising results.

Retooling Laser Speckle Contrast Analysis Algorithm to Enhance Non-Invasive High Resolution Laser Speckle Functional Imaging of Cutaneous Microcirculation

NASA Astrophysics Data System (ADS)

Gnyawali, Surya C.; Blum, Kevin; Pal, Durba; Ghatak, Subhadip; Khanna, Savita; Roy, Sashwati; Sen, Chandan K.

2017-01-01

Cutaneous microvasculopathy complicates wound healing. Functional assessment of gated individual dermal microvessels is therefore of outstanding interest. Functional performance of laser speckle contrast imaging (LSCI) systems is compromised by motion artefacts. To address such weakness, post-processing of stacked images is reported. We report the first post-processing of binary raw data from a high-resolution LSCI camera. Sharp images of low-flowing microvessels were enabled by introducing inverse variance in conjunction with speckle contrast in Matlab-based program code. Extended moving window averaging enhanced signal-to-noise ratio. Functional quantitative study of blood flow kinetics was performed on single gated microvessels using a free hand tool. Based on detection of flow in low-flow microvessels, a new sharp contrast image was derived. Thus, this work presents the first distinct image with quantitative microperfusion data from gated human foot microvasculature. This versatile platform is applicable to study a wide range of tissue systems including fine vascular network in murine brain without craniotomy as well as that in the murine dorsal skin. Importantly, the algorithm reported herein is hardware agnostic and is capable of post-processing binary raw data from any camera source to improve the sensitivity of functional flow data above and beyond standard limits of the optical system.

Retooling Laser Speckle Contrast Analysis Algorithm to Enhance Non-Invasive High Resolution Laser Speckle Functional Imaging of Cutaneous Microcirculation

PubMed Central

Gnyawali, Surya C.; Blum, Kevin; Pal, Durba; Ghatak, Subhadip; Khanna, Savita; Roy, Sashwati; Sen, Chandan K.

2017-01-01

Cutaneous microvasculopathy complicates wound healing. Functional assessment of gated individual dermal microvessels is therefore of outstanding interest. Functional performance of laser speckle contrast imaging (LSCI) systems is compromised by motion artefacts. To address such weakness, post-processing of stacked images is reported. We report the first post-processing of binary raw data from a high-resolution LSCI camera. Sharp images of low-flowing microvessels were enabled by introducing inverse variance in conjunction with speckle contrast in Matlab-based program code. Extended moving window averaging enhanced signal-to-noise ratio. Functional quantitative study of blood flow kinetics was performed on single gated microvessels using a free hand tool. Based on detection of flow in low-flow microvessels, a new sharp contrast image was derived. Thus, this work presents the first distinct image with quantitative microperfusion data from gated human foot microvasculature. This versatile platform is applicable to study a wide range of tissue systems including fine vascular network in murine brain without craniotomy as well as that in the murine dorsal skin. Importantly, the algorithm reported herein is hardware agnostic and is capable of post-processing binary raw data from any camera source to improve the sensitivity of functional flow data above and beyond standard limits of the optical system. PMID:28106129

Parallel algorithm of real-time infrared image restoration based on total variation theory

NASA Astrophysics Data System (ADS)

Zhu, Ran; Li, Miao; Long, Yunli; Zeng, Yaoyuan; An, Wei

2015-10-01

Image restoration is a necessary preprocessing step for infrared remote sensing applications. Traditional methods allow us to remove the noise but penalize too much the gradients corresponding to edges. Image restoration techniques based on variational approaches can solve this over-smoothing problem for the merits of their well-defined mathematical modeling of the restore procedure. The total variation (TV) of infrared image is introduced as a L1 regularization term added to the objective energy functional. It converts the restoration process to an optimization problem of functional involving a fidelity term to the image data plus a regularization term. Infrared image restoration technology with TV-L1 model exploits the remote sensing data obtained sufficiently and preserves information at edges caused by clouds. Numerical implementation algorithm is presented in detail. Analysis indicates that the structure of this algorithm can be easily implemented in parallelization. Therefore a parallel implementation of the TV-L1 filter based on multicore architecture with shared memory is proposed for infrared real-time remote sensing systems. Massive computation of image data is performed in parallel by cooperating threads running simultaneously on multiple cores. Several groups of synthetic infrared image data are used to validate the feasibility and effectiveness of the proposed parallel algorithm. Quantitative analysis of measuring the restored image quality compared to input image is presented. Experiment results show that the TV-L1 filter can restore the varying background image reasonably, and that its performance can achieve the requirement of real-time image processing.

Chronic auditory hallucinations in schizophrenic patients: MR analysis of the coincidence between functional and morphologic abnormalities.

PubMed

Martí-Bonmatí, Luis; Lull, Juan José; García-Martí, Gracián; Aguilar, Eduardo J; Moratal-Pérez, David; Poyatos, Cecilio; Robles, Montserrat; Sanjuán, Julio

2007-08-01

To prospectively evaluate if functional magnetic resonance (MR) imaging abnormalities associated with auditory emotional stimuli coexist with focal brain reductions in schizophrenic patients with chronic auditory hallucinations. Institutional review board approval was obtained and all participants gave written informed consent. Twenty-one right-handed male patients with schizophrenia and persistent hallucinations (started to hear hallucinations at a mean age of 23 years +/- 10, with 15 years +/- 8 of mean illness duration) and 10 healthy paired participants (same ethnic group [white], age, and education level [secondary school]) were studied. Functional echo-planar T2*-weighted (after both emotional and neutral auditory stimulation) and morphometric three-dimensional gradient-recalled echo T1-weighted MR images were analyzed using Statistical Parametric Mapping (SPM2) software. Brain activation images were extracted by subtracting those with emotional from nonemotional words. Anatomic differences were explored by optimized voxel-based morphometry. The functional and morphometric MR images were overlaid to depict voxels statistically reported by both techniques. A coincidence map was generated by multiplying the emotional subtracted functional MR and volume decrement morphometric maps. Statistical analysis used the general linear model, Student t tests, random effects analyses, and analysis of covariance with a correction for multiple comparisons following the false discovery rate method. Large coinciding brain clusters (P < .005) were found in the left and right middle temporal and superior temporal gyri. Smaller coinciding clusters were found in the left posterior and right anterior cingular gyri, left inferior frontal gyrus, and middle occipital gyrus. The middle and superior temporal and the cingular gyri are closely related to the abnormal neural network involved in the auditory emotional dysfunction seen in schizophrenic patients.

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

PubMed

Vergara, Victor M; Ulloa, Alvaro; Calhoun, Vince D; Boutte, David; Chen, Jiayu; Liu, Jingyu

2014-09-01

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications. Copyright © 2014 Elsevier Inc. All rights reserved.

The Image Data Resource: A Bioimage Data Integration and Publication Platform.

PubMed

Williams, Eleanor; Moore, Josh; Li, Simon W; Rustici, Gabriella; Tarkowska, Aleksandra; Chessel, Anatole; Leo, Simone; Antal, Bálint; Ferguson, Richard K; Sarkans, Ugis; Brazma, Alvis; Salas, Rafael E Carazo; Swedlow, Jason R

2017-08-01

Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR) that collects and integrates imaging data acquired across many different imaging modalities. IDR links data from several imaging modalities, including high-content screening, super-resolution and time-lapse microscopy, digital pathology, public genetic or chemical databases, and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable re-analysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open source platform that others can use to publish their own image data. Thus IDR provides both a novel on-line resource and a software infrastructure that promotes and extends publication and re-analysis of scientific image data.

Optical head tracking for functional magnetic resonance imaging using structured light.

PubMed

Zaremba, Andrei A; MacFarlane, Duncan L; Tseng, Wei-Che; Stark, Andrew J; Briggs, Richard W; Gopinath, Kaundinya S; Cheshkov, Sergey; White, Keith D

2008-07-01

An accurate motion-tracking technique is needed to compensate for subject motion during functional magnetic resonance imaging (fMRI) procedures. Here, a novel approach to motion metrology is discussed. A structured light pattern specifically coded for digital signal processing is positioned onto a fiduciary of the patient. As the patient undergoes spatial transformations in 6 DoF (degrees of freedom), a high-resolution CCD camera captures successive images for analysis on a computing platform. A high-speed image processing algorithm is used to calculate spatial transformations in a time frame commensurate with patient movements (10-100 ms) and with a precision of at least 0.5 microm for translations and 0.1 deg for rotations.

Analysis of nulling phase functions suitable to image plane coronagraphy

NASA Astrophysics Data System (ADS)

Hénault, François; Carlotti, Alexis; Vérinaud, Christophe

2016-07-01

Coronagraphy is a very efficient technique for identifying and characterizing extra-solar planets orbiting in the habitable zone of their parent star, especially in a space environment. An important family of coronagraphs is actually based on phase plates located at an intermediate image plane of the optical system, and spreading the starlight outside the "Lyot" exit pupil plane of the instrument. In this commutation we present a set of candidate phase functions generating a central null at the Lyot plane, and study how it propagates to the image plane of the coronagraph. These functions include linear azimuthal phase ramps (the well-known optical vortex), azimuthally cosine-modulated phase profiles, and circular phase gratings. Nnumerical simulations of the expected null depth, inner working angle, sensitivity to pointing errors, effect of central obscuration located at the pupil or image planes, and effective throughput including image mask and Lyot stop transmissions are presented and discussed. The preliminary conclusion is that azimuthal cosine functions appear as an interesting alternative to the classical optical vortex of integer topological charge.

Digital Correction of Motion Artifacts in Microscopy Image Sequences Collected from Living Animals Using Rigid and Non-Rigid Registration

PubMed Central

Lorenz, Kevin S.; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

2013-01-01

Digital image analysis is a fundamental component of quantitative microscopy. However, intravital microscopy presents many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, lack object edge detail, and characteristically have low signal levels. Intravital microscopy introduces the additional problem of motion artifacts, resulting from respiratory motion and heartbeat from specimens imaged in vivo. This paper describes an image registration technique for use with sequences of intravital microscopy images collected in time-series or in 3D volumes. Our registration method involves both rigid and non-rigid components. The rigid registration component corrects global image translations, while the non-rigid component manipulates a uniform grid of control points defined by B-splines. Each control point is optimized by minimizing a cost function consisting of two parts: a term to define image similarity, and a term to ensure deformation grid smoothness. Experimental results indicate that this approach is promising based on the analysis of several image volumes collected from the kidney, lung, and salivary gland of living rodents. PMID:22092443

Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging.

PubMed

Tanaka, Rie

2016-07-01

Dynamic chest radiography is a flat-panel detector (FPD)-based functional X-ray imaging, which is performed as an additional examination in chest radiography. The large field of view (FOV) of FPDs permits real-time observation of the entire lungs and simultaneous right-and-left evaluation of diaphragm kinetics. Most importantly, dynamic chest radiography provides pulmonary ventilation and circulation findings as slight changes in pixel value even without the use of contrast media; the interpretation is challenging and crucial for a better understanding of pulmonary function. The basic concept was proposed in the 1980s; however, it was not realized until the 2010s because of technical limitations. Dynamic FPDs and advanced digital image processing played a key role for clinical application of dynamic chest radiography. Pulmonary ventilation and circulation can be quantified and visualized for the diagnosis of pulmonary diseases. Dynamic chest radiography can be deployed as a simple and rapid means of functional imaging in both routine and emergency medicine. Here, we focus on the evaluation of pulmonary ventilation and circulation. This review article describes the basic mechanism of imaging findings according to pulmonary/circulation physiology, followed by imaging procedures, analysis method, and diagnostic performance of dynamic chest radiography.

Functional Evaluation of Hidden Figures Object Analysis in Children with Autistic Disorder

ERIC Educational Resources Information Center

Malisza, Krisztina L.; Clancy, Christine; Shiloff, Deborah; Foreman, Derek; Holden, Jeanette; Jones, Cheryl; Paulson, K.; Summers, Randy; Yu, C. T.; Chudley, Albert E.

2011-01-01

Functional magnetic resonance imaging (fMRI) during performance of a hidden figures task (HFT) was used to compare differences in brain function in children diagnosed with autism disorder (AD) compared to children with attention-deficit/hyperactivity disorder (ADHD) and typical controls (TC). Overall greater functional MRI activity was observed in…

Language Mapping Using fMRI and Direct Cortical Stimulation for Brain Tumor Surgery

PubMed Central

Brennan, Nicole Petrovich; Peck, Kyung K.; Holodny, Andrei

2016-01-01

Language functional magnetic resonance imaging for neurosurgical planning is a useful but nuanced technique. Consideration of primary and secondary language anatomy, task selection, and data analysis choices all impact interpretation. In the following chapter, we consider practical considerations and nuances alike for language functional magnetic resonance imaging in the support of and comparison with the neurosurgical gold standard, direct cortical stimulation. Pitfalls and limitations are discussed. PMID:26848555

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome.

PubMed

O'Connor, James P B; Rose, Chris J; Waterton, John C; Carano, Richard A D; Parker, Geoff J M; Jackson, Alan

2015-01-15

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care. ©2014 American Association for Cancer Research.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging.

PubMed

Schultz, Simon R; Copeland, Caroline S; Foust, Amanda J; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size.

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

PubMed Central

Schultz, Simon R.; Copeland, Caroline S.; Foust, Amanda J.; Quicke, Peter; Schuck, Renaud

2017-01-01

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotemporal patterns of neural activity, that scale well with pattern size. PMID:28757657

Digital image analysis of a turbulent flame

NASA Astrophysics Data System (ADS)

Zucherman, L.; Kawall, J. G.; Keffer, J. F.

1988-01-01

Digital image analysis of cine pictures of an unconfined rich premixed turbulent flame has been used to determine structural characteristics of the turbulent/non-turbulent interface of the flame. The results, comprising various moments of the interface position, probability density functions and correlation functions, establish that the instantaneous flame-interface position is essentially a Gaussian random variable with a superimposed quasi-periodical component. The latter is ascribable to a pulsation caused by the convection and the stretching of ring vortices present within the flame. To a first approximation, the flame can be considered similar to a three-dimensional axisymmetric turbulent jet, with superimposed ring vortices, in which combustion occurs.

Subdiffraction incoherent optical imaging via spatial-mode demultiplexing: Semiclassical treatment

NASA Astrophysics Data System (ADS)

Tsang, Mankei

2018-02-01

I present a semiclassical analysis of a spatial-mode demultiplexing (SPADE) measurement scheme for far-field incoherent optical imaging under the effects of diffraction and photon shot noise. Building on previous results that assume two point sources or the Gaussian point-spread function, I generalize SPADE for a larger class of point-spread functions and evaluate its errors in estimating the moments of an arbitrary subdiffraction object. Compared with the limits to direct imaging set by the Cramér-Rao bounds, the results show that SPADE can offer far superior accuracy in estimating second- and higher-order moments.

Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

NASA Astrophysics Data System (ADS)

Nikiforov, M. P.; Reukov, V. V.; Thompson, G. L.; Vertegel, A. A.; Guo, S.; Kalinin, S. V.; Jesse, S.

2009-10-01

Functional recognition imaging in scanning probe microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses at a single spatial location to identify the target behavior, which is reminiscent of associative thinking in the human brain, obviating the need for analytical models. We demonstrate, as an example of recognition imaging, rapid identification of cellular organisms using the difference in electromechanical activity over a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

Image-derived arterial input function for quantitative fluorescence imaging of receptor-drug binding in vivo

PubMed Central

Elliott, Jonathan T.; Samkoe, Kimberley S.; Davis, Scott C.; Gunn, Jason R.; Paulsen, Keith D.; Roberts, David W.; Pogue, Brian W.

2017-01-01

Receptor concentration imaging (RCI) with targeted-untargeted optical dye pairs has enabled in vivo immunohistochemistry analysis in preclinical subcutaneous tumors. Successful application of RCI to fluorescence guided resection (FGR), so that quantitative molecular imaging of tumor-specific receptors could be performed in situ, would have a high impact. However, assumptions of pharmacokinetics, permeability and retention, as well as the lack of a suitable reference region limit the potential for RCI in human neurosurgery. In this study, an arterial input graphic analysis (AIGA) method is presented which is enabled by independent component analysis (ICA). The percent difference in arterial concentration between the image-derived arterial input function (AIFICA) and that obtained by an invasive method (ICACAR) was 2.0 ± 2.7% during the first hour of circulation of a targeted-untargeted dye pair in mice. Estimates of distribution volume and receptor concentration in tumor bearing mice (n = 5) recovered using the AIGA technique did not differ significantly from values obtained using invasive AIF measurements (p=0.12). The AIGA method, enabled by the subject-specific AIFICA, was also applied in a rat orthotopic model of U-251 glioblastoma to obtain the first reported receptor concentration and distribution volume maps during open craniotomy. PMID:26349671

Minimum risk wavelet shrinkage operator for Poisson image denoising.

PubMed

Cheng, Wu; Hirakawa, Keigo

2015-05-01

The pixel values of images taken by an image sensor are said to be corrupted by Poisson noise. To date, multiscale Poisson image denoising techniques have processed Haar frame and wavelet coefficients--the modeling of coefficients is enabled by the Skellam distribution analysis. We extend these results by solving for shrinkage operators for Skellam that minimizes the risk functional in the multiscale Poisson image denoising setting. The minimum risk shrinkage operator of this kind effectively produces denoised wavelet coefficients with minimum attainable L2 error.

Procurement specification color graphic camera system

NASA Technical Reports Server (NTRS)

Prow, G. E.

1980-01-01

The performance and design requirements for a Color Graphic Camera System are presented. The system is a functional part of the Earth Observation Department Laboratory System (EODLS) and will be interfaced with Image Analysis Stations. It will convert the output of a raster scan computer color terminal into permanent, high resolution photographic prints and transparencies. Images usually displayed will be remotely sensed LANDSAT imager scenes.

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner

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

Kotasidis, Fotis A., E-mail: Fotis.Kotasidis@unige.ch; Zaidi, Habib; Geneva Neuroscience Centre, Geneva University, CH-1205 Geneva

2014-06-15

Purpose: The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters. Hence, a detailedmore » investigation into the multidimensional basis function parameter space is needed to evaluate the impact of these parameters on spatial resolution. Methods: Using an array of 12 × 7 printed point sources, along with a custom made phantom, and with the MR magnet on, the system's spatially variant image-based PSF was characterized in detail. Moreover, basis function parameters were systematically varied during reconstruction (list-mode TF OSEM) to evaluate their impact on the reconstructed resolution and the image space PSF. Following the spatial resolution optimization, phantom, and clinical studies were subsequently reconstructed using representative basis function parameters. Results: Based on the analysis and under standard basis function parameters, the axial and tangential components of the PSF were found to be almost invariant under spatial transformations (∼4 mm) while the radial component varied modestly from 4 to 6.7 mm. Using a systematic investigation into the basis function parameter space, the spatial resolution was found to degrade for basis functions with a large radius and small shape parameter. However, it was found that optimizing the spatial resolution in the reconstructed PET images, while having a good basis function superposition and keeping the image representation error to a minimum, is feasible, with the parameter combination range depending upon the scanner's intrinsic resolution characteristics. Conclusions: Using the printed point source array as a MR compatible methodology for experimentally measuring the scanner's PSF, the system's spatially variant resolution properties were successfully evaluated in image space. Overall the PET subsystem exhibits excellent resolution characteristics mainly due to the fact that the raw data are not under-sampled/rebinned, enabling the spatial resolution to be dictated by the scanner's intrinsic resolution and the image reconstruction parameters. Due to the impact of these parameters on the resolution properties of the reconstructed images, the image space PSF varies both under spatial transformations and due to basis function parameter selection. Nonetheless, for a range of basis function parameters, the image space PSF remains unaffected, with the range depending on the scanner's intrinsic resolution properties.« less

Resting-state functional brain connectivity: lessons from functional near-infrared spectroscopy.

PubMed

Niu, Haijing; He, Yong

2014-04-01

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain's low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Advancing multiscale structural mapping of the brain through fluorescence imaging and analysis across length scales

PubMed Central

Hogstrom, L. J.; Guo, S. M.; Murugadoss, K.; Bathe, M.

2016-01-01

Brain function emerges from hierarchical neuronal structure that spans orders of magnitude in length scale, from the nanometre-scale organization of synaptic proteins to the macroscopic wiring of neuronal circuits. Because the synaptic electrochemical signal transmission that drives brain function ultimately relies on the organization of neuronal circuits, understanding brain function requires an understanding of the principles that determine hierarchical neuronal structure in living or intact organisms. Recent advances in fluorescence imaging now enable quantitative characterization of neuronal structure across length scales, ranging from single-molecule localization using super-resolution imaging to whole-brain imaging using light-sheet microscopy on cleared samples. These tools, together with correlative electron microscopy and magnetic resonance imaging at the nanoscopic and macroscopic scales, respectively, now facilitate our ability to probe brain structure across its full range of length scales with cellular and molecular specificity. As these imaging datasets become increasingly accessible to researchers, novel statistical and computational frameworks will play an increasing role in efforts to relate hierarchical brain structure to its function. In this perspective, we discuss several prominent experimental advances that are ushering in a new era of quantitative fluorescence-based imaging in neuroscience along with novel computational and statistical strategies that are helping to distil our understanding of complex brain structure. PMID:26855758

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer's disease.

PubMed

Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong

2016-07-01

Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).

Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

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

Bhateja, Vikrant, E-mail: bhateja.vikrant@gmail.com, E-mail: nhuongld@hus.edu.vn; Moin, Aisha; Srivastava, Anuja

Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Componentmore » Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).« less

Color image analysis of contaminants and bacteria transport in porous media

NASA Astrophysics Data System (ADS)

Rashidi, Mehdi; Dehmeshki, Jamshid; Daemi, Mohammad F.; Cole, Larry; Dickenson, Eric

1997-10-01

Transport of contaminants and bacteria in aqueous heterogeneous saturated porous systems have been studied experimentally using a novel fluorescent microscopic imaging technique. The approach involves color visualization and quantification of bacterium and contaminant distributions within a transparent porous column. By introducing stained bacteria and an organic dye as a contaminant into the column and illuminating the porous regions with a planar sheet of laser beam, contaminant and bacterial transport processes through the porous medium can be observed and measured microscopically. A computer controlled color CCD camera is used to record the fluorescent images as a function of time. These images are recorded by a frame accurate high resolution VCR and are then analyzed using a color image analysis code written in our laboratories. The color images are digitized this way and simultaneous concentration and velocity distributions of both contaminant and bacterium are evaluated as a function of time and pore characteristics. The approach provides a unique dynamic probe to observe these transport processes microscopically. These results are extremely valuable in in-situ bioremediation problems since microscopic particle-contaminant- bacterium interactions are the key to understanding and optimization of these processes.

Imaging samples in silica aerogel using an experimental point spread function.

PubMed

White, Amanda J; Ebel, Denton S

2015-02-01

Light microscopy is a powerful tool that allows for many types of samples to be examined in a rapid, easy, and nondestructive manner. Subsequent image analysis, however, is compromised by distortion of signal by instrument optics. Deconvolution of images prior to analysis allows for the recovery of lost information by procedures that utilize either a theoretically or experimentally calculated point spread function (PSF). Using a laser scanning confocal microscope (LSCM), we have imaged whole impact tracks of comet particles captured in silica aerogel, a low density, porous SiO2 solid, by the NASA Stardust mission. In order to understand the dynamical interactions between the particles and the aerogel, precise grain location and track volume measurement are required. We report a method for measuring an experimental PSF suitable for three-dimensional deconvolution of imaged particles in aerogel. Using fluorescent beads manufactured into Stardust flight-grade aerogel, we have applied a deconvolution technique standard in the biological sciences to confocal images of whole Stardust tracks. The incorporation of an experimentally measured PSF allows for better quantitative measurements of the size and location of single grains in aerogel and more accurate measurements of track morphology.

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis

PubMed Central

Garrison, Kathleen A.; Rogalsky, Corianne; Sheng, Tong; Liu, Brent; Damasio, Hanna; Winstein, Carolee J.; Aziz-Zadeh, Lisa S.

2015-01-01

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant’s structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant’s non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design. PMID:26441816

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain

PubMed Central

Huppert, Theodore J.; Diamond, Solomon G.; Franceschini, Maria A.; Boas, David A.

2009-01-01

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data. PMID:19340120

Spatially weighted mutual information image registration for image guided radiation therapy.

PubMed

Park, Samuel B; Rhee, Frank C; Monroe, James I; Sohn, Jason W

2010-09-01

To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT). It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically "important" areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/ MVCT image sets. The SWMI registration with a Gaussian weight function (SWMI-GW) was tested between two different imaging modalities: CT and MRI image sets. SWMI-GW converges 10% faster than registration using mutual information with an ROI. SWMI-GW as well as SWMI with SOI-based weight function (SWMI-SOI) shows better compensation of the target organ's deformation and neighboring critical organs' deformation. SWMI-GW was also used to successfully fuse MRI and CT images. Rigid-body image registration using our SWMI-GW and SWMI-SOI as cost functions can achieve better registration results in (a) designated image region(s) as well as faster convergence. With the theoretical foundation established, we believe SWMI could be extended to larger clinical testing.

Accuracy of lung nodule density on HRCT: analysis by PSF-based image simulation.

PubMed

Ohno, Ken; Ohkubo, Masaki; Marasinghe, Janaka C; Murao, Kohei; Matsumoto, Toru; Wada, Shinichi

2012-11-08

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT-based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high-resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF-based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT-based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals.

Advanced Land Imager Assessment System

NASA Technical Reports Server (NTRS)

Chander, Gyanesh; Choate, Mike; Christopherson, Jon; Hollaren, Doug; Morfitt, Ron; Nelson, Jim; Nelson, Shar; Storey, James; Helder, Dennis; Ruggles, Tim;

Adipose Tissue Quantification by Imaging Methods: A Proposed Classification

PubMed Central

Shen, Wei; Wang, ZiMian; Punyanita, Mark; Lei, Jianbo; Sinav, Ahmet; Kral, John G.; Imielinska, Celina; Ross, Robert; Heymsfield, Steven B.

2007-01-01

Recent advances in imaging techniques and understanding of differences in the molecular biology of adipose tissue has rendered classical anatomy obsolete, requiring a new classification of the topography of adipose tissue. Adipose tissue is one of the largest body compartments, yet a classification that defines specific adipose tissue depots based on their anatomic location and related functions is lacking. The absence of an accepted taxonomy poses problems for investigators studying adipose tissue topography and its functional correlates. The aim of this review was to critically examine the literature on imaging of whole body and regional adipose tissue and to create the first systematic classification of adipose tissue topography. Adipose tissue terminology was examined in over 100 original publications. Our analysis revealed inconsistencies in the use of specific definitions, especially for the compartment termed “visceral” adipose tissue. This analysis leads us to propose an updated classification of total body and regional adipose tissue, providing a well-defined basis for correlating imaging studies of specific adipose tissue depots with molecular processes. PMID:12529479

Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome

PubMed Central

O’Connor, James P.B.; Rose, Chris J.; Waterton, John C.; Carano, Richard A.D.; Parker, Geoff J.M.; Jackson, Alan

2014-01-01

Tumors exhibit genomic and phenotypic heterogeneity which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks. These methods can establish whether one tumor is more or less heterogeneous than another and can identify sub-regions with differing biology. In this article we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, rather than be developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care. PMID:25421725

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; Chang, Catie; Duan, Qi; van Gelderen, Peter; Duyn, Jeff H.

2014-01-01

Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity. PMID:23796947

Multiplexed 3D FRET imaging in deep tissue of live embryos

PubMed Central

Zhao, Ming; Wan, Xiaoyang; Li, Yu; Zhou, Weibin; Peng, Leilei

2015-01-01

Current deep tissue microscopy techniques are mostly restricted to intensity mapping of fluorophores, which significantly limit their applications in investigating biochemical processes in vivo. We present a deep tissue multiplexed functional imaging method that probes multiple Förster resonant energy transfer (FRET) sensors in live embryos with high spatial resolution. The method simultaneously images fluorescence lifetimes in 3D with multiple excitation lasers. Through quantitative analysis of triple-channel intensity and lifetime images, we demonstrated that Ca2+ and cAMP levels of live embryos expressing dual FRET sensors can be monitored simultaneously at microscopic resolution. The method is compatible with a broad range of FRET sensors currently available for probing various cellular biochemical functions. It opens the door to imaging complex cellular circuitries in whole live organisms. PMID:26387920

Electrochemical imaging of cells and tissues

PubMed Central

Lin, Tzu-En; Rapino, Stefania; Girault, Hubert H.

2018-01-01

The technological and experimental progress in electrochemical imaging of biological specimens is discussed with a view on potential applications for skin cancer diagnostics, reproductive medicine and microbial testing. The electrochemical analysis of single cell activity inside cell cultures, 3D cellular aggregates and microtissues is based on the selective detection of electroactive species involved in biological functions. Electrochemical imaging strategies, based on nano/micrometric probes scanning over the sample and sensor array chips, respectively, can be made sensitive and selective without being affected by optical interference as many other microscopy techniques. The recent developments in microfabrication, electronics and cell culturing/tissue engineering have evolved in affordable and fast-sampling electrochemical imaging platforms. We believe that the topics discussed herein demonstrate the applicability of electrochemical imaging devices in many areas related to cellular functions. PMID:29899947

Theory of time-resolved photoelectron imaging. Comparison of a density functional with a time-dependent density functional approach

NASA Astrophysics Data System (ADS)

Suzuki, Yoshi-ichi; Seideman, Tamar; Stener, Mauro

2004-01-01

Time-resolved photoelectron differential cross sections are computed within a quantum dynamical theory that combines a formally exact solution of the nuclear dynamics with density functional theory (DFT)-based approximations of the electronic dynamics. Various observables of time-resolved photoelectron imaging techniques are computed at the Kohn-Sham and at the time-dependent DFT levels. Comparison of the results serves to assess the reliability of the former method and hence its usefulness as an economic approach for time-domain photoelectron cross section calculations, that is applicable to complex polyatomic systems. Analysis of the matrix elements that contain the electronic dynamics provides insight into a previously unexplored aspect of femtosecond-resolved photoelectron imaging.

Use of local noise power spectrum and wavelet analysis in quantitative image quality assurance for EPIDs

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

Lee, Soyoung

Purpose: To investigate the use of local noise power spectrum (NPS) to characterize image noise and wavelet analysis to isolate defective pixels and inter-subpanel flat-fielding artifacts for quantitative quality assurance (QA) of electronic portal imaging devices (EPIDs). Methods: A total of 93 image sets including custom-made bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Global quantitative metrics such as modulation transform function (MTF), NPS, and detective quantum efficiency (DQE) were computed for each image set. Local NPS was also calculated for individual subpanels by sampling region of interests within each subpanelmore » of the EPID. The 1D NPS, obtained by radially averaging the 2D NPS, was fitted to a power-law function. The r-square value of the linear regression analysis was used as a singular metric to characterize the noise properties of individual subpanels of the EPID. The sensitivity of the local NPS was first compared with the global quantitative metrics using historical image sets. It was then compared with two commonly used commercial QA systems with images collected after applying two different EPID calibration methods (single-level gain and multilevel gain). To detect isolated defective pixels and inter-subpanel flat-fielding artifacts, Haar wavelet transform was applied on the images. Results: Global quantitative metrics including MTF, NPS, and DQE showed little change over the period of data collection. On the contrary, a strong correlation between the local NPS (r-square values) and the variation of the EPID noise condition was observed. The local NPS analysis indicated image quality improvement with the r-square values increased from 0.80 ± 0.03 (before calibration) to 0.85 ± 0.03 (after single-level gain calibration) and to 0.96 ± 0.03 (after multilevel gain calibration), while the commercial QA systems failed to distinguish the image quality improvement between the two calibration methods. With wavelet analysis, defective pixels and inter-subpanel flat-fielding artifacts were clearly identified as spikes after thresholding the inversely transformed images. Conclusions: The proposed local NPS (r-square values) showed superior sensitivity to the noise level variations of individual subpanels compared with global quantitative metrics such as MTF, NPS, and DQE. Wavelet analysis was effective in detecting isolated defective pixels and inter-subpanel flat-fielding artifacts. The proposed methods are promising for the early detection of imaging artifacts of EPIDs.« less

Time-series analysis of lung texture on bone-suppressed dynamic chest radiograph for the evaluation of pulmonary function: a preliminary study

NASA Astrophysics Data System (ADS)

Tanaka, Rie; Matsuda, Hiroaki; Sanada, Shigeru

2017-03-01

The density of lung tissue changes as demonstrated on imagery is dependent on the relative increases and decreases in the volume of air and lung vessels per unit volume of lung. Therefore, a time-series analysis of lung texture can be used to evaluate relative pulmonary function. This study was performed to assess a time-series analysis of lung texture on dynamic chest radiographs during respiration, and to demonstrate its usefulness in the diagnosis of pulmonary impairments. Sequential chest radiographs of 30 patients were obtained using a dynamic flat-panel detector (FPD; 100 kV, 0.2 mAs/pulse, 15 frames/s, SID = 2.0 m; Prototype, Konica Minolta). Imaging was performed during respiration, and 210 images were obtained over 14 seconds. Commercial bone suppression image-processing software (Clear Read Bone Suppression; Riverain Technologies, Miamisburg, Ohio, USA) was applied to the sequential chest radiographs to create corresponding bone suppression images. Average pixel values, standard deviation (SD), kurtosis, and skewness were calculated based on a density histogram analysis in lung regions. Regions of interest (ROIs) were manually located in the lungs, and the same ROIs were traced by the template matching technique during respiration. Average pixel value effectively differentiated regions with ventilatory defects and normal lung tissue. The average pixel values in normal areas changed dynamically in synchronization with the respiratory phase, whereas those in regions of ventilatory defects indicated reduced variations in pixel value. There were no significant differences between ventilatory defects and normal lung tissue in the other parameters. We confirmed that time-series analysis of lung texture was useful for the evaluation of pulmonary function in dynamic chest radiography during respiration. Pulmonary impairments were detected as reduced changes in pixel value. This technique is a simple, cost-effective diagnostic tool for the evaluation of regional pulmonary function.

Analysis of a New Variational Model to Restore Point-Like and Curve-Like Singularities in Imaging

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

Aubert, Gilles, E-mail: gaubert@unice.fr; Blanc-Feraud, Laure, E-mail: Laure.Blanc-Feraud@inria.fr; Graziani, Daniele, E-mail: Daniele.Graziani@inria.fr

2013-02-15

The paper is concerned with the analysis of a new variational model to restore point-like and curve-like singularities in biological images. To this aim we investigate the variational properties of a suitable energy which governs these pathologies. Finally in order to realize numerical experiments we minimize, in the discrete setting, a regularized version of this functional by fast descent gradient scheme.

Concurrent Image Processing Executive (CIPE). Volume 3: User's guide

NASA Technical Reports Server (NTRS)

Lee, Meemong; Cooper, Gregory T.; Groom, Steven L.; Mazer, Alan S.; Williams, Winifred I.; Kong, Mih-Seh

1990-01-01

CIPE (the Concurrent Image Processing Executive) is both an executive which organizes the parameter inputs for hypercube applications and an environment which provides temporary data workspace and simple real-time function definition facilities for image analysis. CIPE provides two types of user interface. The Command Line Interface (CLI) provides a simple command-driven environment allowing interactive function definition and evaluation of algebraic expressions. The menu interface employs a hierarchical screen-oriented menu system where the user is led through a menu tree to any specific application and then given a formatted panel screen for parameter entry. How to initialize the system through the setup function, how to read data into CIPE symbols, how to manipulate and display data through the use of executive functions, and how to run an application in either user interface mode, are described.

Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM.

PubMed

Razifar, Pasha; Sandström, Mattias; Schnieder, Harald; Långström, Bengt; Maripuu, Enn; Bengtsson, Ewert; Bergström, Mats

2005-08-25

Positron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone. Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT. Experiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images. The most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images. The combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred.

Quantitative Assessment of Regional Wall Motion Abnormalities Using Dual-Energy Digital Subtraction Intravenous Ventriculography

NASA Astrophysics Data System (ADS)

McCollough, Cynthia H.

Healthy portions of the left ventricle (LV) can often compensate for regional dysfunction, thereby masking regional disease when global indices of LV function are employed. Thus, quantitation of regional function provides a more useful method of assessing LV function, especially in diseases that have regional effects such as coronary artery disease. This dissertation studied the ability of a phase -matched dual-energy digital subtraction angiography (DE -DSA) technique to quantitate changes in regional LV systolic volume. The potential benefits and a theoretical description of the DE imaging technique are detailed. A correlated noise reduction algorithm is also presented which raises the signal-to-noise ratio of DE images by a factor of 2 -4. Ten open-chest dogs were instrumented with transmural ultrasonic crystals to assess regional LV function in terms of systolic normalized-wall-thickening rate (NWTR) and percent-systolic-thickening (PST). A pneumatic occluder was placed on the left-anterior-descending (LAD) coronary artery to temporarily reduce myocardial blood flow, thereby changing regional LV function in the LAD bed. DE-DSA intravenous left ventriculograms were obtained at control and four levels of graded myocardial ischemia, as determined by reductions in PST. Phase-matched images displaying changes in systolic contractile function were created by subtracting an end-systolic (ES) control image from ES images acquired at each level of myocardial ischemia. The resulting wall-motion difference signal (WMD), which represents a change in regional systolic volume between the control and ischemic states, was quantitated by videodensitometry and compared with changes in NWTR and PST. Regression analysis of 56 data points from 10 animals shows a linear relationship between WMD and both NWTR and PST: WMD = -2.46 NWTR + 13.9, r = 0.64, p < 0.001; WMD = -2.11 PST + 18.4, r = 0.54, p < 0.001. Thus, changes in regional ES LV volume between rest and ischemic states, as measured using the described imaging technique, appear linearly related to changes in wall-thickening, as measured using transmural ultrasonic crystals. This type of image analysis may prove useful in a variety of clinical and research applications and further investigation is proposed.

Functional connectivity of the rodent brain using optical imaging

NASA Astrophysics Data System (ADS)

Guevara Codina, Edgar

The aim of this thesis is to apply functional connectivity in a variety of animal models, using several optical imaging modalities. Even at rest, the brain shows high metabolic activity: the correlation in slow spontaneous fluctuations identifies remotely connected areas of the brain; hence the term "functional connectivity". Ongoing changes in spontaneous activity may provide insight into the neural processing that takes most of the brain metabolic activity, and so may provide a vast source of disease related changes. Brain hemodynamics may be modified during disease and affect resting-state activity. The thesis aims to better understand these changes in functional connectivity due to disease, using functional optical imaging. The optical imaging techniques explored in the first two contributions of this thesis are Optical Imaging of Intrinsic Signals and Laser Speckle Contrast Imaging, together they can estimate the metabolic rate of oxygen consumption, that closely parallels neural activity. They both have adequate spatial and temporal resolution and are well adapted to image the convexity of the mouse cortex. In the last article, a depth-sensitive modality called photoacoustic tomography was used in the newborn rat. Optical coherence tomography and laminar optical tomography were also part of the array of imaging techniques developed and applied in other collaborations. The first article of this work shows the changes in functional connectivity in an acute murine model of epileptiform activity. Homologous correlations are both increased and decreased with a small dependence on seizure duration. These changes suggest a potential decoupling between the hemodynamic parameters in resting-state networks, underlining the importance to investigate epileptic networks with several independent hemodynamic measures. The second study examines a novel murine model of arterial stiffness: the unilateral calcification of the right carotid. Seed-based connectivity analysis showed a decreasing trend of homologous correlation in the motor and cingulate cortices. Graph analyses showed a randomization of the cortex functional networks, suggesting a loss of connectivity, more specifically in the motor cortex ipsilateral to the treated carotid; however these changes are not reflected in differentiated metabolic estimates. Confounds remain due to the fact that carotid rigidification gives rise to neural decline in the hippocampus as well as unilateral alteration of vascular pulsatility; however the results support the need to look at several hemodynamic parameters when imaging the brain after arterial remodeling. The third article of this thesis studies a model of inflammatory injury on the newborn rat. Oxygen saturation and functional connectivity were assessed with photoacoustic tomography. Oxygen saturation was decreased in the site of the lesion and on the cortex ipsilateral to the injury; however this decrease is not fully explained by hypovascularization revealed by histology. Seed-based functional connectivity analysis showed that inter-hemispheric connectivity is not affected by inflammatory injury.

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions.

PubMed

Saha, Tanumoy; Rathmann, Isabel; Galic, Milos

2017-07-11

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

Design of an image encryption scheme based on a multiple chaotic map

NASA Astrophysics Data System (ADS)

Tong, Xiao-Jun

2013-07-01

In order to solve the problem that chaos is degenerated in limited computer precision and Cat map is the small key space, this paper presents a chaotic map based on topological conjugacy and the chaotic characteristics are proved by Devaney definition. In order to produce a large key space, a Cat map named block Cat map is also designed for permutation process based on multiple-dimensional chaotic maps. The image encryption algorithm is based on permutation-substitution, and each key is controlled by different chaotic maps. The entropy analysis, differential analysis, weak-keys analysis, statistical analysis, cipher random analysis, and cipher sensibility analysis depending on key and plaintext are introduced to test the security of the new image encryption scheme. Through the comparison to the proposed scheme with AES, DES and Logistic encryption methods, we come to the conclusion that the image encryption method solves the problem of low precision of one dimensional chaotic function and has higher speed and higher security.

Geometric error analysis for shuttle imaging spectrometer experiment

NASA Technical Reports Server (NTRS)

Wang, S. J.; Ih, C. H.

1984-01-01

The demand of more powerful tools for remote sensing and management of earth resources steadily increased over the last decade. With the recent advancement of area array detectors, high resolution multichannel imaging spectrometers can be realistically constructed. The error analysis study for the Shuttle Imaging Spectrometer Experiment system is documented for the purpose of providing information for design, tradeoff, and performance prediction. Error sources including the Shuttle attitude determination and control system, instrument pointing and misalignment, disturbances, ephemeris, Earth rotation, etc., were investigated. Geometric error mapping functions were developed, characterized, and illustrated extensively with tables and charts. Selected ground patterns and the corresponding image distortions were generated for direct visual inspection of how the various error sources affect the appearance of the ground object images.

Qualitative and quantitative interpretation of SEM image using digital image processing.

PubMed

Saladra, Dawid; Kopernik, Magdalena

2016-10-01

The aim of the this study is improvement of qualitative and quantitative analysis of scanning electron microscope micrographs by development of computer program, which enables automatic crack analysis of scanning electron microscopy (SEM) micrographs. Micromechanical tests of pneumatic ventricular assist devices result in a large number of micrographs. Therefore, the analysis must be automatic. Tests for athrombogenic titanium nitride/gold coatings deposited on polymeric substrates (Bionate II) are performed. These tests include microshear, microtension and fatigue analysis. Anisotropic surface defects observed in the SEM micrographs require support for qualitative and quantitative interpretation. Improvement of qualitative analysis of scanning electron microscope images was achieved by a set of computational tools that includes binarization, simplified expanding, expanding, simple image statistic thresholding, the filters Laplacian 1, and Laplacian 2, Otsu and reverse binarization. Several modifications of the known image processing techniques and combinations of the selected image processing techniques were applied. The introduced quantitative analysis of digital scanning electron microscope images enables computation of stereological parameters such as area, crack angle, crack length, and total crack length per unit area. This study also compares the functionality of the developed computer program of digital image processing with existing applications. The described pre- and postprocessing may be helpful in scanning electron microscopy and transmission electron microscopy surface investigations. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Comparison of Cornea Module and DermaInspect for noninvasive imaging of ocular surface pathologies

NASA Astrophysics Data System (ADS)

Steven, Philipp; Müller, Maya; Koop, Norbert; Rose, Christian; Hüttmann, Gereon

2009-11-01

Minimally invasive imaging of ocular surface pathologies aims at securing clinical diagnosis without actual tissue probing. For this matter, confocal microscopy (Cornea Module) is in daily use in ophthalmic practice. Multiphoton microscopy is a new optical technique that enables high-resolution imaging and functional analysis of living tissues based on tissue autofluorescence. This study was set up to compare the potential of a multiphoton microscope (DermaInspect) to the Cornea Module. Ocular surface pathologies such as pterygia, papillomae, and nevi were investigated in vivo using the Cornea Module and imaged immediately after excision by DermaInspect. Two excitation wavelengths, fluorescence lifetime imaging and second-harmonic generation (SHG), were used to discriminate different tissue structures. Images were compared with the histopathological assessment of the samples. At wavelengths of 730 nm, multiphoton microscopy exclusively revealed cellular structures. Collagen fibrils were specifically demonstrated by second-harmonic generation. Measurements of fluorescent lifetimes enabled the highly specific detection of goblet cells, erythrocytes, and nevus-cell clusters. At the settings used, DermaInspect reaches higher resolutions than the Cornea Module and obtains additional structural information. The parallel detection of multiphoton excited autofluorescence and confocal imaging could expand the possibilities of minimally invasive investigation of the ocular surface toward functional analysis at higher resolutions.

Image processing and analysis using neural networks for optometry area

NASA Astrophysics Data System (ADS)

Netto, Antonio V.; Ferreira de Oliveira, Maria C.

2002-11-01

In this work we describe the framework of a functional system for processing and analyzing images of the human eye acquired by the Hartmann-Shack technique (HS), in order to extract information to formulate a diagnosis of eye refractive errors (astigmatism, hypermetropia and myopia). The analysis is to be carried out using an Artificial Intelligence system based on Neural Nets, Fuzzy Logic and Classifier Combination. The major goal is to establish the basis of a new technology to effectively measure ocular refractive errors that is based on methods alternative those adopted in current patented systems. Moreover, analysis of images acquired with the Hartmann-Shack technique may enable the extraction of additional information on the health of an eye under exam from the same image used to detect refraction errors.

Open framework for management and processing of multi-modality and multidimensional imaging data for analysis and modelling muscular function

NASA Astrophysics Data System (ADS)

García Juan, David; Delattre, Bénédicte M. A.; Trombella, Sara; Lynch, Sean; Becker, Matthias; Choi, Hon Fai; Ratib, Osman

2014-03-01

Musculoskeletal disorders (MSD) are becoming a big healthcare economical burden in developed countries with aging population. Classical methods like biopsy or EMG used in clinical practice for muscle assessment are invasive and not accurately sufficient for measurement of impairments of muscular performance. Non-invasive imaging techniques can nowadays provide effective alternatives for static and dynamic assessment of muscle function. In this paper we present work aimed toward the development of a generic data structure for handling n-dimensional metabolic and anatomical data acquired from hybrid PET/MR scanners. Special static and dynamic protocols were developed for assessment of physical and functional images of individual muscles of the lower limb. In an initial stage of the project a manual segmentation of selected muscles was performed on high-resolution 3D static images and subsequently interpolated to full dynamic set of contours from selected 2D dynamic images across different levels of the leg. This results in a full set of 4D data of lower limb muscles at rest and during exercise. These data can further be extended to a 5D data by adding metabolic data obtained from PET images. Our data structure and corresponding image processing extension allows for better evaluation of large volumes of multidimensional imaging data that are acquired and processed to generate dynamic models of the moving lower limb and its muscular function.

Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging.

PubMed

Lee, Dong-Hoon; Lee, Do-Wan; Henry, David; Park, Hae-Jin; Han, Bong-Soo; Woo, Dong-Cheol

2018-04-12

To evaluate the effects of signal intensity differences between the b0 image and diffusion tensor imaging (DTI) in the image registration process. To correct signal intensity differences between the b0 image and DTI data, a simple image intensity compensation (SIMIC) method, which is a b0 image re-calculation process from DTI data, was applied before the image registration. The re-calculated b0 image (b0 ext ) from each diffusion direction was registered to the b0 image acquired through the MR scanning (b0 nd ) with two types of cost functions and their transformation matrices were acquired. These transformation matrices were then used to register the DTI data. For quantifications, the dice similarity coefficient (DSC) values, diffusion scalar matrix, and quantified fibre numbers and lengths were calculated. The combined SIMIC method with two cost functions showed the highest DSC value (0.802 ± 0.007). Regarding diffusion scalar values and numbers and lengths of fibres from the corpus callosum, superior longitudinal fasciculus, and cortico-spinal tract, only using normalised cross correlation (NCC) showed a specific tendency toward lower values in the brain regions. Image-based distortion correction with SIMIC for DTI data would help in image analysis by accounting for signal intensity differences as one additional option for DTI analysis. • We evaluated the effects of signal intensity differences at DTI registration. • The non-diffusion-weighted image re-calculation process from DTI data was applied. • SIMIC can minimise the signal intensity differences at DTI registration.

Subcellular object quantification with Squassh3C and SquasshAnalyst.

PubMed

Rizk, Aurélien; Mansouri, Maysam; Ballmer-Hofer, Kurt; Berger, Philipp

2015-11-01

Quantitative image analysis plays an important role in contemporary biomedical research. Squassh is a method for automatic detection, segmentation, and quantification of subcellular structures and analysis of their colocalization. Here we present the applications Squassh3C and SquasshAnalyst. Squassh3C extends the functionality of Squassh to three fluorescence channels and live-cell movie analysis. SquasshAnalyst is an interactive web interface for the analysis of Squassh3C object data. It provides segmentation image overview and data exploration, figure generation, object and image filtering, and a statistical significance test in an easy-to-use interface. The overall procedure combines the Squassh3C plug-in for the free biological image processing program ImageJ and a web application working in conjunction with the free statistical environment R, and it is compatible with Linux, MacOS X, or Microsoft Windows. Squassh3C and SquasshAnalyst are available for download at www.psi.ch/lbr/SquasshAnalystEN/SquasshAnalyst.zip.

Effect of the image resolution on the statistical descriptors of heterogeneous media.

PubMed

Ledesma-Alonso, René; Barbosa, Romeli; Ortegón, Jaime

2018-02-01

The characterization and reconstruction of heterogeneous materials, such as porous media and electrode materials, involve the application of image processing methods to data acquired by scanning electron microscopy or other microscopy techniques. Among them, binarization and decimation are critical in order to compute the correlation functions that characterize the microstructure of the above-mentioned materials. In this study, we present a theoretical analysis of the effects of the image-size reduction, due to the progressive and sequential decimation of the original image. Three different decimation procedures (random, bilinear, and bicubic) were implemented and their consequences on the discrete correlation functions (two-point, line-path, and pore-size distribution) and the coarseness (derived from the local volume fraction) are reported and analyzed. The chosen statistical descriptors (correlation functions and coarseness) are typically employed to characterize and reconstruct heterogeneous materials. A normalization for each of the correlation functions has been performed. When the loss of statistical information has not been significant for a decimated image, its normalized correlation function is forecast by the trend of the original image (reference function). In contrast, when the decimated image does not hold statistical evidence of the original one, the normalized correlation function diverts from the reference function. Moreover, the equally weighted sum of the average of the squared difference, between the discrete correlation functions of the decimated images and the reference functions, leads to a definition of an overall error. During the first stages of the gradual decimation, the error remains relatively small and independent of the decimation procedure. Above a threshold defined by the correlation length of the reference function, the error becomes a function of the number of decimation steps. At this stage, some statistical information is lost and the error becomes dependent on the decimation procedure. These results may help us to restrict the amount of information that one can afford to lose during a decimation process, in order to reduce the computational and memory cost, when one aims to diminish the time consumed by a characterization or reconstruction technique, yet maintaining the statistical quality of the digitized sample.

Effect of the image resolution on the statistical descriptors of heterogeneous media

NASA Astrophysics Data System (ADS)

Ledesma-Alonso, René; Barbosa, Romeli; Ortegón, Jaime

2018-02-01

The characterization and reconstruction of heterogeneous materials, such as porous media and electrode materials, involve the application of image processing methods to data acquired by scanning electron microscopy or other microscopy techniques. Among them, binarization and decimation are critical in order to compute the correlation functions that characterize the microstructure of the above-mentioned materials. In this study, we present a theoretical analysis of the effects of the image-size reduction, due to the progressive and sequential decimation of the original image. Three different decimation procedures (random, bilinear, and bicubic) were implemented and their consequences on the discrete correlation functions (two-point, line-path, and pore-size distribution) and the coarseness (derived from the local volume fraction) are reported and analyzed. The chosen statistical descriptors (correlation functions and coarseness) are typically employed to characterize and reconstruct heterogeneous materials. A normalization for each of the correlation functions has been performed. When the loss of statistical information has not been significant for a decimated image, its normalized correlation function is forecast by the trend of the original image (reference function). In contrast, when the decimated image does not hold statistical evidence of the original one, the normalized correlation function diverts from the reference function. Moreover, the equally weighted sum of the average of the squared difference, between the discrete correlation functions of the decimated images and the reference functions, leads to a definition of an overall error. During the first stages of the gradual decimation, the error remains relatively small and independent of the decimation procedure. Above a threshold defined by the correlation length of the reference function, the error becomes a function of the number of decimation steps. At this stage, some statistical information is lost and the error becomes dependent on the decimation procedure. These results may help us to restrict the amount of information that one can afford to lose during a decimation process, in order to reduce the computational and memory cost, when one aims to diminish the time consumed by a characterization or reconstruction technique, yet maintaining the statistical quality of the digitized sample.

The clinico-radiological paradox of cognitive function and MRI burden of white matter lesions in people with multiple sclerosis: A systematic review and meta-analysis.

PubMed

Mollison, Daisy; Sellar, Robin; Bastin, Mark; Mollison, Denis; Chandran, Siddharthan; Wardlaw, Joanna; Connick, Peter

2017-01-01

Moderate correlation exists between the imaging quantification of brain white matter lesions and cognitive performance in people with multiple sclerosis (MS). This may reflect the greater importance of other features, including subvisible pathology, or methodological limitations of the primary literature. To summarise the cognitive clinico-radiological paradox and explore the potential methodological factors that could influence the assessment of this relationship. Systematic review and meta-analysis of primary research relating cognitive function to white matter lesion burden. Fifty papers met eligibility criteria for review, and meta-analysis of overall results was possible in thirty-two (2050 participants). Aggregate correlation between cognition and T2 lesion burden was r = -0.30 (95% confidence interval: -0.34, -0.26). Wide methodological variability was seen, particularly related to key factors in the cognitive data capture and image analysis techniques. Resolving the persistent clinico-radiological paradox will likely require simultaneous evaluation of multiple components of the complex pathology using optimum measurement techniques for both cognitive and MRI feature quantification. We recommend a consensus initiative to support common standards for image analysis in MS, enabling benchmarking while also supporting ongoing innovation.

Detection of low-amplitude in vivo intrinsic signals from an optical imager of retinal function

NASA Astrophysics Data System (ADS)

Barriga, Eduardo S.; T'so, Dan; Pattichis, Marios; Kwon, Young; Kardon, Randy; Abramoff, Michael; Soliz, Peter

2006-02-01

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with today's clinical instruments. Many of today's instruments focus on detecting changes in anatomical structures, such as the nerve fiber layer. Our device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. The functional imager uses a patterned stimulus at wavelength of 535nm. An intrinsic functional signal is collected at a near infrared wavelength. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by imaging system noise. In this paper, we analyze the video sequences from a set of 60 experiments with different patterned stimuli from cats. Using a set of statistical techniques known as Independent Component Analysis (ICA), we estimate the signals present in the videos. Through controlled simulation experiments, we quantify the limits of signal strength in order to detect the physiological signal of interest. The results of the analysis show that, in principle, signal levels of 0.1% (-30dB) can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted. The analysis of the different responses extracted from the videos can give an insight of the functional processes present during the stimulation of the retina.

Sensor image prediction techniques

NASA Astrophysics Data System (ADS)

Stenger, A. J.; Stone, W. R.; Berry, L.; Murray, T. J.

1981-02-01

The preparation of prediction imagery is a complex, costly, and time consuming process. Image prediction systems which produce a detailed replica of the image area require the extensive Defense Mapping Agency data base. The purpose of this study was to analyze the use of image predictions in order to determine whether a reduced set of more compact image features contains enough information to produce acceptable navigator performance. A job analysis of the navigator's mission tasks was performed. It showed that the cognitive and perceptual tasks he performs during navigation are identical to those performed for the targeting mission function. In addition, the results of the analysis of his performance when using a particular sensor can be extended to the analysis of this mission tasks using any sensor. An experimental approach was used to determine the relationship between navigator performance and the type of amount of information in the prediction image. A number of subjects were given image predictions containing varying levels of scene detail and different image features, and then asked to identify the predicted targets in corresponding dynamic flight sequences over scenes of cultural, terrain, and mixed (both cultural and terrain) content.

The Association Between Sexual Satisfaction and Body Image in Women

PubMed Central

Pujols, Yasisca; Meston, Cindy M.; Seal, Brooke N.

2010-01-01

Introduction Although sexual functioning has been linked to sexual satisfaction, it only partially explains the degree to which women report being sexually satisfied. Other factors include quality of life, relational variables, and individual factors such as body image. Of the few studies that have investigated the link between body image and sexual satisfaction, most have considered body image to be a single construct and have shown mixed results. Aim The present study assessed multiple body image variables in order to better understand which aspects of body image influence multiple domains of sexual satisfaction, including sexual communication, compatibility, contentment, personal concern, and relational concern in a community sample of women. Methods Women between the ages of 18 and 49 years in sexual relationships (N = 154) participated in an Internet survey that assessed sexual functioning, five domains of sexual satisfaction, and several body image variables. Main Outcome Measures Body image variables included the sexual attractiveness, weight concern, and physical condition subscales of the Body Esteem Scale, the appearance-based subscale of the Cognitive Distractions During Sexual Activity Scale, and body mass index. Total score of the Sexual Satisfaction Scale for Women was the main outcome measure. Sexual functioning was measured by a modified Female Sexual Function Index. Results Consistent with expectations, correlations indicated significant positive relationships between sexual functioning, sexual satisfaction, and all body image variables. A multiple regression analysis revealed that sexual satisfaction was predicted by high body esteem and low frequency of appearance-based distracting thoughts during sexual activity, even after controlling for sexual functioning status. Conclusion Several aspects of body image, including weight concern, physical condition, sexual attractiveness, and thoughts about the body during sexual activity predict sexual satisfaction in women. The findings suggest that women who experience low sexual satisfaction may benefit from treatments that target these specific aspects of body image. PMID:19968771

Validation of a rapid, semiautomatic image analysis tool for measurement of gastric accommodation and emptying by magnetic resonance imaging

PubMed Central

Dixit, Sudeepa; Fox, Mark; Pal, Anupam

2014-01-01

Magnetic resonance imaging (MRI) has advantages for the assessment of gastrointestinal structures and functions; however, processing MRI data is time consuming and this has limited uptake to a few specialist centers. This study introduces a semiautomatic image processing system for rapid analysis of gastrointestinal MRI. For assessment of simpler regions of interest (ROI) such as the stomach, the system generates virtual images along arbitrary planes that intersect the ROI edges in the original images. This generates seed points that are joined automatically to form contours on each adjacent two-dimensional image and reconstructed in three dimensions (3D). An alternative thresholding approach is available for rapid assessment of complex structures like the small intestine. For assessment of dynamic gastrointestinal function, such as gastric accommodation and emptying, the initial 3D reconstruction is used as reference to process adjacent image stacks automatically. This generates four-dimensional (4D) reconstructions of dynamic volume change over time. Compared with manual processing, this semiautomatic system reduced the user input required to analyze a MRI gastric emptying study (estimated 100 vs. 10,000 mouse clicks). This analysis was not subject to variation in volume measurements seen between three human observers. In conclusion, the image processing platform presented processed large volumes of MRI data, such as that produced by gastric accommodation and emptying studies, with minimal user input. 3D and 4D reconstructions of the stomach and, potentially, other gastrointestinal organs are produced faster and more accurately than manual methods. This system will facilitate the application of MRI in gastrointestinal research and clinical practice. PMID:25540229

The multiple sclerosis visual pathway cohort: understanding neurodegeneration in MS.

PubMed

Martínez-Lapiscina, Elena H; Fraga-Pumar, Elena; Gabilondo, Iñigo; Martínez-Heras, Eloy; Torres-Torres, Ruben; Ortiz-Pérez, Santiago; Llufriu, Sara; Tercero, Ana; Andorra, Magi; Roca, Marc Figueras; Lampert, Erika; Zubizarreta, Irati; Saiz, Albert; Sanchez-Dalmau, Bernardo; Villoslada, Pablo

2014-12-15

Multiple Sclerosis (MS) is an immune-mediated disease of the Central Nervous System with two major underlying etiopathogenic processes: inflammation and neurodegeneration. The latter determines the prognosis of this disease. MS is the main cause of non-traumatic disability in middle-aged populations. The MS-VisualPath Cohort was set up to study the neurodegenerative component of MS using advanced imaging techniques by focusing on analysis of the visual pathway in a middle-aged MS population in Barcelona, Spain. We started the recruitment of patients in the early phase of MS in 2010 and it remains permanently open. All patients undergo a complete neurological and ophthalmological examination including measurements of physical and disability (Expanded Disability Status Scale; Multiple Sclerosis Functional Composite and neuropsychological tests), disease activity (relapses) and visual function testing (visual acuity, color vision and visual field). The MS-VisualPath protocol also assesses the presence of anxiety and depressive symptoms (Hospital Anxiety and Depression Scale), general quality of life (SF-36) and visual quality of life (25-Item National Eye Institute Visual Function Questionnaire with the 10-Item Neuro-Ophthalmic Supplement). In addition, the imaging protocol includes both retinal (Optical Coherence Tomography and Wide-Field Fundus Imaging) and brain imaging (Magnetic Resonance Imaging). Finally, multifocal Visual Evoked Potentials are used to perform neurophysiological assessment of the visual pathway. The analysis of the visual pathway with advance imaging and electrophysilogical tools in parallel with clinical information will provide significant and new knowledge regarding neurodegeneration in MS and provide new clinical and imaging biomarkers to help monitor disease progression in these patients.

Hardware, software, and scanning issues encountered during small animal imaging of photodynamic therapy in the athymic nude rat

NASA Astrophysics Data System (ADS)

Cross, Nathan; Sharma, Rahul; Varghai, Davood; Spring-Robinson, Chandra; Oleinick, Nancy L.; Muzic, Raymond F., Jr.; Dean, David

2007-02-01

Small animal imaging devices are now commonly used to study gene activation and model the effects of potential therapies. We are attempting to develop a protocol that non-invasively tracks the affect of Pc 4-mediated photodynamic therapy (PDT) in a human glioma model using structural image data from micro-CT and/or micro-MR scanning and functional data from 18F-fluorodeoxy-glucose (18F-FDG) micro-PET imaging. Methods: Athymic nude rat U87-derived glioma was imaged by micro-PET and either micro-CT or micro-MR prior to Pc 4-PDT. Difficulty insuring animal anesthesia and anatomic position during the micro-PET, micro-CT, and micro-MR scans required adaptation of the scanning bed hardware. Following Pc 4-PDT the animals were again 18F-FDG micro-PET scanned, euthanized one day later, and their brains were explanted and prepared for H&E histology. Histology provided the gold standard for tumor location and necrosis. The tumor and surrounding brain functional and structural image data were then isolated and coregistered. Results: Surprisingly, both the non-PDT and PDT groups showed an increase in tumor functional activity when we expected this signal to disappear in the group receiving PDT. Co-registration of the functional and structural image data was done manually. Discussion: As expected, micro-MR imaging provided better structural discrimination of the brain tumor than micro-CT. Contrary to expectations, in our preliminary analysis 18F-FDG micro-PET imaging does not readily discriminate the U87 tumors that received Pc 4-PDT. We continue to investigate the utility of micro-PET and other methods of functional imaging to remotely detect the specificity and sensitivity of Pc 4-PDT in deeply placed tumors.

Crossed Cerebellar Tracer Uptake on Acute-Stage 123I-Iomazenil SPECT Imaging Predicts 3-Month Functional Outcome in Patients With Nonfatal Hypertensive Putaminal or Thalamic Hemorrhage.

PubMed

Kojima, Daigo; Komoribayashi, Nobukazu; Omama, Shinichi; Oikawa, Kohki; Fujiwara, Shunrou; Kobayashi, Masakazu; Kubo, Yoshitaka; Terasaki, Kazunori; Ogasawara, Kuniaki

2018-06-01

Whereas SPECT images obtained 180 minutes after administration of I-iomazenil (IMZ) (late images) are proportional to the distribution of central benzodiazepine receptor-binding potential, SPECT images obtained within 30 minutes after I-IMZ administration (early images) correlate with regional brain perfusion. The aim of the present study was to determine whether crossed cerebellar tracer uptake on acute-stage I-IMZ SPECT imaging predicts 3-month functional outcome in patients with nonfatal hypertensive putaminal or thalamic hemorrhage. Forty-six patients underwent early and late SPECT imaging with I-IMZ within 7 days after the onset of hemorrhage. A region of interest was automatically placed in the bilateral cerebellar hemispheres using a 3-dimensional stereotaxic region-of-interest template, and the ratio of the value in the cerebellar hemisphere contralateral to the affected side to that in the ipsilateral cerebellar hemisphere (ARcbl) was calculated in each patient. Each patient's physical function was measured using the modified Rankin scale (mRS) score 3 months after onset. The ARcbl on early (ρ = -0.511, P = 0.0003) and late (ρ = -0.714, P < 0.0001) images correlated with the mRS 3 months after the onset of hemorrhage. Multivariate analysis showed that only a low ARcbl in late images was significantly associated with a poor functional outcome (mRS score ≥3 at 3 months after onset) (95% confidence interval, 0.001-0.003; P = 0.0212). Crossed cerebellar tracer uptake on acute-stage I-IMZ SPECT imaging predicts 3-month functional outcome in patients with nonfatal hypertensive putaminal or thalamic hemorrhage.

TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization.

PubMed

Ollion, Jean; Cochennec, Julien; Loll, François; Escudé, Christophe; Boudier, Thomas

2013-07-15

The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images. Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization. TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence. thomas.boudier@snv.jussieu.fr Supplementary data are available at Bioinformatics online.

Design of compactly supported wavelet to match singularities in medical images

NASA Astrophysics Data System (ADS)

Fung, Carrson C.; Shi, Pengcheng

2002-11-01

Analysis and understanding of medical images has important clinical values for patient diagnosis and treatment, as well as technical implications for computer vision and pattern recognition. One of the most fundamental issues is the detection of object boundaries or singularities, which is often the basis for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. The focus of this work involved taking a correlation based approach toward edge detection, by exploiting some of desirable properties of wavelet analysis. This leads to the possibility of constructing a bank of detectors, consisting of multiple wavelet basis functions of different scales which are optimal for specific types of edges, in order to optimally detect all the edges in an image. Our work involved developing a set of wavelet functions which matches the shape of the ramp and pulse edges. The matching algorithm used focuses on matching the edges in the frequency domain. It was proven that this technique could create matching wavelets applicable at all scales. Results have shown that matching wavelets can be obtained for the pulse edge while the ramp edge requires another matching algorithm.

The usefulness of videomanometry for studying pediatric esophageal motor disease.

PubMed

Kawahara, Hisayoshi; Kubota, Akio; Okuyama, Hiroomi; Oue, Takaharu; Tazuke, Yuko; Okada, Akira

2004-12-01

Abnormalities in esophageal motor function underlie various symptoms in the pediatric population. Manometry remains an important tool for studying esophageal motor function, whereas its analyses have been conducted with considerable subjective interpretation. The usefulness of videomanometry with topographic analysis was examined in the current study. Videomanometry was conducted in 5 patients with primary gastroesophageal reflux disease (GERD), 4 with postoperative esophageal atresia (EA), 1 with congenital esophageal stenosis (CES), and 1 with diffuse esophageal spasms (DES). Digitized videofluoroscopic images were recorded synchronously with manometric digital data in a personal computer. Manometric analysis was conducted with a view of concurrent esophageal contour and bolus transit. Primary GERD patients showed esophageal flow proceeding into the stomach during peristaltic contractions recorded manometrically, whereas patients with EA/CES frequently showed impaired esophageal transit during defective esophageal peristaltic contractions. A characteristic corkscrew appearance and esophageal flow in a to-and-fro fashion were seen with high-amplitude synchronous esophageal contractions in a DES patient. The topographic analysis showed distinctive images characteristic of each pathological condition. Videomanometry is helpful in interpreting manometric data by analyzing concurrent fluoroscopic images. Topographic analyses provide characteristic images reflecting motor abnormalities in pediatric esophageal disease.

Quantitative image analysis for evaluating the coating thickness and pore distribution in coated small particles.

PubMed

Laksmana, F L; Van Vliet, L J; Hartman Kok, P J A; Vromans, H; Frijlink, H W; Van der Voort Maarschalk, K

2009-04-01

This study aims to develop a characterization method for coating structure based on image analysis, which is particularly promising for the rational design of coated particles in the pharmaceutical industry. The method applies the MATLAB image processing toolbox to images of coated particles taken with Confocal Laser Scanning Microscopy (CSLM). The coating thicknesses have been determined along the particle perimeter, from which a statistical analysis could be performed to obtain relevant thickness properties, e.g. the minimum coating thickness and the span of the thickness distribution. The characterization of the pore structure involved a proper segmentation of pores from the coating and a granulometry operation. The presented method facilitates the quantification of porosity, thickness and pore size distribution of a coating. These parameters are considered the important coating properties, which are critical to coating functionality. Additionally, the effect of the coating process variations on coating quality can straight-forwardly be assessed. Enabling a good characterization of the coating qualities, the presented method can be used as a fast and effective tool to predict coating functionality. This approach also enables the influence of different process conditions on coating properties to be effectively monitored, which latterly leads to process tailoring.

Image-Based Predictive Modeling of Heart Mechanics.

PubMed

Wang, V Y; Nielsen, P M F; Nash, M P

2015-01-01

Personalized biophysical modeling of the heart is a useful approach for noninvasively analyzing and predicting in vivo cardiac mechanics. Three main developments support this style of analysis: state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques. In vivo measurements of cardiac structure and function can be integrated using sophisticated computational methods to investigate mechanisms of myocardial function and dysfunction, and can aid in clinical diagnosis and developing personalized treatment. In this article, we review the state-of-the-art in cardiac imaging modalities, model-based interpretation of 3D images of cardiac structure and function, and recent advances in modeling that allow personalized predictions of heart mechanics. We discuss how using such image-based modeling frameworks can increase the understanding of the fundamental biophysics behind cardiac mechanics, and assist with diagnosis, surgical guidance, and treatment planning. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities. We also discuss future directions that can be taken to bridge the gap between basic science and clinical translation.

Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

PubMed

Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

2015-03-01

During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Model-based quantification of image quality

NASA Technical Reports Server (NTRS)

Hazra, Rajeeb; Miller, Keith W.; Park, Stephen K.

1989-01-01

In 1982, Park and Schowengerdt published an end-to-end analysis of a digital imaging system quantifying three principal degradation components: (1) image blur - blurring caused by the acquisition system, (2) aliasing - caused by insufficient sampling, and (3) reconstruction blur - blurring caused by the imperfect interpolative reconstruction. This analysis, which measures degradation as the square of the radiometric error, includes the sample-scene phase as an explicit random parameter and characterizes the image degradation caused by imperfect acquisition and reconstruction together with the effects of undersampling and random sample-scene phases. In a recent paper Mitchell and Netravelli displayed the visual effects of the above mentioned degradations and presented subjective analysis about their relative importance in determining image quality. The primary aim of the research is to use the analysis of Park and Schowengerdt to correlate their mathematical criteria for measuring image degradations with subjective visual criteria. Insight gained from this research can be exploited in the end-to-end design of optical systems, so that system parameters (transfer functions of the acquisition and display systems) can be designed relative to each other, to obtain the best possible results using quantitative measurements.

A Wavelet-Based Algorithm for the Spatial Analysis of Poisson Data

NASA Astrophysics Data System (ADS)

Freeman, P. E.; Kashyap, V.; Rosner, R.; Lamb, D. Q.

2002-01-01

Wavelets are scalable, oscillatory functions that deviate from zero only within a limited spatial regime and have average value zero, and thus may be used to simultaneously characterize the shape, location, and strength of astronomical sources. But in addition to their use as source characterizers, wavelet functions are rapidly gaining currency within the source detection field. Wavelet-based source detection involves the correlation of scaled wavelet functions with binned, two-dimensional image data. If the chosen wavelet function exhibits the property of vanishing moments, significantly nonzero correlation coefficients will be observed only where there are high-order variations in the data; e.g., they will be observed in the vicinity of sources. Source pixels are identified by comparing each correlation coefficient with its probability sampling distribution, which is a function of the (estimated or a priori known) background amplitude. In this paper, we describe the mission-independent, wavelet-based source detection algorithm ``WAVDETECT,'' part of the freely available Chandra Interactive Analysis of Observations (CIAO) software package. Our algorithm uses the Marr, or ``Mexican Hat'' wavelet function, but may be adapted for use with other wavelet functions. Aspects of our algorithm include: (1) the computation of local, exposure-corrected normalized (i.e., flat-fielded) background maps; (2) the correction for exposure variations within the field of view (due to, e.g., telescope support ribs or the edge of the field); (3) its applicability within the low-counts regime, as it does not require a minimum number of background counts per pixel for the accurate computation of source detection thresholds; (4) the generation of a source list in a manner that does not depend upon a detailed knowledge of the point spread function (PSF) shape; and (5) error analysis. These features make our algorithm considerably more general than previous methods developed for the analysis of X-ray image data, especially in the low count regime. We demonstrate the robustness of WAVDETECT by applying it to an image from an idealized detector with a spatially invariant Gaussian PSF and an exposure map similar to that of the Einstein IPC; to Pleiades Cluster data collected by the ROSAT PSPC; and to simulated Chandra ACIS-I image of the Lockman Hole region.

Malware Analysis Using Visualized Image Matrices

PubMed Central

Im, Eul Gyu

2014-01-01

This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images. The proposed method generates RGB-colored pixels on image matrices using the opcode sequences extracted from malware samples and calculates the similarities for the image matrices. Particularly, our proposed methods are available for packed malware samples by applying them to the execution traces extracted through dynamic analysis. When the images are generated, we can reduce the overheads by extracting the opcode sequences only from the blocks that include the instructions related to staple behaviors such as functions and application programming interface (API) calls. In addition, we propose a technique that generates a representative image for each malware family in order to reduce the number of comparisons for the classification of unknown samples and the colored pixel information in the image matrices is used to calculate the similarities between the images. Our experimental results show that the image matrices of malware can effectively be used to classify malware families both statically and dynamically with accuracy of 0.9896 and 0.9732, respectively. PMID:25133202

Information theoretic analysis of linear shift-invariant edge-detection operators

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2012-06-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the influences by the image gathering process. However, experiments show that the image gathering process has a profound impact on the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. We perform an end-to-end information theory based system analysis to assess linear shift-invariant edge-detection algorithms. We evaluate the performance of the different algorithms as a function of the characteristics of the scene and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge-detection algorithm is regarded as having high performance only if the information rate from the scene to the edge image approaches its maximum possible. This goal can be achieved only by jointly optimizing all processes. Our information-theoretic assessment provides a new tool that allows us to compare different linear shift-invariant edge detectors in a common environment.

Development of an image analysis system to monitor the retention of residual cytoplasm by human spermatozoa: correlation with biochemical markers of the cytoplasmic space, oxidative stress, and sperm function.

PubMed

Gomez, E; Buckingham, D W; Brindle, J; Lanzafame, F; Irvine, D S; Aitken, R J

1996-01-01

A method has been developed for quantifying the residual cytoplasm present in the midpiece of human spermatozoa, based upon the imaging of NADH oxidoreductase activity. This procedure used NADH and nitroblue tetrazolium as electron donor and acceptor, respectively, and resulted in the discrete staining of the entire midpiece area, including the residual cytoplasm. Image analysis techniques were then used to generate binary images of the midpiece, from which objective measurements of this cellular domain could be undertaken. Such data were found to be highly correlated with biochemical markers of the cytoplasmic space, such as creatine kinase (CK) and glucose-6-phosphate dehydrogenase (G-6-PDH), in sperm populations depleted of detectable leukocyte contamination. Morphometric analysis of the sperm midpiece was also found to reflect semen quality in that it predicted the proportion of the ejaculate that would be recovered from the high-density region of Percoll gradients and was negatively correlated with the movement and morphology of the spermatozoa in semen. Variation in the retention of excess residual cytoplasm was also associated with differences in the functional competence of washed sperm preparations, both within and between ejaculates. Thus, within-ejaculate comparisons of high- and low-density sperm subpopulations revealed a relative disruption of sperm function in the low-density fraction. This disruption was associated with the presence of excess residual cytoplasm in the midpiece, high concentrations of cytoplasmic enzymes, and the enhanced-generation reactive oxygen species (ROS). Functional differences between individual high-density Percoll preparations were also negatively correlated with the area of the midpiece and the corresponding capacity of the spermatozoa to generate ROS. These findings suggest that one of the factors involved in the etiology of defective sperm function is the incomplete extrusion of germ cell cytoplasm during spermiogenesis as a consequence of which the spermatozoa experience a loss of function associated with the induction of oxidative stress.

Generalized Scalar-on-Image Regression Models via Total Variation.

PubMed

Wang, Xiao; Zhu, Hongtu

2017-01-01

The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data. We develop an efficient penalized total variation optimization to estimate the unknown slope function and other parameters. We also establish nonasymptotic error bounds on the excess risk. These bounds are explicitly specified in terms of sample size, image size, and image smoothness. Our simulations demonstrate a superior performance of GSIRM-TV against many existing approaches. We apply GSIRM-TV to the analysis of hippocampus data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) 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.

Segmentation-based wavelet transform for still-image compression

NASA Astrophysics Data System (ADS)

Mozelle, Gerard; Seghier, Abdellatif; Preteux, Francoise J.

1996-10-01

In order to address simultaneously the two functionalities, content-based scalability required by MPEG-4, we introduce a segmentation-based wavelet transform (SBWT). SBWT takes into account both the mathematical properties of multiresolution analysis and the flexibility of region-based approaches for image compression. The associated methodology has two stages: 1) image segmentation into convex and polygonal regions; 2) 2D-wavelet transform of the signal corresponding to each region. In this paper, we have mathematically studied a method for constructing a multiresolution analysis (VjOmega)j (epsilon) N adapted to a polygonal region which provides an adaptive region-based filtering. The explicit construction of scaling functions, pre-wavelets and orthonormal wavelets bases defined on a polygon is carried out by using scaling functions is established by using the theory of Toeplitz operators. The corresponding expression can be interpreted as a location property which allow defining interior and boundary scaling functions. Concerning orthonormal wavelets and pre-wavelets, a similar expansion is obtained by taking advantage of the properties of the orthogonal projector P(V(j(Omega )) perpendicular from the space Vj(Omega ) + 1 onto the space (Vj(Omega )) perpendicular. Finally the mathematical results provide a simple and fast algorithm adapted to polygonal regions.

Effect of masking phase-only holograms on the quality of reconstructed images.

PubMed

Deng, Yuanbo; Chu, Daping

2016-04-20

A phase-only hologram modulates the phase of the incident light and diffracts it efficiently with low energy loss because of the minimum absorption. Much research attention has been focused on how to generate phase-only holograms, and little work has been done to understand the effect and limitation of their partial implementation, possibly due to physical defects and constraints, in particular as in the practical situations where a phase-only hologram is confined or needs to be sliced or tiled. The present study simulates the effect of masking phase-only holograms on the quality of reconstructed images in three different scenarios with different filling factors, filling positions, and illumination intensity profiles. Quantitative analysis confirms that the width of the image point spread function becomes wider and the image quality decreases, as expected, when the filling factor decreases, and the image quality remains the same for different filling positions as well. The width of the image point spread function as derived from different filling factors shows a consistent behavior to that as measured directly from the reconstructed image, especially as the filling factor becomes small. Finally, mask profiles of different shapes and intensity distributions are shown to have more complicated effects on the image point spread function, which in turn affects the quality and textures of the reconstructed image.

Computerized image analysis for quantitative neuronal phenotyping in zebrafish.

PubMed

Liu, Tianming; Lu, Jianfeng; Wang, Ye; Campbell, William A; Huang, Ling; Zhu, Jinmin; Xia, Weiming; Wong, Stephen T C

2006-06-15

An integrated microscope image analysis pipeline is developed for automatic analysis and quantification of phenotypes in zebrafish with altered expression of Alzheimer's disease (AD)-linked genes. We hypothesize that a slight impairment of neuronal integrity in a large number of zebrafish carrying the mutant genotype can be detected through the computerized image analysis method. Key functionalities of our zebrafish image processing pipeline include quantification of neuron loss in zebrafish embryos due to knockdown of AD-linked genes, automatic detection of defective somites, and quantitative measurement of gene expression levels in zebrafish with altered expression of AD-linked genes or treatment with a chemical compound. These quantitative measurements enable the archival of analyzed results and relevant meta-data. The structured database is organized for statistical analysis and data modeling to better understand neuronal integrity and phenotypic changes of zebrafish under different perturbations. Our results show that the computerized analysis is comparable to manual counting with equivalent accuracy and improved efficacy and consistency. Development of such an automated data analysis pipeline represents a significant step forward to achieve accurate and reproducible quantification of neuronal phenotypes in large scale or high-throughput zebrafish imaging studies.

Technologies for imaging neural activity in large volumes

PubMed Central

Ji, Na; Freeman, Jeremy; Smith, Spencer L.

2017-01-01

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Collecting data from individual planes, conventional microscopy cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here, we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for the processing and analysis of volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics, and help elucidate how brain regions work in concert to support behavior. PMID:27571194

Fundamental limits of image registration performance: Effects of image noise and resolution in CT-guided interventions.

PubMed

Ketcha, M D; de Silva, T; Han, R; Uneri, A; Goerres, J; Jacobson, M; Vogt, S; Kleinszig, G; Siewerdsen, J H

2017-02-11

In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance. To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters. Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose. Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

Temporal and spatial resolution required for imaging myocardial function

NASA Astrophysics Data System (ADS)

Eusemann, Christian D.; Robb, Richard A.

2004-05-01

4-D functional analysis of myocardial mechanics is an area of significant interest and research in cardiology and vascular/interventional radiology. Current multidimensional analysis is limited by insufficient temporal resolution of x-ray and magnetic resonance based techniques, but recent improvements in system design holds hope for faster and higher resolution scans to improve images of moving structures allowing more accurate functional studies, such as in the heart. This paper provides a basis for the requisite temporal and spatial resolution for useful imaging during individual segments of the cardiac cycle. Multiple sample rates during systole and diastole are compared to determine an adequate sample frequency to reduce regional myocardial tracking errors. Concurrently, out-of-plane resolution has to be sufficiently high to minimize partial volume effect. Temporal resolution and out-of-plane spatial resolution are related factors that must be considered together. The data used for this study is a DSR dynamic volume image dataset with high temporal and spatial resolution using implanted fiducial markers to track myocardial motion. The results of this study suggest a reduced exposure and scan time for x-ray and magnetic resonance imaging methods, since a lower sample rate during systole is sufficient, whereas the period of rapid filling during diastole requires higher sampling. This could potentially reduce the cost of these procedures and allow higher patient throughput.

On-Line GIS Analysis and Image Processing for Geoportal Kielce/poland Development

NASA Astrophysics Data System (ADS)

Hejmanowska, B.; Głowienka, E.; Florek-Paszkowski, R.

2016-06-01

GIS databases are widely available on the Internet, but mainly for visualization with limited functionality; very simple queries are possible i.e. attribute query, coordinate readout, line and area measurements or pathfinder. A little more complex analysis (i.e. buffering or intersection) are rare offered. Paper aims at the concept of Geoportal functionality development in the field of GIS analysis. Multi-Criteria Evaluation (MCE) is planned to be implemented in web application. OGC Service is used for data acquisition from the server and results visualization. Advanced GIS analysis is planned in PostGIS and Python programming. In the paper an example of MCE analysis basing on Geoportal Kielce is presented. Other field where Geoportal can be developed is implementation of processing new available satellite images free of charge (Sentinel-2, Landsat 8, ASTER, WV-2). Now we are witnessing a revolution in access to the satellite imagery without charge. This should result in an increase of interest in the use of these data in various fields by a larger number of users, not necessarily specialists in remote sensing. Therefore, it seems reasonable to expand the functionality of Internet's tools for data processing by non-specialists, by automating data collection and prepared predefined analysis.

Texture functions in image analysis: A computationally efficient solution

NASA Technical Reports Server (NTRS)

Cox, S. C.; Rose, J. F.

1983-01-01

A computationally efficient means for calculating texture measurements from digital images by use of the co-occurrence technique is presented. The calculation of the statistical descriptors of image texture and a solution that circumvents the need for calculating and storing a co-occurrence matrix are discussed. The results show that existing efficient algorithms for calculating sums, sums of squares, and cross products can be used to compute complex co-occurrence relationships directly from the digital image input.

Quantitative Image Informatics for Cancer Research (QIICR) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

Imaging has enormous untapped potential to improve cancer research through software to extract and process morphometric and functional biomarkers. In the era of non-cytotoxic treatment agents, multi- modality image-guided ablative therapies and rapidly evolving computational resources, quantitative imaging software can be transformative in enabling minimally invasive, objective and reproducible evaluation of cancer treatment response. Post-processing algorithms are integral to high-throughput analysis and fine- grained differentiation of multiple molecular targets.

Defining Functional Areas in Individual Human Brains using Resting Functional Connectivity MRI

PubMed Central

Cohen, Alexander L.; Fair, Damien A.; Dosenbach, Nico U.F.; Miezin, Francis M.; Dierker, Donna; Van Essen, David C.; Schlaggar, Bradley L.; Petersen, Steven E.

2009-01-01

The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g. topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of correlated activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a region’s function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations. PMID:18367410

PlantCV v2: Image analysis software for high-throughput plant phenotyping

PubMed Central

Abbasi, Arash; Berry, Jeffrey C.; Callen, Steven T.; Chavez, Leonardo; Doust, Andrew N.; Feldman, Max J.; Gilbert, Kerrigan B.; Hodge, John G.; Hoyer, J. Steen; Lin, Andy; Liu, Suxing; Lizárraga, César; Lorence, Argelia; Miller, Michael; Platon, Eric; Tessman, Monica; Sax, Tony

2017-01-01

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning. PMID:29209576

PlantCV v2: Image analysis software for high-throughput plant phenotyping.

PubMed

Gehan, Malia A; Fahlgren, Noah; Abbasi, Arash; Berry, Jeffrey C; Callen, Steven T; Chavez, Leonardo; Doust, Andrew N; Feldman, Max J; Gilbert, Kerrigan B; Hodge, John G; Hoyer, J Steen; Lin, Andy; Liu, Suxing; Lizárraga, César; Lorence, Argelia; Miller, Michael; Platon, Eric; Tessman, Monica; Sax, Tony

2017-01-01

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

PlantCV v2: Image analysis software for high-throughput plant phenotyping

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

Gehan, Malia A.; Fahlgren, Noah; Abbasi, Arash

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details andmore » rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.« less

PlantCV v2: Image analysis software for high-throughput plant phenotyping

DOE PAGES

Gehan, Malia A.; Fahlgren, Noah; Abbasi, Arash; ...

2017-12-01

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here in this paper we present the details andmore » rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.« less

Studying Axon-Astrocyte Functional Interactions by 3D Two-Photon Ca2+ Imaging: A Practical Guide to Experiments and "Big Data" Analysis.

PubMed

Savtchouk, Iaroslav; Carriero, Giovanni; Volterra, Andrea

2018-01-01

Recent advances in fast volumetric imaging have enabled rapid generation of large amounts of multi-dimensional functional data. While many computer frameworks exist for data storage and analysis of the multi-gigabyte Ca 2+ imaging experiments in neurons, they are less useful for analyzing Ca 2+ dynamics in astrocytes, where transients do not follow a predictable spatio-temporal distribution pattern. In this manuscript, we provide a detailed protocol and commentary for recording and analyzing three-dimensional (3D) Ca 2+ transients through time in GCaMP6f-expressing astrocytes of adult brain slices in response to axonal stimulation, using our recently developed tools to perform interactive exploration, filtering, and time-correlation analysis of the transients. In addition to the protocol, we release our in-house software tools and discuss parameters pertinent to conducting axonal stimulation/response experiments across various brain regions and conditions. Our software tools are available from the Volterra Lab webpage at https://wwwfbm.unil.ch/dnf/group/glia-an-active-synaptic-partner/member/volterra-andrea-volterra in the form of software plugins for Image J (NIH)-a de facto standard in scientific image analysis. Three programs are available: MultiROI_TZ_profiler for interactive graphing of several movable ROIs simultaneously, Gaussian_Filter5D for Gaussian filtering in several dimensions, and Correlation_Calculator for computing various cross-correlation parameters on voxel collections through time.

Topics in the two-dimensional sampling and reconstruction of images. [in remote sensing

NASA Technical Reports Server (NTRS)

Schowengerdt, R.; Gray, S.; Park, S. K.

1984-01-01

Mathematical analysis of image sampling and interpolative reconstruction is summarized and extended to two dimensions for application to data acquired from satellite sensors such as the Thematic mapper and SPOT. It is shown that sample-scene phase influences the reconstruction of sampled images, adds a considerable blur to the average system point spread function, and decreases the average system modulation transfer function. It is also determined that the parametric bicubic interpolator with alpha = -0.5 is more radiometrically accurate than the conventional bicubic interpolator with alpha = -1, and this at no additional cost. Finally, the parametric bicubic interpolator is found to be suitable for adaptive implementation by relating the alpha parameter to the local frequency content of an image.

Empirical orthogonal function analysis of cloud-containing coastal zone color scanner images of northeastern North American coastal waters

NASA Technical Reports Server (NTRS)

Eslinger, David L.; O'Brien, James J.; Iverson, Richard L.

1989-01-01

Empirical-orthogonal-function (EOF) analyses were carried out on 36 images of the Mid-Atlantic Bight and the Gulf of Maine, obtained by the CZCS aboard Nimbus 7 for the time period from February 28 through July 9, 1979, with the purpose of determining pigment concentrations in coastal waters. The EOF procedure was modified so as to include images with significant portions of data missing due to cloud obstruction, making it possible to estimate pigment values in areas beneath clouds. The results of image analyses explained observed variances in pigment concentrations and showed a south-to-north pattern corresponding to an April Mid-Atlantic Bight bloom and a June bloom over Nantucket Shoals and Platts Bank.

Wigner analysis of three dimensional pupil with finite lateral aperture

PubMed Central

Chen, Hsi-Hsun; Oh, Se Baek; Zhai, Xiaomin; Tsai, Jui-Chang; Cao, Liang-Cai; Barbastathis, George; Luo, Yuan

2015-01-01

A three dimensional (3D) pupil is an optical element, most commonly implemented on a volume hologram, that processes the incident optical field on a 3D fashion. Here we analyze the diffraction properties of a 3D pupil with finite lateral aperture in the 4-f imaging system configuration, using the Wigner Distribution Function (WDF) formulation. Since 3D imaging pupil is finite in both lateral and longitudinal directions, the WDF of the volume holographic 4-f imager theoretically predicts distinct Bragg diffraction patterns in phase space. These result in asymmetric profiles of diffracted coherent point spread function between degenerate diffraction and Bragg diffraction, elucidating the fundamental performance of volume holographic imaging. Experimental measurements are also presented, confirming the theoretical predictions. PMID:25836443

[MR tomography of the heart].

PubMed

Hahn, D; Beer, M; Sandstede, J

2000-10-01

The introduction of magnetic resonance (MR) tomography has fundamentally changed radiological diagnosis for many diseases. Invasive digital subtraction angiography has already been widely replaced by noninvasive MR angiography for most of the vascular diseases. The rapid technical development of MR imaging in recent years has opened new functional imaging techniques. MR imaging of the heart allows simultaneous measurement of morphological and functional parameters in a single noninvasive examination without any radiation exposure. Because of the high spatial resolution and the reproducibility cine MR imaging is now the gold standard for functional analysis. With the improvement of myocardial perfusion and viability studies many diseases of the heart can be diagnosed in a single examination. MR spectroscopy is the only method which allows a view of the metabolism of the heart. New examinations for vascular imaging and flow quantification complete the goal of "one-stop-shop" imaging of the heart. MR imaging is the only diagnostic modality which allows a complete evaluation of many diseases of the heart with one technique, basic examination as well as follow-up studies. The very rapid improvement in MRI will overcome most of the limitations in the near future, especially concerning MR coronary angiography.

Functional changes of neural circuits in stroke patients with dysphagia: A meta-analysis.

PubMed

Liu, Lu; Xiao, Yuan; Zhang, Wenjing; Yao, Li; Gao, Xin; Chandan, Shah; Lui, Su

2017-08-01

Dysphagia is a common problem in stroke patients with unclear pathogenesis. Several recent functional magnetic resonance imaging (fMRI) studies had been carried out to explore the cerebral functional changes in dysphagic stroke patients. The aim of this study was to analysis these imaging findings using a meta-analysis. We used seed-based d mapping (SDM) to conduct a meta-analysis for dysphagic stroke patients prior to any kind of special treatment for dysphagia. A systematic search was conducted for the relevant studies. SDM meta-analysis method was used to examine regions of increased and decreased functional activation between dysphagic stroke patients and healthy controls. Finally, six studies including 81 stroke patients with dysphagia and 78 healthy controls met the inclusion standards. When compared with healthy controls, stroke patients with dysphagia showed hyperactivation in left cingulate gyrus, left precentral gyrus and right posterior cingulate gyrus, and hypoactivation in right cuneus and left middle frontal gyrus. The hyperactivity of precentral gyrus is crucial in stroke patients with dysphagia and may be associated with the severity of stroke. Besides the motor areas, the default-mode network regions (DMN) and affective network regions (AN) circuits are also involved in dysphagia after stroke. © 2017 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Combined optimization of image-gathering and image-processing systems for scene feature detection

NASA Technical Reports Server (NTRS)

Halyo, Nesim; Arduini, Robert F.; Samms, Richard W.

1987-01-01

The relationship between the image gathering and image processing systems for minimum mean squared error estimation of scene characteristics is investigated. A stochastic optimization problem is formulated where the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled and noisy image data. An analytical solution for the optimal characteristic image processor is developed. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristic is scene restoration. Optimal edge detection is investigated using the Laplacian operator x G as the desired characteristic, where G is a two dimensional Gaussian distribution function. It is shown that the optimal edge detector compensates for the blurring introduced by the image gathering optics, and notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition. The optimal image gathering optical transfer function is also investigated and the results of a sensitivity analysis are shown.

Regulating effect of epithalone on gastric endocrine cells in pinealectomized rats.

PubMed

Khavinson, V K; Popuchiev, V V; Kvetnoii, I M; Yuzhakov, V V; Kotlova, L N

2000-12-01

Endocrine cells in the stomach of pinealectomized rats after injection of epithalone (pineal gland peptide) were studied by immunohistochemical tests, morphometry, and image analysis microscopic images. A functional relationship was found between the pineal gland and stomach, which is regulated by peptides produced by the pineal gland.

Inter-vender and test-retest reliabilities of resting-state functional magnetic resonance imaging: Implications for multi-center imaging studies.

PubMed

An, Hyeong Su; Moon, Won-Jin; Ryu, Jae-Kyun; Park, Ju Yeon; Yun, Won Sung; Choi, Jin Woo; Jahng, Geon-Ho; Park, Jang-Yeon

2017-12-01

This prospective multi-center study aimed to evaluate the inter-vendor and test-retest reliabilities of resting-state functional magnetic resonance imaging (RS-fMRI) by assessing the temporal signal-to-noise ratio (tSNR) and functional connectivity. Study included 10 healthy subjects and each subject was scanned using three 3T MR scanners (GE Signa HDxt, Siemens Skyra, and Philips Achieva) in two sessions. The tSNR was calculated from the time course data. Inter-vendor and test-retest reliabilities were assessed with intra-class correlation coefficients (ICCs) derived from variant component analysis. Independent component analysis was performed to identify the connectivity of the default-mode network (DMN). In result, the tSNR for the DMN was not significantly different among the GE, Philips, and Siemens scanners (P=0.638). In terms of vendor differences, the inter-vendor reliability was good (ICC=0.774). Regarding the test-retest reliability, the GE scanner showed excellent correlation (ICC=0.961), while the Philips (ICC=0.671) and Siemens (ICC=0.726) scanners showed relatively good correlation. The DMN pattern of the subjects between the two sessions for each scanner and between three scanners showed the identical patterns of functional connectivity. The inter-vendor and test-retest reliabilities of RS-fMRI using different 3T MR scanners are good. Thus, we suggest that RS-fMRI could be used in multicenter imaging studies as a reliable imaging marker. Copyright © 2017 Elsevier Inc. All rights reserved.

A Hybrid Soft-computing Method for Image Analysis of Digital Plantar Scanners.

PubMed

Razjouyan, Javad; Khayat, Omid; Siahi, Mehdi; Mansouri, Ali Alizadeh

2013-01-01

Digital foot scanners have been developed in recent years to yield anthropometrists digital image of insole with pressure distribution and anthropometric information. In this paper, a hybrid algorithm containing gray level spatial correlation (GLSC) histogram and Shanbag entropy is presented for analysis of scanned foot images. An evolutionary algorithm is also employed to find the optimum parameters of GLSC and transform function of the membership values. Resulting binary images as the thresholded images are undergone anthropometric measurements taking in to account the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper. Running computation time and the effects of GLSC parameters are investigated in the simulation results.

Analyzing Protein Clusters on the Plasma Membrane: Application of Spatial Statistical Analysis Methods on Super-Resolution Microscopy Images.

PubMed

Paparelli, Laura; Corthout, Nikky; Pavie, Benjamin; Annaert, Wim; Munck, Sebastian

2016-01-01

The spatial distribution of proteins within the cell affects their capability to interact with other molecules and directly influences cellular processes and signaling. At the plasma membrane, multiple factors drive protein compartmentalization into specialized functional domains, leading to the formation of clusters in which intermolecule interactions are facilitated. Therefore, quantifying protein distributions is a necessity for understanding their regulation and function. The recent advent of super-resolution microscopy has opened up the possibility of imaging protein distributions at the nanometer scale. In parallel, new spatial analysis methods have been developed to quantify distribution patterns in super-resolution images. In this chapter, we provide an overview of super-resolution microscopy and summarize the factors influencing protein arrangements on the plasma membrane. Finally, we highlight methods for analyzing clusterization of plasma membrane proteins, including examples of their applications.

Radar Imaging Using The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Kenny, Owen P.; Whitehouse, Harper J.

1989-12-01

The need for analysis of time-varying signals has led to the formulation of a class of joint time-frequency distributions (TFDs). One of these TFDs, the Wigner-Ville distribution (WVD), has useful properties which can be applied to radar imaging. This paper first discusses the radar equation in terms of the time-frequency representation of the signal received from a radar system. It then presents a method of tomographic reconstruction for time-frequency images to estimate the scattering function of the aircraft. An optical archi-tecture is then discussed for the real-time implementation of the analysis method based on the WVD.

Analysis of spatial pseudodepolarizers in imaging systems

NASA Technical Reports Server (NTRS)

Mcguire, James P., Jr.; Chipman, Russell A.

1990-01-01

The objective of a number of optical instruments is to measure the intensity accurately without bias as to the incident polarization state. One method to overcome polarization bias in optical systems is the insertion of a spatial pseudodepolarizer. Both the degree of depolarization and image degradation (from the polarization aberrations of the pseudodepolarizer) are analyzed for two depolarizer designs: (1) the Cornu pseudodepolarizer, effective for linearly polarized light, and (2) the dual Babinet compensator pseudodepolarizer, effective for all incident polarization states. The image analysis uses a matrix formalism to describe the polarization dependence of the diffraction patterns and optical transfer function.

An approach for quantitative image quality analysis for CT

NASA Astrophysics Data System (ADS)

Rahimi, Amir; Cochran, Joe; Mooney, Doug; Regensburger, Joe

2016-03-01

An objective and standardized approach to assess image quality of Compute Tomography (CT) systems is required in a wide variety of imaging processes to identify CT systems appropriate for a given application. We present an overview of the framework we have developed to help standardize and to objectively assess CT image quality for different models of CT scanners used for security applications. Within this framework, we have developed methods to quantitatively measure metrics that should correlate with feature identification, detection accuracy and precision, and image registration capabilities of CT machines and to identify strengths and weaknesses in different CT imaging technologies in transportation security. To that end we have designed, developed and constructed phantoms that allow for systematic and repeatable measurements of roughly 88 image quality metrics, representing modulation transfer function, noise equivalent quanta, noise power spectra, slice sensitivity profiles, streak artifacts, CT number uniformity, CT number consistency, object length accuracy, CT number path length consistency, and object registration. Furthermore, we have developed a sophisticated MATLAB based image analysis tool kit to analyze CT generated images of phantoms and report these metrics in a format that is standardized across the considered models of CT scanners, allowing for comparative image quality analysis within a CT model or between different CT models. In addition, we have developed a modified sparse principal component analysis (SPCA) method to generate a modified set of PCA components as compared to the standard principal component analysis (PCA) with sparse loadings in conjunction with Hotelling T2 statistical analysis method to compare, qualify, and detect faults in the tested systems.

Ex-vivo imaging of excised tissue using vital dyes and confocal microscopy

PubMed Central

Johnson, Simon; Rabinovitch, Peter

2012-01-01

Vital dyes routinely used for staining cultured cells can also be used to stain and image live tissue slices ex-vivo. Staining tissue with vital dyes allows researchers to collect structural and functional data simultaneously and can be used for qualitative or quantitative fluorescent image collection. The protocols presented here are useful for structural and functional analysis of viable properties of cells in intact tissue slices, allowing for the collection of data in a structurally relevant environment. With these protocols, vital dyes can be applied as a research tool to disease processes and properties of tissue not amenable to cell culture based studies. PMID:22752953

3D Time-lapse Imaging and Quantification of Mitochondrial Dynamics

NASA Astrophysics Data System (ADS)

Sison, Miguel; Chakrabortty, Sabyasachi; Extermann, Jérôme; Nahas, Amir; James Marchand, Paul; Lopez, Antonio; Weil, Tanja; Lasser, Theo

2017-02-01

We present a 3D time-lapse imaging method for monitoring mitochondrial dynamics in living HeLa cells based on photothermal optical coherence microscopy and using novel surface functionalization of gold nanoparticles. The biocompatible protein-based biopolymer coating contains multiple functional groups which impart better cellular uptake and mitochondria targeting efficiency. The high stability of the gold nanoparticles allows continuous imaging over an extended time up to 3000 seconds without significant cell damage. By combining temporal autocorrelation analysis with a classical diffusion model, we quantify mitochondrial dynamics and cast these results into 3D maps showing the heterogeneity of diffusion parameters across the whole cell volume.

HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis.

PubMed

Wang, Liya; Uilecan, Ioan Vlad; Assadi, Amir H; Kozmik, Christine A; Spalding, Edgar P

2009-04-01

Analysis of time series of images can quantify plant growth and development, including the effects of genetic mutations (phenotypes) that give information about gene function. Here is demonstrated a software application named HYPOTrace that automatically extracts growth and shape information from electronic gray-scale images of Arabidopsis (Arabidopsis thaliana) seedlings. Key to the method is the iterative application of adaptive local principal components analysis to extract a set of ordered midline points (medial axis) from images of the seedling hypocotyl. Pixel intensity is weighted to avoid the medial axis being diverted by the cotyledons in areas where the two come in contact. An intensity feature useful for terminating the midline at the hypocotyl apex was isolated in each image by subtracting the baseline with a robust local regression algorithm. Applying the algorithm to time series of images of Arabidopsis seedlings responding to light resulted in automatic quantification of hypocotyl growth rate, apical hook opening, and phototropic bending with high spatiotemporal resolution. These functions are demonstrated here on wild-type, cryptochrome1, and phototropin1 seedlings for the purpose of showing that HYPOTrace generated expected results and to show how much richer the machine-vision description is compared to methods more typical in plant biology. HYPOTrace is expected to benefit seedling development research, particularly in the photomorphogenesis field, by replacing many tedious, error-prone manual measurements with a precise, largely automated computational tool.

Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture.

PubMed

Russell, Richard A; Adams, Niall M; Stephens, David A; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S

2009-04-22

Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments.

Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

PubMed Central

Russell, Richard A.; Adams, Niall M.; Stephens, David A.; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S.

2009-01-01

Abstract Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments. PMID:19383481

[Functional magnetic resonance imaging in psychiatry and psychotherapy].

PubMed

Derntl, B; Habel, U; Schneider, F

2010-01-01

technical improvements, functional magnetic resonance imaging (fMRI) has become the most popular and versatile imaging method in psychiatric research. The scope of this manuscript is to briefly introduce the basics of MR physics, the blood oxygenation level-dependent (BOLD) contrast as well as the principles of MR study design and functional data analysis. The presentation of exemplary studies on emotion recognition and empathy in schizophrenia patients will highlight the importance of MR methods in psychiatry. Finally, we will demonstrate insights into new developments that will further boost MR techniques in clinical research and will help to gain more insight into dysfunctional neural networks underlying cognitive and emotional deficits in psychiatric patients. Moreover, some techniques such as neurofeedback seem promising for evaluation of therapy effects on a behavioral and neural level.

Design and performance evaluation of the imaging payload for a remote sensing satellite

NASA Astrophysics Data System (ADS)

Abolghasemi, Mojtaba; Abbasi-Moghadam, Dariush

2012-11-01

In this paper an analysis method and corresponding analytical tools for design of the experimental imaging payload (IMPL) of a remote sensing satellite (SINA-1) are presented. We begin with top-level customer system performance requirements and constraints and derive the critical system and component parameters, then analyze imaging payload performance until a preliminary design that meets customer requirements. We consider system parameters and components composing the image chain for imaging payload system which includes aperture, focal length, field of view, image plane dimensions, pixel dimensions, detection quantum efficiency, and optical filter requirements. The performance analysis is accomplished by calculating the imaging payload's SNR (signal-to-noise ratio), and imaging resolution. The noise components include photon noise due to signal scene and atmospheric background, cold shield, out-of-band optical filter leakage and electronic noise. System resolution is simulated through cascaded modulation transfer functions (MTFs) and includes effects due to optics, image sampling, and system motion. Calculations results for the SINA-1 satellite are also presented.

Quantification of right ventricular volumes and function by real time three-dimensional echocardiographic longitudinal axial plane method: validation in the clinical setting.

PubMed

Endo, Yuka; Maddukuri, Prasad V; Vieira, Marcelo L C; Pandian, Natesa G; Patel, Ayan R

2006-11-01

Measurement of right ventricular (RV) volumes and right ventricular ejection fraction (RVEF) by three-dimensional echocardiographic (3DE) short-axis disc summation method has been validated in multiple studies. However, in some patients, short-axis images are of insufficient quality for accurate tracing of the RV endocardial border. This study examined the accuracy of long-axis analysis in multiple planes (longitudinal axial plane method) for assessment of RV volumes and RVEF. 3DE images were analyzed in 40 subjects with a broad range of RV function. RV end-diastolic (RVEDV) and end-systolic volumes (RVESV) and RVEF were calculated by both short-axis disc summation method and longitudinal axial plane method. Excellent correlation was obtained between the two methods for RVEDV, RVESV, and RVEF (r = 0.99, 0.99, 0.94, respectively; P < 0.0001 for all comparisons). 3DE longitudinal-axis analysis is a promising technique for the evaluation of RV function, and may provide an alternative method of assessment in patients with suboptimal short-axis images.

Processing of food, body and emotional stimuli in anorexia nervosa: a systematic review and meta-analysis of functional magnetic resonance imaging studies.

PubMed

Zhu, Yikang; Hu, Xiaochen; Wang, Jijun; Chen, Jue; Guo, Qian; Li, Chunbo; Enck, Paul

2012-11-01

The characteristics of the cognitive processing of food, body and emotional information in patients with anorexia nervosa (AN) are debatable. We reviewed functional magnetic resonance imaging studies to assess whether there were consistent neural basis and networks in the studies to date. Searching PubMed, Ovid, Web of Science, The Cochrane Library and Google Scholar between January 1980 and May 2012, we identified 17 relevant studies. Activation likelihood estimation was used to perform a quantitative meta-analysis of functional magnetic resonance imaging studies. For both food stimuli and body stimuli, AN patients showed increased hemodynamic response in the emotion-related regions (frontal, caudate, uncus, insula and temporal) and decreased activation in the parietal region. Although no robust brain activation has been found in response to emotional stimuli, emotion-related neural networks are involved in the processing of food and body stimuli among AN. It suggests that negative emotional arousal is related to cognitive processing bias of food and body stimuli in AN. Copyright © 2012 John Wiley & Sons, Ltd and Eating Disorders Association.

Optical design and testing: introduction.

PubMed

Liang, Chao-Wen; Koshel, John; Sasian, Jose; Breault, Robert; Wang, Yongtian; Fang, Yi Chin

2014-10-10

Optical design and testing has numerous applications in industrial, military, consumer, and medical settings. Assembling a complete imaging or nonimage optical system may require the integration of optics, mechatronics, lighting technology, optimization, ray tracing, aberration analysis, image processing, tolerance compensation, and display rendering. This issue features original research ranging from the optical design of image and nonimage optical stimuli for human perception, optics applications, bio-optics applications, 3D display, solar energy system, opto-mechatronics to novel imaging or nonimage modalities in visible and infrared spectral imaging, modulation transfer function measurement, and innovative interferometry.

Neurobiological changes of schizotypy: evidence from both volume-based morphometric analysis and resting-state functional connectivity.

PubMed

Wang, Yi; Yan, Chao; Yin, Da-zhi; Fan, Ming-xia; Cheung, Eric F C; Pantelis, Christos; Chan, Raymond C K

2015-03-01

The current study sought to examine the underlying brain changes in individuals with high schizotypy by integrating networks derived from brain structural and functional imaging. Individuals with high schizotypy (n = 35) and low schizotypy (n = 34) controls were screened using the Schizotypal Personality Questionnaire and underwent brain structural and resting-state functional magnetic resonance imaging on a 3T scanner. Voxel-based morphometric analysis and graph theory-based functional network analysis were conducted. Individuals with high schizotypy showed reduced gray matter (GM) density in the insula and the dorsolateral prefrontal gyrus. The graph theoretical analysis showed that individuals with high schizotypy showed similar global properties in their functional networks as low schizotypy individuals. Several hubs of the functional network were identified in both groups, including the insula, the lingual gyrus, the postcentral gyrus, and the rolandic operculum. More hubs in the frontal lobe and fewer hubs in the occipital lobe were identified in individuals with high schizotypy. By comparing the functional connectivity between clusters with abnormal GM density and the whole brain, individuals with high schizotypy showed weaker functional connectivity between the left insula and the putamen, but stronger connectivity between the cerebellum and the medial frontal gyrus. Taken together, our findings suggest that individuals with high schizotypy present changes in terms of GM and resting-state functional connectivity, especially in the frontal lobe. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Graph Analysis of Functional Brain Networks for Cognitive Control of Action in Traumatic Brain Injury

ERIC Educational Resources Information Center

Caeyenberghs, Karen; Leemans, Alexander; Heitger, Marcus H.; Leunissen, Inge; Dhollander, Thijs; Sunaert, Stefan; Dupont, Patrick; Swinnen, Stephan P.

2012-01-01

Patients with traumatic brain injury show clear impairments in behavioural flexibility and inhibition that often persist beyond the time of injury, affecting independent living and psychosocial functioning. Functional magnetic resonance imaging studies have shown that patients with traumatic brain injury typically show increased and more broadly…

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

PubMed Central

Uğurbil, Kamil; Xu, Junqian; Auerbach, Edward J.; Moeller, Steen; Vu, An; Duarte-Carvajalino, Julio M.; Lenglet, Christophe; Wu, Xiaoping; Schmitter, Sebastian; Van de Moortele, Pierre Francois; Strupp, John; Sapiro, Guillermo; De Martino, Federico; Wang, Dingxin; Harel, Noam; Garwood, Michael; Chen, Liyong; Feinberg, David A.; Smith, Stephen M.; Miller, Karla L.; Sotiropoulos, Stamatios N; Jbabdi, Saad; Andersson, Jesper L; Behrens, Timothy EJ; Glasser, Matthew F.; Van Essen, David; Yacoub, Essa

2013-01-01

The human connectome project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3 Tesla, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 second for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 Tesla magnetic field are also presented, targeting higher resolution, specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields and power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure. PMID:23702417

A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.

PubMed

Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong

2015-12-01

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.

Comparison of dose response functions for EBT3 model GafChromic™ film dosimetry system.

PubMed

Aldelaijan, Saad; Devic, Slobodan

2018-05-01

Different dose response functions of EBT3 model GafChromic™ film dosimetry system have been compared in terms of sensitivity as well as uncertainty vs. error analysis. We also made an assessment of the necessity of scanning film pieces before and after irradiation. Pieces of EBT3 film model were irradiated to different dose values in Solid Water (SW) phantom. Based on images scanned in both reflection and transmission mode before and after irradiation, twelve different response functions were calculated. For every response function, a reference radiochromic film dosimetry system was established by generating calibration curve and by performing the error vs. uncertainty analysis. Response functions using pixel values from the green channel demonstrated the highest sensitivity in both transmission and reflection mode. All functions were successfully fitted with rational functional form, and provided an overall one-sigma uncertainty of better than 2% for doses above 2 Gy. Use of pre-scanned images to calculate response functions resulted in negligible improvement in dose measurement accuracy. Although reflection scanning mode provides higher sensitivity and could lead to a more widespread use of radiochromic film dosimetry, it has fairly limited dose range and slightly increased uncertainty when compared to transmission scan based response functions. Double-scanning technique, either in transmission or reflection mode, shows negligible improvement in dose accuracy as well as a negligible increase in dose uncertainty. Normalized pixel value of the images scanned in transmission mode shows linear response in a dose range of up to 11 Gy. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Automatic registration of ICG images using mutual information and perfusion analysis

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Jong-Mo; Lee, June-goo; Kim, Jong Hyo; Park, Kwangsuk; Yu, Hyeong-Gon; Yu, Young Suk; Chung, Hum

2005-04-01

Introduction: Indocyanin green fundus angiographic images (ICGA) of the eyes is useful method in detecting and characterizing the choroidal neovascularization (CNV), which is the major cause of the blindness over 65 years of age. To investigate the quantitative analysis of the blood flow on ICGA, systematic approach for automatic registration of using mutual information and a quantitative analysis was developed. Methods: Intermittent sequential images of indocyanin green angiography were acquired by Heidelberg retinal angiography that uses the laser scanning system for the image acquisition. Misalignment of the each image generated by the minute eye movement of the patients was corrected by the mutual information method because the distribution of the contrast media on image is changing throughout the time sequences. Several region of interest (ROI) were selected by a physician and the intensities of the selected region were plotted according to the time sequences. Results: The registration of ICGA time sequential images is required not only translate transform but also rotational transform. Signal intensities showed variation based on gamma-variate function depending on ROIs and capillary vessels show more variance of signal intensity than major vessels. CNV showed intermediate variance of signal intensity and prolonged transit time. Conclusion: The resulting registered images can be used not only for quantitative analysis, but also for perfusion analysis. Various investigative approached on CNV using this method will be helpful in the characterization of the lesion and follow-up.

Spectral Analysis and Experimental Modeling of Ice Accretion Roughness

NASA Technical Reports Server (NTRS)

Orr, D. J.; Breuer, K. S.; Torres, B. E.; Hansman, R. J., Jr.

1996-01-01

A self-consistent scheme for relating wind tunnel ice accretion roughness to the resulting enhancement of heat transfer is described. First, a spectral technique of quantitative analysis of early ice roughness images is reviewed. The image processing scheme uses a spectral estimation technique (SET) which extracts physically descriptive parameters by comparing scan lines from the experimentally-obtained accretion images to a prescribed test function. Analysis using this technique for both streamwise and spanwise directions of data from the NASA Lewis Icing Research Tunnel (IRT) are presented. An experimental technique is then presented for constructing physical roughness models suitable for wind tunnel testing that match the SET parameters extracted from the IRT images. The icing castings and modeled roughness are tested for enhancement of boundary layer heat transfer using infrared techniques in a "dry" wind tunnel.

Optimal image alignment with random projections of manifolds: algorithm and geometric analysis.

PubMed

Kokiopoulou, Effrosyni; Kressner, Daniel; Frossard, Pascal

2011-06-01

This paper addresses the problem of image alignment based on random measurements. Image alignment consists of estimating the relative transformation between a query image and a reference image. We consider the specific problem where the query image is provided in compressed form in terms of linear measurements captured by a vision sensor. We cast the alignment problem as a manifold distance minimization problem in the linear subspace defined by the measurements. The transformation manifold that represents synthesis of shift, rotation, and isotropic scaling of the reference image can be given in closed form when the reference pattern is sparsely represented over a parametric dictionary. We show that the objective function can then be decomposed as the difference of two convex functions (DC) in the particular case where the dictionary is built on Gaussian functions. Thus, the optimization problem becomes a DC program, which in turn can be solved globally by a cutting plane method. The quality of the solution is typically affected by the number of random measurements and the condition number of the manifold that describes the transformations of the reference image. We show that the curvature, which is closely related to the condition number, remains bounded in our image alignment problem, which means that the relative transformation between two images can be determined optimally in a reduced subspace.

Analytic programming with FMRI data: a quick-start guide for statisticians using R.

PubMed

Eloyan, Ani; Li, Shanshan; Muschelli, John; Pekar, Jim J; Mostofsky, Stewart H; Caffo, Brian S

2014-01-01

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

Digital Image Display Control System, DIDCS. [for astronomical analysis

NASA Technical Reports Server (NTRS)

Fischel, D.; Klinglesmith, D. A., III

1979-01-01

DIDCS is an interactive image display and manipulation system that is used for a variety of astronomical image reduction and analysis operations. The hardware system consists of a PDP 11/40 main frame with 32K of 16-bit core memory; 96K of 16-bit MOS memory; two 9 track 800 BPI tape drives; eight 2.5 million byte RKO5 type disk packs, three user terminals, and a COMTAL 8000-S display system which has sufficient memory to store and display three 512 x 512 x 8 bit images along with an overlay plane and function table for each image, a pseudo color table and the capability for displaying true color. The software system is based around the language FORTH, which will permit an open ended dictionary of user level words for image analyses and display. A description of the hardware and software systems will be presented along with examples of the types of astronomical research that are being performed. Also a short discussion of the commonality and exchange of this type of image analysis system will be given.

Creation of a virtual cutaneous tissue bank

NASA Astrophysics Data System (ADS)

LaFramboise, William A.; Shah, Sujal; Hoy, R. W.; Letbetter, D.; Petrosko, P.; Vennare, R.; Johnson, Peter C.

2000-04-01

Cellular and non-cellular constituents of skin contain fundamental morphometric features and structural patterns that correlate with tissue function. High resolution digital image acquisitions performed using an automated system and proprietary software to assemble adjacent images and create a contiguous, lossless, digital representation of individual microscope slide specimens. Serial extraction, evaluation and statistical analysis of cutaneous feature is performed utilizing an automated analysis system, to derive normal cutaneous parameters comprising essential structural skin components. Automated digital cutaneous analysis allows for fast extraction of microanatomic dat with accuracy approximating manual measurement. The process provides rapid assessment of feature both within individual specimens and across sample populations. The images, component data, and statistical analysis comprise a bioinformatics database to serve as an architectural blueprint for skin tissue engineering and as a diagnostic standard of comparison for pathologic specimens.

NeuronRead, an open source semi-automated tool for morphometric analysis of phase contrast and fluorescence neuronal images.

PubMed

Dias, Roberto A; Gonçalves, Bruno P; da Rocha, Joana F; da Cruz E Silva, Odete A B; da Silva, Augusto M F; Vieira, Sandra I

2017-12-01

Neurons are specialized cells of the Central Nervous System whose function is intricately related to the neuritic network they develop to transmit information. Morphological evaluation of this network and other neuronal structures is required to establish relationships between neuronal morphology and function, and may allow monitoring physiological and pathophysiologic alterations. Fluorescence-based microphotographs are the most widely used in cellular bioimaging, but phase contrast (PhC) microphotographs are easier to obtain, more affordable, and do not require invasive, complicated and disruptive techniques. Despite the various freeware tools available for fluorescence-based images analysis, few exist that can tackle the more elusive and harder-to-analyze PhC images. To surpass this, an interactive semi-automated image processing workflow was developed to easily extract relevant information (e.g. total neuritic length, average cell body area) from both PhC and fluorescence neuronal images. This workflow, named 'NeuronRead', was developed in the form of an ImageJ macro. Its robustness and adaptability were tested and validated on rat cortical primary neurons under control and differentiation inhibitory conditions. Validation included a comparison to manual determinations and to a golden standard freeware tool for fluorescence image analysis. NeuronRead was subsequently applied to PhC images of neurons at distinct differentiation days and exposed or not to DAPT, a pharmacological inhibitor of the γ-secretase enzyme, which cleaves the well-known Alzheimer's amyloid precursor protein (APP) and the Notch receptor. Data obtained confirms a neuritogenic regulatory role for γ-secretase products and validates NeuronRead as a time- and cost-effective useful monitoring tool. Copyright © 2017. Published by Elsevier Inc.

Resting-state synchrony between the retrosplenial cortex and anterior medial cortical structures relates to memory complaints in subjective cognitive impairment.

PubMed

Yasuno, Fumihiko; Kazui, Hiroaki; Yamamoto, Akihide; Morita, Naomi; Kajimoto, Katsufumi; Ihara, Masafumi; Taguchi, Akihiko; Matsuoka, Kiwamu; Kosaka, Jun; Tanaka, Toshihisa; Kudo, Takashi; Takeda, Masatoshi; Nagatsuka, Kazuyuki; Iida, Hidehiro; Kishimoto, Toshifumi

2015-06-01

Subjective cognitive impairment (SCI) is a clinical state characterized by subjective cognitive deficits without cognitive impairment. To test the hypothesis that this state might involve dysfunction of self-referential processing mediated by cortical midline structures, we investigated abnormalities of functional connectivity in these structures in individuals with SCI using resting-state functional magnetic resonance imaging. We performed functional connectivity analysis for 23 individuals with SCI and 30 individuals without SCI. To reveal the pathophysiological basis of the functional connectivity change, we performed magnetic resonance-diffusion tensor imaging. Positron emission tomography-amyloid imaging was conducted in 13 SCI and 15 nonSCI subjects. Individuals with SCI showed reduced functional connectivity in cortical midline structures. Reduction in white matter connections was related to reduced functional connectivity, but we found no amyloid deposition in individuals with SCI. The results do not necessarily contradict the possibility that SCI indicates initial cognitive decrements, but imply that reduced functional connectivity in cortical midline structures contributes to overestimation of the experience of forgetfulness. Copyright © 2015 Elsevier Inc. All rights reserved.

PIRATE: pediatric imaging response assessment and targeting environment

NASA Astrophysics Data System (ADS)

Glenn, Russell; Zhang, Yong; Krasin, Matthew; Hua, Chiaho

2010-02-01

By combining the strengths of various imaging modalities, the multimodality imaging approach has potential to improve tumor staging, delineation of tumor boundaries, chemo-radiotherapy regime design, and treatment response assessment in cancer management. To address the urgent needs for efficient tools to analyze large-scale clinical trial data, we have developed an integrated multimodality, functional and anatomical imaging analysis software package for target definition and therapy response assessment in pediatric radiotherapy (RT) patients. Our software provides quantitative tools for automated image segmentation, region-of-interest (ROI) histogram analysis, spatial volume-of-interest (VOI) analysis, and voxel-wise correlation across modalities. To demonstrate the clinical applicability of this software, histogram analyses were performed on baseline and follow-up 18F-fluorodeoxyglucose (18F-FDG) PET images of nine patients with rhabdomyosarcoma enrolled in an institutional clinical trial at St. Jude Children's Research Hospital. In addition, we combined 18F-FDG PET, dynamic-contrast-enhanced (DCE) MR, and anatomical MR data to visualize the heterogeneity in tumor pathophysiology with the ultimate goal of adaptive targeting of regions with high tumor burden. Our software is able to simultaneously analyze multimodality images across multiple time points, which could greatly speed up the analysis of large-scale clinical trial data and validation of potential imaging biomarkers.

Speckle tracking analysis: a new tool for left atrial function analysis in systemic hypertension: an overview.

PubMed

Cameli, Matteo; Ciccone, Marco M; Maiello, Maria; Modesti, Pietro A; Muiesan, Maria L; Scicchitano, Pietro; Novo, Salvatore; Palmiero, Pasquale; Saba, Pier S; Pedrinelli, Roberto

2016-05-01

Speckle tracking echocardiography (STE) is an imaging technique applied to the analysis of left atrial function. STE provides a non-Doppler, angle-independent and objective quantification of left atrial myocardial deformation. Data regarding feasibility, accuracy and clinical applications of left atrial strain are rapidly gathering. This review describes the fundamental concepts of left atrial STE, illustrates its pathophysiological background and discusses its emerging role in systemic arterial hypertension.

Quantum computation and analysis of Wigner and Husimi functions: toward a quantum image treatment.

PubMed

Terraneo, M; Georgeot, B; Shepelyansky, D L

2005-06-01

We study the efficiency of quantum algorithms which aim at obtaining phase-space distribution functions of quantum systems. Wigner and Husimi functions are considered. Different quantum algorithms are envisioned to build these functions, and compared with the classical computation. Different procedures to extract more efficiently information from the final wave function of these algorithms are studied, including coarse-grained measurements, amplitude amplification, and measure of wavelet-transformed wave function. The algorithms are analyzed and numerically tested on a complex quantum system showing different behavior depending on parameters: namely, the kicked rotator. The results for the Wigner function show in particular that the use of the quantum wavelet transform gives a polynomial gain over classical computation. For the Husimi distribution, the gain is much larger than for the Wigner function and is larger with the help of amplitude amplification and wavelet transforms. We discuss the generalization of these results to the simulation of other quantum systems. We also apply the same set of techniques to the analysis of real images. The results show that the use of the quantum wavelet transform allows one to lower dramatically the number of measurements needed, but at the cost of a large loss of information.

Steerable Principal Components for Space-Frequency Localized Images*

PubMed Central

Landa, Boris; Shkolnisky, Yoel

2017-01-01

As modern scientific image datasets typically consist of a large number of images of high resolution, devising methods for their accurate and efficient processing is a central research task. In this paper, we consider the problem of obtaining the steerable principal components of a dataset, a procedure termed “steerable PCA” (steerable principal component analysis). The output of the procedure is the set of orthonormal basis functions which best approximate the images in the dataset and all of their planar rotations. To derive such basis functions, we first expand the images in an appropriate basis, for which the steerable PCA reduces to the eigen-decomposition of a block-diagonal matrix. If we assume that the images are well localized in space and frequency, then such an appropriate basis is the prolate spheroidal wave functions (PSWFs). We derive a fast method for computing the PSWFs expansion coefficients from the images' equally spaced samples, via a specialized quadrature integration scheme, and show that the number of required quadrature nodes is similar to the number of pixels in each image. We then establish that our PSWF-based steerable PCA is both faster and more accurate then existing methods, and more importantly, provides us with rigorous error bounds on the entire procedure. PMID:29081879

Fast periodic stimulation (FPS): a highly effective approach in fMRI brain mapping.

PubMed

Gao, Xiaoqing; Gentile, Francesco; Rossion, Bruno

2018-06-01

Defining the neural basis of perceptual categorization in a rapidly changing natural environment with low-temporal resolution methods such as functional magnetic resonance imaging (fMRI) is challenging. Here, we present a novel fast periodic stimulation (FPS)-fMRI approach to define face-selective brain regions with natural images. Human observers are presented with a dynamic stream of widely variable natural object images alternating at a fast rate (6 images/s). Every 9 s, a short burst of variable face images contrasting with object images in pairs induces an objective face-selective neural response at 0.111 Hz. A model-free Fourier analysis achieves a twofold increase in signal-to-noise ratio compared to a conventional block-design approach with identical stimuli and scanning duration, allowing to derive a comprehensive map of face-selective areas in the ventral occipito-temporal cortex, including the anterior temporal lobe (ATL), in all individual brains. Critically, periodicity of the desired category contrast and random variability among widely diverse images effectively eliminates the contribution of low-level visual cues, and lead to the highest values (80-90%) of test-retest reliability in the spatial activation map yet reported in imaging higher level visual functions. FPS-fMRI opens a new avenue for understanding brain function with low-temporal resolution methods.

Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice.

PubMed

Wright, Patrick W; Brier, Lindsey M; Bauer, Adam Q; Baxter, Grant A; Kraft, Andrew W; Reisman, Matthew D; Bice, Annie R; Snyder, Abraham Z; Lee, Jin-Moo; Culver, Joseph P

2017-01-01

The interplay between hemodynamic-based markers of cortical activity (e.g. fMRI and optical intrinsic signal imaging), which are an indirect and relatively slow report of neural activity, and underlying synaptic electrical and metabolic activity through neurovascular coupling is a topic of ongoing research and debate. As application of resting state functional connectivity measures is extended further into topics such as brain development, aging and disease, the importance of understanding the fundamental physiological basis for functional connectivity will grow. Here we extend functional connectivity analysis from hemodynamic- to calcium-based imaging. Transgenic mice (n = 7) expressing a fluorescent calcium indicator (GCaMP6) driven by the Thy1 promoter in glutamatergic neurons were imaged transcranially in both anesthetized (using ketamine/xylazine) and awake states. Sequential LED illumination (λ = 454, 523, 595, 640nm) enabled concurrent imaging of both GCaMP6 fluorescence emission (corrected for hemoglobin absorption) and hemodynamics. Functional connectivity network maps were constructed for infraslow (0.009-0.08Hz), intermediate (0.08-0.4Hz), and high (0.4-4.0Hz) frequency bands. At infraslow and intermediate frequencies, commonly used in BOLD fMRI and fcOIS studies of functional connectivity and implicated in neurovascular coupling mechanisms, GCaMP6 and HbO2 functional connectivity structures were in high agreement, both qualitatively and also quantitatively through a measure of spatial similarity. The spontaneous dynamics of both contrasts had the highest correlation when the GCaMP6 signal was delayed with a ~0.6-1.5s temporal offset. Within the higher-frequency delta band, sensitive to slow wave sleep oscillations in non-REM sleep and anesthesia, we evaluate the speed with which the connectivity analysis stabilized and found that the functional connectivity maps captured putative network structure within time window lengths as short as 30 seconds. Homotopic GCaMP6 functional connectivity maps at 0.4-4.0Hz in the anesthetized states show a striking correlated and anti-correlated structure along the anterior to posterior axis. This structure is potentially explained in part by observed propagation of delta-band activity from frontal somatomotor regions to visuoparietal areas. During awake imaging, this spatio-temporal quality is altered, and a more complex and detailed functional connectivity structure is observed. The combined calcium/hemoglobin imaging technique described here will enable the dissociation of changes in ionic and hemodynamic functional structure and neurovascular coupling and provide a framework for subsequent studies of neurological disease such as stroke.

Functional connectivity structure of cortical calcium dynamics in anesthetized and awake mice

PubMed Central

Wright, Patrick W.; Brier, Lindsey M.; Bauer, Adam Q.; Baxter, Grant A.; Kraft, Andrew W.; Reisman, Matthew D.; Bice, Annie R.; Snyder, Abraham Z.; Lee, Jin-Moo; Culver, Joseph P.

2017-01-01

The interplay between hemodynamic-based markers of cortical activity (e.g. fMRI and optical intrinsic signal imaging), which are an indirect and relatively slow report of neural activity, and underlying synaptic electrical and metabolic activity through neurovascular coupling is a topic of ongoing research and debate. As application of resting state functional connectivity measures is extended further into topics such as brain development, aging and disease, the importance of understanding the fundamental physiological basis for functional connectivity will grow. Here we extend functional connectivity analysis from hemodynamic- to calcium-based imaging. Transgenic mice (n = 7) expressing a fluorescent calcium indicator (GCaMP6) driven by the Thy1 promoter in glutamatergic neurons were imaged transcranially in both anesthetized (using ketamine/xylazine) and awake states. Sequential LED illumination (λ = 454, 523, 595, 640nm) enabled concurrent imaging of both GCaMP6 fluorescence emission (corrected for hemoglobin absorption) and hemodynamics. Functional connectivity network maps were constructed for infraslow (0.009–0.08Hz), intermediate (0.08–0.4Hz), and high (0.4–4.0Hz) frequency bands. At infraslow and intermediate frequencies, commonly used in BOLD fMRI and fcOIS studies of functional connectivity and implicated in neurovascular coupling mechanisms, GCaMP6 and HbO2 functional connectivity structures were in high agreement, both qualitatively and also quantitatively through a measure of spatial similarity. The spontaneous dynamics of both contrasts had the highest correlation when the GCaMP6 signal was delayed with a ~0.6–1.5s temporal offset. Within the higher-frequency delta band, sensitive to slow wave sleep oscillations in non-REM sleep and anesthesia, we evaluate the speed with which the connectivity analysis stabilized and found that the functional connectivity maps captured putative network structure within time window lengths as short as 30 seconds. Homotopic GCaMP6 functional connectivity maps at 0.4–4.0Hz in the anesthetized states show a striking correlated and anti-correlated structure along the anterior to posterior axis. This structure is potentially explained in part by observed propagation of delta-band activity from frontal somatomotor regions to visuoparietal areas. During awake imaging, this spatio-temporal quality is altered, and a more complex and detailed functional connectivity structure is observed. The combined calcium/hemoglobin imaging technique described here will enable the dissociation of changes in ionic and hemodynamic functional structure and neurovascular coupling and provide a framework for subsequent studies of neurological disease such as stroke. PMID:29049297

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Image retrieval and processing system version 2.0 development work

NASA Technical Reports Server (NTRS)

Slavney, Susan H.; Guinness, Edward A.

1991-01-01

The Image Retrieval and Processing System (IRPS) is a software package developed at Washington University and used by the NASA Regional Planetary Image Facilities (RPIF's). The IRPS combines data base management and image processing components to allow the user to examine catalogs of image data, locate the data of interest, and perform radiometric and geometric calibration of the data in preparation for analysis. Version 1.0 of IRPS was completed in Aug. 1989 and was installed at several IRPS's. Other RPIF's use remote logins via NASA Science Internet to access IRPS at Washington University. Work was begun on designing and population a catalog of Magellan image products that will be part of IRPS Version 2.0, planned for release by the end of calendar year 1991. With this catalog, a user will be able to search by orbit and by location for Magellan Basic Image Data Records (BIDR's), Mosaicked Image Data Records (MIDR's), and Altimetry-Radiometry Composite Data Records (ARCDR's). The catalog will include the Magellan CD-ROM volume, director, and file name for each data product. The image processing component of IRPS is based on the Planetary Image Cartography Software (PICS) developed by the U.S. Geological Survey, Flagstaff, Arizona. To augment PICS capabilities, a set of image processing programs were developed that are compatible with PICS-format images. This software includes general-purpose functions that PICS does not have, analysis and utility programs for specific data sets, and programs from other sources that were modified to work with PICS images. Some of the software will be integrated into the Version 2.0 release of IRPS. A table is presented that lists the programs with a brief functional description of each.

General tensor discriminant analysis and gabor features for gait recognition.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

Percent area coverage through image analysis

NASA Astrophysics Data System (ADS)

Wong, Chung M.; Hong, Sung M.; Liu, De-Ling

2016-09-01

The notion of percent area coverage (PAC) has been used to characterize surface cleanliness levels in the spacecraft contamination control community. Due to the lack of detailed particle data, PAC has been conventionally calculated by multiplying the particle surface density in predetermined particle size bins by a set of coefficients per MIL-STD-1246C. In deriving the set of coefficients, the surface particle size distribution is assumed to follow a log-normal relation between particle density and particle size, while the cross-sectional area function is given as a combination of regular geometric shapes. For particles with irregular shapes, the cross-sectional area function cannot describe the true particle area and, therefore, may introduce error in the PAC calculation. Other errors may also be introduced by using the lognormal surface particle size distribution function that highly depends on the environmental cleanliness and cleaning process. In this paper, we present PAC measurements from silicon witness wafers that collected fallouts from a fabric material after vibration testing. PAC calculations were performed through analysis of microscope images and compare them to values derived through the MIL-STD-1246C method. Our results showed that the MIL-STD-1246C method does provide a reasonable upper bound to the PAC values determined through image analysis, in particular for PAC values below 0.1.

Image correlation based method for the analysis of collagen fibers patterns

NASA Astrophysics Data System (ADS)

Rosa, Ramon G. T.; Pratavieira, Sebastião.; Kurachi, Cristina

2015-06-01

The collagen fibers are one of the most important structural proteins in skin, being responsible for its strength and flexibility. It is known that their properties, like fibers density, ordination and mean diameter can be affected by several skin conditions, what makes these properties a good parameter to be used on the diagnosis and evaluation of skin aging, cancer, healing, among other conditions. There is, however, a need for methods capable of analyzing quantitatively the organization patterns of these fibers. To address this need, we developed a method based on the autocorrelation function of the images that allows the construction of vector field plots of the fibers directions and does not require any kind of curve fitting or optimization. The analyzed images were obtained through Second Harmonic Generation Imaging Microscopy. This paper presents a concise review on the autocorrelation function and some of its applications to image processing, details the developed method and the results obtained through the analysis of hystopathological slides of landrace porcine skin. The method has high accuracy on the determination of the fibers direction and presents high performance. We look forward to perform further studies keeping track of different skin conditions over time.

Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

PubMed Central

Yan, Pingkum; Zhou, Xiaobo; Shah, Mubarak; Wong, Stephen T. C.

2010-01-01

High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems. PMID:18270043

Spatiotemporal relations of primary sensorimotor and secondary motor activation patterns mapped by NIR imaging

PubMed Central

Khan, Bilal; Chand, Pankaj; Alexandrakis, George

2011-01-01

Functional near infrared (fNIR) imaging was used to identify spatiotemporal relations between spatially distinct cortical regions activated during various hand and arm motion protocols. Imaging was performed over a field of view (FOV, 12 x 8.4 cm) including the secondary motor, primary sensorimotor, and the posterior parietal cortices over a single brain hemisphere. This is a more extended FOV than typically used in current fNIR studies. Three subjects performed four motor tasks that induced activation over this extended FOV. The tasks included card flipping (pronation and supination) that, to our knowledge, has not been performed in previous functional magnetic resonance imaging (fMRI) or fNIR studies. An earlier rise and a longer duration of the hemodynamic activation response were found in tasks requiring increased physical or mental effort. Additionally, analysis of activation images by cluster component analysis (CCA) demonstrated that cortical regions can be grouped into clusters, which can be adjacent or distant from each other, that have similar temporal activation patterns depending on whether the performed motor task is guided by visual or tactile feedback. These analyses highlight the future potential of fNIR imaging to tackle clinically relevant questions regarding the spatiotemporal relations between different sensorimotor cortex regions, e.g. ones involved in the rehabilitation response to motor impairments. PMID:22162826

Method for multimodal analysis of independent source differences in schizophrenia: combining gray matter structural and auditory oddball functional data.

PubMed

Calhoun, V D; Adali, T; Giuliani, N R; Pekar, J J; Kiehl, K A; Pearlson, G D

2006-01-01

The acquisition of both structural MRI (sMRI) and functional MRI (fMRI) data 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 independent component analysis across image modalities, specifically, gray matter images and fMRI activation images as well as a joint histogram visualization technique. Joint independent component analysis (jICA) is used to decompose a matrix with a given row consisting of an fMRI activation image resulting from auditory oddball target stimuli and an sMRI gray matter segmentation image, collected from the same individual. We analyzed data collected on a group of schizophrenia patients and healthy controls using the jICA approach. Spatially independent joint-components are estimated and resulting components were further analyzed only if they showed a significant difference between patients and controls. The main finding was that group differences in bilateral parietal and frontal as well as posterior temporal regions in gray matter were associated with bilateral temporal regions activated by the auditory oddball target stimuli. A finding of less patient gray matter and less hemodynamic activity for target detection in these bilateral anterior temporal lobe regions was consistent with previous work. An unexpected corollary to this finding was that, in the regions showing the largest group differences, gray matter concentrations were larger in patients vs. controls, suggesting that more gray matter may be related to less functional connectivity in the auditory oddball fMRI task. Hum Brain Mapp, 2005. (c) 2005 Wiley-Liss, Inc.

Quantification of Diaphragm Mechanics in Pompe Disease Using Dynamic 3D MRI

PubMed Central

Mogalle, Katja; Perez-Rovira, Adria; Ciet, Pierluigi; Wens, Stephan C. A.; van Doorn, Pieter A.; Tiddens, Harm A. W. M.; van der Ploeg, Ans T.; de Bruijne, Marleen

2016-01-01

Background Diaphragm weakness is the main reason for respiratory dysfunction in patients with Pompe disease, a progressive metabolic myopathy affecting respiratory and limb-girdle muscles. Since respiratory failure is the major cause of death among adult patients, early identification of respiratory muscle involvement is necessary to initiate treatment in time and possibly prevent irreversible damage. In this paper we investigate the suitability of dynamic MR imaging in combination with state-of-the-art image analysis methods to assess respiratory muscle weakness. Methods The proposed methodology relies on image registration and lung surface extraction to quantify lung kinematics during breathing. This allows for the extraction of geometry and motion features of the lung that characterize the independent contribution of the diaphragm and the thoracic muscles to the respiratory cycle. Results Results in 16 3D+t MRI scans (10 Pompe patients and 6 controls) of a slow expiratory maneuver show that kinematic analysis from dynamic 3D images reveals important additional information about diaphragm mechanics and respiratory muscle involvement when compared to conventional pulmonary function tests. Pompe patients with severely reduced pulmonary function showed severe diaphragm weakness presented by minimal motion of the diaphragm. In patients with moderately reduced pulmonary function, cranial displacement of posterior diaphragm parts was reduced and the diaphragm dome was oriented more horizontally at full inspiration compared to healthy controls. Conclusion Dynamic 3D MRI provides data for analyzing the contribution of both diaphragm and thoracic muscles independently. The proposed image analysis method has the potential to detect less severe diaphragm weakness and could thus be used to determine the optimal start of treatment in adult patients with Pompe disease in prospect of increased treatment response. PMID:27391236

Altered functional MR imaging language activation in elderly individuals with cerebral leukoaraiosis.

PubMed

Welker, Kirk M; De Jesus, Reordan O; Watson, Robert E; Machulda, Mary M; Jack, Clifford R

2012-10-01

To test the hypothesis that leukoaraiosis alters functional activation during a semantic decision language task. With institutional review board approval and written informed consent, 18 right-handed, cognitively healthy elderly participants with an aggregate leukoaraiosis lesion volume of more than 25 cm(3) and 18 age-matched control participants with less than 5 cm(3) of leukoaraiosis underwent functional MR imaging to allow comparison of activation during semantic decisions with that during visual perceptual decisions. Brain statistical maps were derived from the general linear model. Spatially normalized group t maps were created from individual contrast images. A cluster extent threshold of 215 voxels was used to correct for multiple comparisons. Intergroup random effects analysis was performed. Language laterality indexes were calculated for each participant. In control participants, semantic decisions activated the bilateral visual cortex, left posteroinferior temporal lobe, left posterior cingulate gyrus, left frontal lobe expressive language regions, and left basal ganglia. Visual perceptual decisions activated the right parietal and posterior temporal lobes. Participants with leukoaraiosis showed reduced activation in all regions associated with semantic decisions; however, activation associated with visual perceptual decisions increased in extent. Intergroup analysis showed significant activation decreases in the left anterior occipital lobe (P=.016), right posterior temporal lobe (P=.048), and right basal ganglia (P=.009) in particpants with leukoariosis. Individual participant laterality indexes showed a strong trend (P=.059) toward greater left lateralization in the leukoaraiosis group. Moderate leukoaraiosis is associated with atypical functional activation during semantic decision tasks. Consequently, leukoaraiosis is an important confounding variable in functional MR imaging studies of elderly individuals. © RSNA, 2012.

Automatic microscopy for mitotic cell location.

NASA Technical Reports Server (NTRS)

Herron, J.; Ranshaw, R.; Castle, J.; Wald, N.

1972-01-01

Advances are reported in the development of an automatic microscope with which to locate hematologic or other cells in mitosis for subsequent chromosome analysis. The system under development is designed to perform the functions of: slide scanning to locate metaphase cells; conversion of images of selected cells into binary form; and on-line computer analysis of the digitized image for significant cytogenetic data. Cell detection criteria are evaluated using a test sample of 100 mitotic cells and 100 artifacts.

Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability analysis.

PubMed

Hock, Sabrina; Hasenauer, Jan; Theis, Fabian J

2013-01-01

Diffusion is a key component of many biological processes such as chemotaxis, developmental differentiation and tissue morphogenesis. Since recently, the spatial gradients caused by diffusion can be assessed in-vitro and in-vivo using microscopy based imaging techniques. The resulting time-series of two dimensional, high-resolutions images in combination with mechanistic models enable the quantitative analysis of the underlying mechanisms. However, such a model-based analysis is still challenging due to measurement noise and sparse observations, which result in uncertainties of the model parameters. We introduce a likelihood function for image-based measurements with log-normal distributed noise. Based upon this likelihood function we formulate the maximum likelihood estimation problem, which is solved using PDE-constrained optimization methods. To assess the uncertainty and practical identifiability of the parameters we introduce profile likelihoods for diffusion processes. As proof of concept, we model certain aspects of the guidance of dendritic cells towards lymphatic vessels, an example for haptotaxis. Using a realistic set of artificial measurement data, we estimate the five kinetic parameters of this model and compute profile likelihoods. Our novel approach for the estimation of model parameters from image data as well as the proposed identifiability analysis approach is widely applicable to diffusion processes. The profile likelihood based method provides more rigorous uncertainty bounds in contrast to local approximation methods.

An Automated Method of Scanning Probe Microscopy (SPM) Data Analysis and Reactive Site Tracking for Mineral-Water Interface Reactions Observed at the Nanometer Scale

NASA Astrophysics Data System (ADS)

Campbell, B. D.; Higgins, S. R.

2008-12-01

Developing a method for bridging the gap between macroscopic and microscopic measurements of reaction kinetics at the mineral-water interface has important implications in geological and chemical fields. Investigating these reactions on the nanometer scale with SPM is often limited by image analysis and data extraction due to the large quantity of data usually obtained in SPM experiments. Here we present a computer algorithm for automated analysis of mineral-water interface reactions. This algorithm automates the analysis of sequential SPM images by identifying the kinetically active surface sites (i.e., step edges), and by tracking the displacement of these sites from image to image. The step edge positions in each image are readily identified and tracked through time by a standard edge detection algorithm followed by statistical analysis on the Hough Transform of the edge-mapped image. By quantifying this displacement as a function of time, the rate of step edge displacement is determined. Furthermore, the total edge length, also determined from analysis of the Hough Transform, combined with the computed step speed, yields the surface area normalized rate of the reaction. The algorithm was applied to a study of the spiral growth of the calcite(104) surface from supersaturated solutions, yielding results almost 20 times faster than performing this analysis by hand, with results being statistically similar for both analysis methods. This advance in analysis of kinetic data from SPM images will facilitate the building of experimental databases on the microscopic kinetics of mineral-water interface reactions.

Mathematical Development and Computational Analysis of Harmonic Phase-Magnetic Resonance Imaging (HARP-MRI) Based on Bloch Nuclear Magnetic Resonance (NMR) Diffusion Model for Myocardial Motion.

PubMed

Dada, Michael O; Jayeoba, Babatunde; Awojoyogbe, Bamidele O; Uno, Uno E; Awe, Oluseyi E

2017-09-13

Harmonic Phase-Magnetic Resonance Imaging (HARP-MRI) is a tagged image analysis method that can measure myocardial motion and strain in near real-time and is considered a potential candidate to make magnetic resonance tagging clinically viable. However, analytical expressions of radially tagged transverse magnetization in polar coordinates (which is required to appropriately describe the shape of the heart) have not been explored because the physics required to directly connect myocardial deformation of tagged Nuclear Magnetic Resonance (NMR) transverse magnetization in polar geometry and the appropriate harmonic phase parameters are not yet available. The analytical solution of Bloch NMR diffusion equation in spherical geometry with appropriate spherical wave tagging function is important for proper analysis and monitoring of heart systolic and diastolic deformation with relevant boundary conditions. In this study, we applied Harmonic Phase MRI method to compute the difference between tagged and untagged NMR transverse magnetization based on the Bloch NMR diffusion equation and obtained radial wave tagging function for analysis of myocardial motion. The analytical solution of the Bloch NMR equations and the computational simulation of myocardial motion as developed in this study are intended to significantly improve healthcare for accurate diagnosis, prognosis and treatment of cardiovascular related deceases at the lowest cost because MRI scan is still one of the most expensive anywhere. The analysis is fundamental and significant because all Magnetic Resonance Imaging techniques are based on the Bloch NMR flow equations.

Image analysis tools and emerging algorithms for expression proteomics

PubMed Central

English, Jane A.; Lisacek, Frederique; Morris, Jeffrey S.; Yang, Guang-Zhong; Dunn, Michael J.

2012-01-01

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-D Gel Electrophoresis (2-DE) technique of protein separation, and by first covering signal analysis for Mass Spectrometry (MS), we also explain the current image analysis workflow for the emerging high-throughput ‘shotgun’ proteomics platform of Liquid Chromatography coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whilst existing commercial and academic packages and their workflows are described from both a user’s and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS. PMID:21046614

Simultaneous dual-color fluorescence microscope: a characterization study.

PubMed

Li, Zheng; Chen, Xiaodong; Ren, Liqiang; Song, Jie; Li, Yuhua; Zheng, Bin; Liu, Hong

2013-01-01

High spatial resolution and geometric accuracy is crucial for chromosomal analysis of clinical cytogenetic applications. High resolution and rapid simultaneous acquisition of multiple fluorescent wavelengths can be achieved by utilizing concurrent imaging with multiple detectors. However, such class of microscopic systems functions differently from traditional fluorescence microscopes. To develop a practical characterization framework to assess and optimize the performance of a high resolution and dual-color fluorescence microscope designed for clinical chromosomal analysis. A dual-band microscopic imaging system utilizes a dichroic mirror, two sets of specially selected optical filters, and two detectors to simultaneously acquire two fluorescent wavelengths. The system's geometric distortion, linearity, the modulation transfer function, and the dual detectors' alignment were characterized. Experiment results show that the geometric distortion at lens periphery is less than 1%. Both fluorescent channels show linear signal responses, but there exists discrepancy between the two due to the detectors' non-uniform response ratio to different wavelengths. In terms of the spatial resolution, the two contrast transfer function curves trend agreeably with the spatial frequency. The alignment measurement allows quantitatively assessing the cameras' alignment. A result image of adjusted alignment is demonstrated to show the reduced discrepancy by using the alignment measurement method. In this paper, we present a system characterization study and its methods for a specially designed imaging system for clinical cytogenetic applications. The presented characterization methods are not only unique to this dual-color imaging system but also applicable to evaluation and optimization of other similar multi-color microscopic image systems for improving their clinical utilities for future cytogenetic applications.

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

[Research on Time-frequency Characteristics of Magneto-acoustic Signal of Different Thickness Medium Based on Wave Summing Method].

PubMed

Zhang, Shunqi; Yin, Tao; Ma, Ren; Liu, Zhipeng

2015-08-01

Functional imaging method of biological electrical characteristics based on magneto-acoustic effect gives valuable information of tissue in early tumor diagnosis, therein time and frequency characteristics analysis of magneto-acoustic signal is important in image reconstruction. This paper proposes wave summing method based on Green function solution for acoustic source of magneto-acoustic effect. Simulations and analysis under quasi 1D transmission condition are carried out to time and frequency characteristics of magneto-acoustic signal of models with different thickness. Simulation results of magneto-acoustic signal were verified through experiments. Results of the simulation with different thickness showed that time-frequency characteristics of magneto-acoustic signal reflected thickness of sample. Thin sample, which is less than one wavelength of pulse, and thick sample, which is larger than one wavelength, showed different summed waveform and frequency characteristics, due to difference of summing thickness. Experimental results verified theoretical analysis and simulation results. This research has laid a foundation for acoustic source and conductivity reconstruction to the medium with different thickness in magneto-acoustic imaging.

The Imperial Curriculum: Racial Images and Education in the British Colonial Experience.

ERIC Educational Resources Information Center

Mangan, J. A., Ed.

This book presents a comparative analysis of racial attitudes within formal schooling of both Britain and its former dominions and colonies. It provides essays that examine racism, education, and imperialism, and focuses on the function of education, curriculum, and textbooks in shaping imperial images of dominance and deference. The following…

Functional imaging of glucose-evoked rat islet activities using transient intrinsic optical signals

NASA Astrophysics Data System (ADS)

Yao, Xin-Cheng; Cui, Wan-Xing; Li, Yi-Chao; Zhang, Wei; Lu, Rong-Wen; Thompson, Anthony; Amthor, Franklin; Wang, Xu-Jing

2012-05-01

We demonstrate intrinsic optical signal (IOS) imaging of intact rat islet, which consists of many endocrine cells working together. A near-infrared digital microscope was employed for optical monitoring of islet activities evoked by glucose stimulation. Dynamic NIR images revealed transient IOS responses in the islet activated by low-dose (2.75 mM) and high-dose (5.5 mM) glucose stimuli. Comparative experiments and quantitative analysis indicated that both glucose metabolism and calcium/insulin dynamics might contribute to the observed IOS responses. Further investigation of the IOS imaging technology may provide a high resolution method for ex vivo functional examination of the islet, which is important for advanced study of diabetes associated islet dysfunctions and for improved quality control of donor islets for transplantation.

Magnetic resonance imaging for diagnosis of early Alzheimer's disease.

PubMed

Colliot, O; Hamelin, L; Sarazin, M

2013-10-01

A major challenge for neuroimaging is to contribute to the early diagnosis of Alzheimer's disease (AD). In particular, magnetic resonance imaging (MRI) allows detecting different types of structural and functional abnormalities at an early stage of the disease. Anatomical MRI is the most widely used technique and provides local and global measures of atrophy. The recent diagnostic criteria of "mild cognitive impairment due to AD" include hippocampal atrophy, which is considered a marker of neuronal injury. Advanced image analysis techniques generate automatic and reproducible measures both in the hippocampus and throughout the whole brain. Recent modalities such as diffusion-tensor imaging and resting-state functional MRI provide additional measures that could contribute to the early diagnosis but require further validation. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

NASA Astrophysics Data System (ADS)

Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

2013-03-01

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

Low-level processing for real-time image analysis

NASA Technical Reports Server (NTRS)

Eskenazi, R.; Wilf, J. M.

1979-01-01

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given.

Restoration of motion blurred image with Lucy-Richardson algorithm

NASA Astrophysics Data System (ADS)

Li, Jing; Liu, Zhao Hui; Zhou, Liang

2015-10-01

Images will be blurred by relative motion between the camera and the object of interest. In this paper, we analyzed the process of motion-blurred image, and demonstrated a restoration method based on Lucy-Richardson algorithm. The blur extent and angle can be estimated by Radon transform algorithm and auto-correlation function, respectively, and then the point spread function (PSF) of the motion-blurred image can be obtained. Thus with the help of the obtained PSF, the Lucy-Richardson restoration algorithm is used for experimental analysis on the motion-blurred images that have different blur extents, spatial resolutions and signal-to-noise ratios (SNR's). Further, its effectiveness is also evaluated by structural similarity (SSIM). Further studies show that, at first, for the image with a spatial frequency of 0.2 per pixel, the modulation transfer function (MTF) of the restored images can maintains above 0.7 when the blur extent is no bigger than 13 pixels. That means the method compensates low frequency information of the image, while attenuates high frequency information. At second, we fund that the method is more effective on condition that the product of the blur extent and spatial frequency is smaller than 3.75. Finally, the Lucy-Richardson algorithm is found insensitive to the Gaussian noise (of which the variance is not bigger than 0.1) by calculating the MTF of the restored image.

Spatially resolved chemical analysis of cicada wings using laser-ablation electrospray ionization (LAESI) imaging mass spectrometry (IMS).

PubMed

Román, Jessica K; Walsh, Callee M; Oh, Junho; Dana, Catherine E; Hong, Sungmin; Jo, Kyoo D; Alleyne, Marianne; Miljkovic, Nenad; Cropek, Donald M

2018-03-01

Laser-ablation electrospray ionization (LAESI) imaging mass spectrometry (IMS) is an emerging bioanalytical tool for direct imaging and analysis of biological tissues. Performing ionization in an ambient environment, this technique requires little sample preparation and no additional matrix, and can be performed on natural, uneven surfaces. When combined with optical microscopy, the investigation of biological samples by LAESI allows for spatially resolved compositional analysis. We demonstrate here the applicability of LAESI-IMS for the chemical analysis of thin, desiccated biological samples, specifically Neotibicen pruinosus cicada wings. Positive-ion LAESI-IMS accurate ion-map data was acquired from several wing cells and superimposed onto optical images allowing for compositional comparisons across areas of the wing. Various putative chemical identifications were made indicating the presence of hydrocarbons, lipids/esters, amines/amides, and sulfonated/phosphorylated compounds. With the spatial resolution capability, surprising chemical distribution patterns were observed across the cicada wing, which may assist in correlating trends in surface properties with chemical distribution. Observed ions were either (1) equally dispersed across the wing, (2) more concentrated closer to the body of the insect (proximal end), or (3) more concentrated toward the tip of the wing (distal end). These findings demonstrate LAESI-IMS as a tool for the acquisition of spatially resolved chemical information from fragile, dried insect wings. This LAESI-IMS technique has important implications for the study of functional biomaterials, where understanding the correlation between chemical composition, physical structure, and biological function is critical. Graphical abstract Positive-ion laser-ablation electrospray ionization mass spectrometry coupled with optical imaging provides a powerful tool for the spatially resolved chemical analysis of cicada wings.

Quantitative Computerized Two-Point Correlation Analysis of Lung CT Scans Correlates With Pulmonary Function in Pulmonary Sarcoidosis

PubMed Central

Erdal, Barbaros Selnur; Yildiz, Vedat; King, Mark A.; Patterson, Andrew T.; Knopp, Michael V.; Clymer, Bradley D.

2012-01-01

Background: Chest CT scans are commonly used to clinically assess disease severity in patients presenting with pulmonary sarcoidosis. Despite their ability to reliably detect subtle changes in lung disease, the utility of chest CT scans for guiding therapy is limited by the fact that image interpretation by radiologists is qualitative and highly variable. We sought to create a computerized CT image analysis tool that would provide quantitative and clinically relevant information. Methods: We established that a two-point correlation analysis approach reduced the background signal attendant to normal lung structures, such as blood vessels, airways, and lymphatics while highlighting diseased tissue. This approach was applied to multiple lung fields to generate an overall lung texture score (LTS) representing the quantity of diseased lung parenchyma. Using deidentified lung CT scan and pulmonary function test (PFT) data from The Ohio State University Medical Center’s Information Warehouse, we analyzed 71 consecutive CT scans from patients with sarcoidosis for whom simultaneous matching PFTs were available to determine whether the LTS correlated with standard PFT results. Results: We found a high correlation between LTS and FVC, total lung capacity, and diffusing capacity of the lung for carbon monoxide (P < .0001 for all comparisons). Moreover, LTS was equivalent to PFTs for the detection of active lung disease. The image analysis protocol was conducted quickly (< 1 min per study) on a standard laptop computer connected to a publicly available National Institutes of Health ImageJ toolkit. Conclusions: The two-point image analysis tool is highly practical and appears to reliably assess lung disease severity. We predict that this tool will be useful for clinical and research applications. PMID:22628487

Brain–heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field

PubMed Central

Raven, Erika P.; Duyn, Jeff H.

2016-01-01

Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain–heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain–heart interactions. PMID:27044994

Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field.

PubMed

Chang, Catie; Raven, Erika P; Duyn, Jeff H

2016-05-13

Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain-heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain-heart interactions. © 2016 The Author(s).

An interactive method based on the live wire for segmentation of the breast in mammography images.

PubMed

Zewei, Zhang; Tianyue, Wang; Li, Guo; Tingting, Wang; Lu, Xu

2014-01-01

In order to improve accuracy of computer-aided diagnosis of breast lumps, the authors introduce an improved interactive segmentation method based on Live Wire. This paper presents the Gabor filters and FCM clustering algorithm is introduced to the Live Wire cost function definition. According to the image FCM analysis for image edge enhancement, we eliminate the interference of weak edge and access external features clear segmentation results of breast lumps through improving Live Wire on two cases of breast segmentation data. Compared with the traditional method of image segmentation, experimental results show that the method achieves more accurate segmentation of breast lumps and provides more accurate objective basis on quantitative and qualitative analysis of breast lumps.

Precision analysis for standard deviation measurements of immobile single fluorescent molecule images.

PubMed

DeSantis, Michael C; DeCenzo, Shawn H; Li, Je-Luen; Wang, Y M

2010-03-29

Standard deviation measurements of intensity profiles of stationary single fluorescent molecules are useful for studying axial localization, molecular orientation, and a fluorescence imaging system's spatial resolution. Here we report on the analysis of the precision of standard deviation measurements of intensity profiles of single fluorescent molecules imaged using an EMCCD camera.We have developed an analytical expression for the standard deviation measurement error of a single image which is a function of the total number of detected photons, the background photon noise, and the camera pixel size. The theoretical results agree well with the experimental, simulation, and numerical integration results. Using this expression, we show that single-molecule standard deviation measurements offer nanometer precision for a large range of experimental parameters.

Beckwith-Wiedemann and IMAGe syndromes: two very different diseases caused by mutations on the same gene.

PubMed

Milani, Donatella; Pezzani, Lidia; Tabano, Silvia; Miozzo, Monica

2014-01-01

Genomic imprinting is an epigenetically regulated mechanism leading to parental-origin allele-specific expression. Beckwith-Wiedemann syndrome (BWS) is an imprinting disease related to 11p15.5 genetic and epigenetic alterations, among them loss-of-function CDKN1C mutations. Intriguing is that CDKN1C gain-of-function variations were recently found in patients with IMAGe syndrome (intrauterine growth restriction, metaphyseal dysplasia, congenital adrenal hypoplasia, and genital anomalies). BWS and IMAGe share an imprinted mode of inheritance; familial analysis demonstrated the presence of the phenotype exclusively when the mutant CDKN1C allele is inherited from the mother. Interestingly, both IMAGe and BWS are characterized by growth disturbances, although with opposite clinical phenotypes; IMAGe patients display growth restriction whereas BWS patients display overgrowth. CDKN1C codifies for CDKN1C/KIP2, a nuclear protein and potent tight-binding inhibitor of several cyclin/Cdk complexes, playing a role in maintenance of the nonproliferative state of cells. The mirror phenotype of BWS and IMAGe can be, at least in part, explained by the effect of mutations on protein functions. All the IMAGe-associated mutations are clustered in the proliferating cell nuclear antigen-binding domain of CDKN1C and cause a dramatic increase in the stability of the protein, which probably results in a functional gain of growth inhibition properties. In contrast, BWS mutations are not clustered within a single domain, are loss-of-function, and promote cell proliferation. CDKN1C is an example of allelic heterogeneity associated with opposite syndromes.

Neuroimaging in epilepsy.

PubMed

Sidhu, Meneka Kaur; Duncan, John S; Sander, Josemir W

2018-05-17

Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy. Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.

Brain functional BOLD perturbation modelling for forward fMRI and inverse mapping

PubMed Central

Robinson, Jennifer; Calhoun, Vince

2018-01-01

Purpose To computationally separate dynamic brain functional BOLD responses from static background in a brain functional activity for forward fMRI signal analysis and inverse mapping. Methods A brain functional activity is represented in terms of magnetic source by a perturbation model: χ = χ0 +δχ, with δχ for BOLD magnetic perturbations and χ0 for background. A brain fMRI experiment produces a timeseries of complex-valued images (T2* images), whereby we extract the BOLD phase signals (denoted by δP) by a complex division. By solving an inverse problem, we reconstruct the BOLD δχ dataset from the δP dataset, and the brain χ distribution from a (unwrapped) T2* phase image. Given a 4D dataset of task BOLD fMRI, we implement brain functional mapping by temporal correlation analysis. Results Through a high-field (7T) and high-resolution (0.5mm in plane) task fMRI experiment, we demonstrated in detail the BOLD perturbation model for fMRI phase signal separation (P + δP) and reconstructing intrinsic brain magnetic source (χ and δχ). We also provided to a low-field (3T) and low-resolution (2mm) task fMRI experiment in support of single-subject fMRI study. Our experiments show that the δχ-depicted functional map reveals bidirectional BOLD χ perturbations during the task performance. Conclusions The BOLD perturbation model allows us to separate fMRI phase signal (by complex division) and to perform inverse mapping for pure BOLD δχ reconstruction for intrinsic functional χ mapping. The full brain χ reconstruction (from unwrapped fMRI phase) provides a new brain tissue image that allows to scrutinize the brain tissue idiosyncrasy for the pure BOLD δχ response through an automatic function/structure co-localization. PMID:29351339

Chronic kidney disease: Pathological and functional evaluation with intravoxel incoherent motion diffusion-weighted imaging.

PubMed

Mao, Wei; Zhou, Jianjun; Zeng, Mengsu; Ding, Yuqin; Qu, Lijie; Chen, Caizhong; Ding, Xiaoqiang; Wang, Yaqiong; Fu, Caixia

2018-05-01

Because chronic kidney disease (CKD) is a worldwide problem, accurate pathological and functional evaluation is required for planning treatment and follow-up. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can assess both capillary perfusion and tissue diffusion and may be helpful in evaluating renal function and pathology. To evaluate functional and pathological alterations in CKD by applying IVIM-DWI. Prospective study. In all, 72 CKD patients who required renal biopsy and 20 healthy volunteers. 1.5T. All subjects underwent IVIM-DWI of the kidneys, and image analysis was performed by two radiologists. The mean values of true diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) were acquired from renal parenchyma. Correlation between IVIM-DWI parameters and estimated glomerular filtration rate (eGFR), as well as pathological damage, were assessed. One-way analysis of variance (ANOVA), paired sample t-test and Spearman correlation analysis. The paired sample t-test revealed that IVIM-DWI parameters were significantly lower in medulla than cortex for both patients and controls (P < 0.01). Regardless of whether eGFR was reduced, ANOVA revealed that f values of renal parenchyma were significantly lower in patients than controls (P < 0.05). Spearman correlation analysis revealed that there were positive correlations between eGFR and D (cortex, r = 0.466, P < 0.001; medulla, r = 0.491, P < 0.001), and between eGFR and f (cortex, r = 0.713, P < 0.001; medulla, r = 0.512, P < 0.001). Negative correlations were found between f and glomerular injury (cortex, r = -0.773, P < 0.001; medulla, r = -0.629, P < 0.001), and between f and tubulointerstitial lesion (cortex, r = -0.728, P < 0.001; medulla, r = -0.547, P < 0.001). IVIM-DWI might be feasible for noninvasive evaluation of renal function and pathology of CKD, especially in detection of renal insufficiency at an early stage. 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1251-1259. © 2017 International Society for Magnetic Resonance in Medicine.

Mesoscale brain explorer, a flexible python-based image analysis and visualization tool.

PubMed

Haupt, Dirk; Vanni, Matthieu P; Bolanos, Federico; Mitelut, Catalin; LeDue, Jeffrey M; Murphy, Tim H

2017-07-01

Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Reprint of Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method

NASA Astrophysics Data System (ADS)

D'Ambra, Pasqua; Tartaglione, Gaetano

2015-04-01

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology.

Solution of Ambrosio-Tortorelli model for image segmentation by generalized relaxation method

NASA Astrophysics Data System (ADS)

D'Ambra, Pasqua; Tartaglione, Gaetano

2015-03-01

Image segmentation addresses the problem to partition a given image into its constituent objects and then to identify the boundaries of the objects. This problem can be formulated in terms of a variational model aimed to find optimal approximations of a bounded function by piecewise-smooth functions, minimizing a given functional. The corresponding Euler-Lagrange equations are a set of two coupled elliptic partial differential equations with varying coefficients. Numerical solution of the above system often relies on alternating minimization techniques involving descent methods coupled with explicit or semi-implicit finite-difference discretization schemes, which are slowly convergent and poorly scalable with respect to image size. In this work we focus on generalized relaxation methods also coupled with multigrid linear solvers, when a finite-difference discretization is applied to the Euler-Lagrange equations of Ambrosio-Tortorelli model. We show that non-linear Gauss-Seidel, accelerated by inner linear iterations, is an effective method for large-scale image analysis as those arising from high-throughput screening platforms for stem cells targeted differentiation, where one of the main goal is segmentation of thousand of images to analyze cell colonies morphology.

Direct magnetic field estimation based on echo planar raw data.

PubMed

Testud, Frederik; Splitthoff, Daniel Nicolas; Speck, Oliver; Hennig, Jürgen; Zaitsev, Maxim

2010-07-01

Gradient recalled echo echo planar imaging is widely used in functional magnetic resonance imaging. The fast data acquisition is, however, very sensitive to field inhomogeneities which manifest themselves as artifacts in the images. Typically used correction methods have the common deficit that the data for the correction are acquired only once at the beginning of the experiment, assuming the field inhomogeneity distribution B(0) does not change over the course of the experiment. In this paper, methods to extract the magnetic field distribution from the acquired k-space data or from the reconstructed phase image of a gradient echo planar sequence are compared and extended. A common derivation for the presented approaches provides a solid theoretical basis, enables a fair comparison and demonstrates the equivalence of the k-space and the image phase based approaches. The image phase analysis is extended here to calculate the local gradient in the readout direction and improvements are introduced to the echo shift analysis, referred to here as "k-space filtering analysis." The described methods are compared to experimentally acquired B(0) maps in phantoms and in vivo. The k-space filtering analysis presented in this work demonstrated to be the most sensitive method to detect field inhomogeneities.

Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

ERIC Educational Resources Information Center

Calhoun, Vince D.; Allen, Elena

2013-01-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in…

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Structural Connectivity Changes Underlying Altered Working Memory Networks in Mild Cognitive Impairment: A Three-Way Image Fusion Analysis.

PubMed

Teipel, Stefan; Ehlers, Inga; Erbe, Anna; Holzmann, Carsten; Lau, Esther; Hauenstein, Karlheinz; Berger, Christoph

2015-01-01

Working memory impairment is among the earliest signs of cognitive decline in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We aimed to study the functional and structural substrate of working memory impairment in early AD dementia and MCI. We studied a group of 12 MCI and AD subjects compared to 12 age- and gender-matched healthy elderly controls using diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI) during a 2-back versus 1-back letter recognition task. We performed a three-way image fusion analysis with joint independent component analysis of cortical activation during working memory, and DTI derived measures of fractional anisotropy (FA) and the mode of anisotropy. We found significant hypoactivation in posterior brain areas and relative hyperactivation in anterior brain areas during working memory in AD/MCI subjects compared to controls. Corresponding independent components from DTI data revealed reduced FA and reduced mode of anisotropy in intracortical projecting fiber tracts with posterior predominance and increased FA and increased mode along the corticospinal tract in AD/MCI compared to controls. Our findings suggest that impairments of structural fiber tract integrity accompany breakdown of posterior and relatively preserved anterior cortical activation during working memory performance in MCI/AD subjects. Copyright © 2014 by the American Society of Neuroimaging.

Field Ground Truthing Data Collector - a Mobile Toolkit for Image Analysis and Processing

NASA Astrophysics Data System (ADS)

Meng, X.

2012-07-01

Field Ground Truthing Data Collector is one of the four key components of the NASA funded ICCaRS project, being developed in Southeast Michigan. The ICCaRS ground truthing toolkit entertains comprehensive functions: 1) Field functions, including determining locations through GPS, gathering and geo-referencing visual data, laying out ground control points for AEROKAT flights, measuring the flight distance and height, and entering observations of land cover (and use) and health conditions of ecosystems and environments in the vicinity of the flight field; 2) Server synchronization functions, such as, downloading study-area maps, aerial photos and satellite images, uploading and synchronizing field-collected data with the distributed databases, calling the geospatial web services on the server side to conduct spatial querying, image analysis and processing, and receiving the processed results in field for near-real-time validation; and 3) Social network communication functions for direct technical assistance and pedagogical support, e.g., having video-conference calls in field with the supporting educators, scientists, and technologists, participating in Webinars, or engaging discussions with other-learning portals. This customized software package is being built on Apple iPhone/iPad and Google Maps/Earth. The technical infrastructures, data models, coupling methods between distributed geospatial data processing and field data collector tools, remote communication interfaces, coding schema, and functional flow charts will be illustrated and explained at the presentation. A pilot case study will be also demonstrated.

A Novel Imaging Analysis Method for Capturing Pharyngeal Constriction During Swallowing.

PubMed

Schwertner, Ryan W; Garand, Kendrea L; Pearson, William G

2016-01-01

Videofluoroscopic imaging of swallowing known as the Modified Barium Study (MBS) is the standard of care for assessing swallowing difficulty. While the clinical purpose of this radiographic imaging is to primarily assess aspiration risk, valuable biomechanical data is embedded in these studies. Computational analysis of swallowing mechanics (CASM) is an established research methodology for assessing multiple interactions of swallowing mechanics based on coordinates mapping muscle function including hyolaryngeal movement, pharyngeal shortening, tongue base retraction, and extension of the head and neck, however coordinates characterizing pharyngeal constriction is undeveloped. The aim of this study was to establish a method for locating the superior and middle pharyngeal constrictors using hard landmarks as guides on MBS videofluoroscopic imaging, and to test the reliability of this new method. Twenty de-identified, normal, MBS videos were randomly selected from a database. Two raters annotated landmarks for the superior and middle pharyngeal constrictors frame-by-frame using a semi-automated MATLAB tracker tool at two time points. Intraclass correlation coefficients were used to assess test-retest reliability between two raters with an ICC = 0.99 or greater for all coordinates for the retest measurement. MorphoJ integrated software was used to perform a discriminate function analysis to visualize how all 12 coordinates interact with each other in normal swallowing. The addition of the superior and middle pharyngeal constrictor coordinates to CASM allows for a robust analysis of the multiple components of swallowing mechanics interacting with a wide range of variables in both patient specific and cohort studies derived from common use imaging data.

A Novel Imaging Analysis Method for Capturing Pharyngeal Constriction During Swallowing

PubMed Central

Schwertner, Ryan W.; Garand, Kendrea L.; Pearson, William G.

2016-01-01

Videofluoroscopic imaging of swallowing known as the Modified Barium Study (MBS) is the standard of care for assessing swallowing difficulty. While the clinical purpose of this radiographic imaging is to primarily assess aspiration risk, valuable biomechanical data is embedded in these studies. Computational analysis of swallowing mechanics (CASM) is an established research methodology for assessing multiple interactions of swallowing mechanics based on coordinates mapping muscle function including hyolaryngeal movement, pharyngeal shortening, tongue base retraction, and extension of the head and neck, however coordinates characterizing pharyngeal constriction is undeveloped. The aim of this study was to establish a method for locating the superior and middle pharyngeal constrictors using hard landmarks as guides on MBS videofluoroscopic imaging, and to test the reliability of this new method. Twenty de-identified, normal, MBS videos were randomly selected from a database. Two raters annotated landmarks for the superior and middle pharyngeal constrictors frame-by-frame using a semi-automated MATLAB tracker tool at two time points. Intraclass correlation coefficients were used to assess test-retest reliability between two raters with an ICC = 0.99 or greater for all coordinates for the retest measurement. MorphoJ integrated software was used to perform a discriminate function analysis to visualize how all 12 coordinates interact with each other in normal swallowing. The addition of the superior and middle pharyngeal constrictor coordinates to CASM allows for a robust analysis of the multiple components of swallowing mechanics interacting with a wide range of variables in both patient specific and cohort studies derived from common use imaging data. PMID:28239682

Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

NASA Astrophysics Data System (ADS)

Kim, Dong-Youl; Lee, Jong-Hwan

2014-05-01

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

3D intrathoracic region definition and its application to PET-CT analysis

NASA Astrophysics Data System (ADS)

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W.; Higgins, William E.

2014-03-01

Recently developed integrated PET-CT scanners give co-registered multimodal data sets that offer complementary three-dimensional (3D) digital images of the chest. PET (positron emission tomography) imaging gives highly specific functional information of suspect cancer sites, while CT (X-ray computed tomography) gives associated anatomical detail. Because the 3D CT and PET scans generally span the body from the eyes to the knees, accurate definition of the intrathoracic region is vital for focusing attention to the central-chest region. In this way, diagnostically important regions of interest (ROIs), such as central-chest lymph nodes and cancer nodules, can be more efficiently isolated. We propose a method for automatic segmentation of the intrathoracic region from a given co-registered 3D PET-CT study. Using the 3D CT scan as input, the method begins by finding an initial intrathoracic region boundary for a given 2D CT section. Next, active contour analysis, driven by a cost function depending on local image gradient, gradient-direction, and contour shape features, iteratively estimates the contours spanning the intrathoracic region on neighboring 2D CT sections. This process continues until the complete region is defined. We next present an interactive system that employs the segmentation method for focused 3D PET-CT chest image analysis. A validation study over a series of PET-CT studies reveals that the segmentation method gives a Dice index accuracy of less than 98%. In addition, further results demonstrate the utility of the method for focused 3D PET-CT chest image analysis, ROI definition, and visualization.

Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients.

PubMed

Pizarro, Ricardo; Nair, Veena; Meier, Timothy; Holdsworth, Ryan; Tunnell, Evelyn; Rutecki, Paul; Sillay, Karl; Meyerand, Mary E; Prabhakaran, Vivek

2016-08-01

Seizure localization includes neuroimaging like electroencephalogram, and magnetic resonance imaging (MRI) with limited ability to characterize the epileptogenic network. Temporal clustering analysis (TCA) characterizes epileptogenic network congruent with interictal epileptiform discharges by clustering together voxels with transient signals. We generated epileptogenic areas for 12 of 13 epilepsy patients with TCA, congruent with different areas of seizure onset. Resting functional MRI (fMRI) scans are noninvasive, and can be acquired quickly, in patients with different levels of severity and function. Analyzing resting fMRI data using TCA is quick and can complement clinical methods to characterize the epileptogenic network.

Functional ultrasound imaging of intrinsic connectivity in the living rat brain with high spatiotemporal resolution

PubMed Central

Osmanski, Bruno-Félix; Pezet, Sophie; Ricobaraza, Ana; Lenkei, Zsolt; Tanter, Mickael

2014-01-01

Long-range coherences in spontaneous brain activity reflect functional connectivity. Here we propose a novel, highly resolved connectivity mapping approach, using ultrafast functional ultrasound (fUS), which enables imaging of cerebral microvascular haemodynamics deep in the anaesthetized rodent brain, through a large thinned-skull cranial window, with pixel dimensions of 100 μm × 100 μm in-plane. The millisecond-range temporal resolution allows unambiguous cancellation of low-frequency cardio-respiratory noise. Both seed-based and singular value decomposition analysis of spatial coherences in the low-frequency (<0.1 Hz) spontaneous fUS signal fluctuations reproducibly report, at different coronal planes, overlapping high-contrast, intrinsic functional connectivity patterns. These patterns are similar to major functional networks described in humans by resting-state fMRI, such as the lateral task-dependent network putatively anticorrelated with the midline default-mode network. These results introduce fUS as a powerful novel neuroimaging method, which could be extended to portable systems for three-dimensional functional connectivity imaging in awake and freely moving rodents. PMID:25277668

Error analysis of the Golay3 optical imaging system.

PubMed

Wu, Quanying; Fan, Junliu; Wu, Feng; Zhao, Jun; Qian, Lin

2013-05-01

We use aberration theory to derive a generalized pupil function of the Golay3 imaging system when astigmatisms exist in its submirrors. Theoretical analysis and numerical simulation using ZEMAX show that the point spread function (PSF) and the modulation transfer function (MTF) of the Golay3 sparse aperture system have a periodic change when there are piston errors. When the peak-valley value of the wavefront (PV(tilt)) due to the tilt error increases from zero to λ, the PSF and the MTF change significantly, and the change direction is determined by the location of the submirror with the tilt error. When PV(tilt) becomes larger than λ, the PSF and the MTF remain unvaried. We calculate the peaks of the signal-to-noise ratio (PSNR) resulting from the piston and tilt errors according to the Strehl ratio, and show that the PSNR decreases when the errors increase.

In Vivo Noninvasive Analysis of Human Forearm Muscle Function and Fatigue: Applications to EVA Operations and Training Maneuvers

NASA Technical Reports Server (NTRS)

Fotedar, L. K.; Marshburn, T.; Quast, M. J.; Feeback, D. L.

1999-01-01

Forearm muscle fatigue is one of the major limiting factors affecting endurance during performance of deep-space extravehicular activity (EVA) by crew members. Magnetic resonance (MR) provides in vivo noninvasive analysis of tissue level metabolism and fluid exchange dynamics in exercised forearm muscles through the monitoring of proton magnetic resonance imaging (MRI) and phosphorus magnetic resonance spectroscopy (P-31-MRS) parameter variations. Using a space glove box and EVA simulation protocols, we conducted a preliminary MRS/MRI study in a small group of human test subjects during submaximal exercise and recovery and following exhaustive exercise. In assessing simulated EVA-related muscle fatigue and function, this pilot study revealed substantial changes in the MR image longitudinal relaxation times (T2) as an indicator of specific muscle activation and proton flux as well as changes in spectral phosphocreatine-to-phosphate (PCr/Pi) levels as a function of tissue bioenergetic potential.

Error of the slanted edge method for measuring the modulation transfer function of imaging systems.

PubMed

Xie, Xufen; Fan, Hongda; Wang, Hongyuan; Wang, Zebin; Zou, Nianyu

2018-03-01

The slanted edge method is a basic approach for measuring the modulation transfer function (MTF) of imaging systems; however, its measurement accuracy is limited in practice. Theoretical analysis of the slanted edge MTF measurement method performed in this paper reveals that inappropriate edge angles and random noise reduce this accuracy. The error caused by edge angles is analyzed using sampling and reconstruction theory. Furthermore, an error model combining noise and edge angles is proposed. We verify the analyses and model with respect to (i) the edge angle, (ii) a statistical analysis of the measurement error, (iii) the full width at half-maximum of a point spread function, and (iv) the error model. The experimental results verify the theoretical findings. This research can be referential for applications of the slanted edge MTF measurement method.

Dynamic physiological modeling for functional diffuse optical tomography

PubMed Central

Diamond, Solomon Gilbert; Huppert, Theodore J.; Kolehmainen, Ville; Franceschini, Maria Angela; Kaipio, Jari P.; Arridge, Simon R.; Boas, David A.

2009-01-01

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n = 10, P < 0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis. PMID:16242967

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

New approaches in renal microscopy: volumetric imaging and superresolution microscopy.

PubMed

Kim, Alfred H J; Suleiman, Hani; Shaw, Andrey S

2016-05-01

Histologic and electron microscopic analysis of the kidney has provided tremendous insight into structures such as the glomerulus and nephron. Recent advances in imaging, such as deep volumetric approaches and superresolution microscopy, have the capacity to dramatically enhance our current understanding of the structure and function of the kidney. Volumetric imaging can generate images millimeters below the surface of the intact kidney. Superresolution microscopy breaks the diffraction barrier inherent in traditional light microscopy, enabling the visualization of fine structures. Here, we describe new approaches to deep volumetric and superresolution microscopy of the kidney. Rapid advances in lasers, microscopic objectives, and tissue preparation have transformed our ability to deep volumetric image the kidney. Innovations in sample preparation have allowed for superresolution imaging with electron microscopy correlation, providing unprecedented insight into the structures within the glomerulus. Technological advances in imaging have revolutionized our capacity to image both large volumes of tissue and the finest structural details of a cell. These new advances have the potential to provide additional profound observations into the normal and pathologic functions of the kidney.

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.

Display management subsystem, version 1: A user's eye view

NASA Technical Reports Server (NTRS)

Parker, Dolores

1986-01-01

The structure and application functions of the Display Management Subsystem (DMS) are described. The DMS, a subsystem of the Transportable Applications Executive (TAE), was designed to provide a device-independent interface for an image processing and display environment. The system is callable by C and FORTRAN applications, portable to accommodate different image analysis terminals, and easily expandable to meet local needs. Generic applications are also available for performing many image processing tasks.

Model-Based Segmentation of Cortical Regions of Interest for Multi-subject Analysis of fMRI Data

NASA Astrophysics Data System (ADS)

Engel, Karin; Brechmann, Andr'e.; Toennies, Klaus

The high inter-subject variability of human neuroanatomy complicates the analysis of functional imaging data across subjects. We propose a method for the correct segmentation of cortical regions of interest based on the cortical surface. First results on the segmentation of Heschl's gyrus indicate the capability of our approach for correct comparison of functional activations in relation to individual cortical patterns.

Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas.

PubMed

Nguyen, T B; Cron, G O; Perdrizet, K; Bezzina, K; Torres, C H; Chakraborty, S; Woulfe, J; Jansen, G H; Sinclair, J; Thornhill, R E; Foottit, C; Zanette, B; Cameron, I G

2015-11-01

Dynamic contrast-enhanced MR imaging parameters can be biased by poor measurement of the vascular input function. We have compared the diagnostic accuracy of dynamic contrast-enhanced MR imaging by using a phase-derived vascular input function and "bookend" T1 measurements with DSC MR imaging for preoperative grading of astrocytomas. This prospective study included 48 patients with a new pathologic diagnosis of an astrocytoma. Preoperative MR imaging was performed at 3T, which included 2 injections of 5-mL gadobutrol for dynamic contrast-enhanced and DSC MR imaging. During dynamic contrast-enhanced MR imaging, both magnitude and phase images were acquired to estimate plasma volume obtained from phase-derived vascular input function (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function (K(trans)_Φ) as well as plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K(trans)_SI). From DSC MR imaging, corrected relative CBV was computed. Four ROIs were placed over the solid part of the tumor, and the highest value among the ROIs was recorded. A Mann-Whitney U test was used to test for difference between grades. Diagnostic accuracy was assessed by using receiver operating characteristic analysis. Vp_ Φ and K(trans)_Φ values were lower for grade II compared with grade III astrocytomas (P < .05). Vp_SI and K(trans)_SI were not significantly different between grade II and grade III astrocytomas (P = .08-0.15). Relative CBV and dynamic contrast-enhanced MR imaging parameters except for K(trans)_SI were lower for grade III compared with grade IV (P ≤ .05). In differentiating low- and high-grade astrocytomas, we found no statistically significant difference in diagnostic accuracy between relative CBV and dynamic contrast-enhanced MR imaging parameters. In the preoperative grading of astrocytomas, the diagnostic accuracy of dynamic contrast-enhanced MR imaging parameters is similar to that of relative CBV. © 2015 by American Journal of Neuroradiology.

Analysis of photographic X-ray images. [S-054 telescope on Skylab

NASA Technical Reports Server (NTRS)

Krieger, A. S.

1977-01-01

Some techniques used to extract quantitative data from the information contained in photographic images produced by grazing incidence soft X-ray optical systems are described. The discussion is focussed on the analysis of the data returned by the S-054 X-Ray Spectrographic Telescope Experiment on Skylab. The parameters of the instrument and the procedures used for its calibration are described. The technique used to convert photographic density to focal plane X-ray irradiance is outlined. The deconvolution of the telescope point response function from the image data is discussed. Methods of estimating the temperature, pressure, and number density of coronal plasmas are outlined.

Analysis of Orientations of Collagen Fibers by Novel Fiber-Tracking Software

NASA Astrophysics Data System (ADS)

Wu, Jun; Rajwa, Bartlomiej; Filmer, David L.; Hoffmann, Christoph M.; Yuan, Bo; Chiang, Ching-Shoei; Sturgis, Jennie; Robinson, J. Paul

2003-12-01

Recent evidence supports the notion that biological functions of extracellular matrix (ECM) are highly correlated to not only its composition but also its structure. This article integrates confocal microscopy imaging and image-processing techniques to analyze the microstructural properties of ECM. This report describes a two- and three-dimensional fiber middle-line tracing algorithm that may be used to quantify collagen fibril organization. We utilized computer simulation and statistical analysis to validate the developed algorithm. These algorithms were applied to confocal images of collagen gels made with reconstituted bovine collagen type I, to demonstrate the computation of orientations of individual fibers.

Imaging plant leaves to determine changes in radioactive contamination status in Fukushima, Japan.

PubMed

Nakajima, Hiroo; Fujiwara, Mamoru; Tanihata, Isao; Saito, Tadashi; Matsuda, Norihiro; Todo, Takeshi

2014-05-01

The chemical composition of plant leaves often reflects environmental contamination. The authors analyzed images of plant leaves to investigate the regional radioactivity ecology resulting from the 2011 accident at the Fukushima No. 1 nuclear power plant, Japan. The present study is not an evaluation of the macro radiation dose per weight, which has been performed previously, but rather an image analysis of the radioactive dose per leaf, allowing the capture of various gradual changes in radioactive contamination as a function of elapsed time. In addition, the leaf analysis method has potential applications in the decontamination of food plants or other materials.

Ultrafast Method for the Analysis of Fluorescence Lifetime Imaging Microscopy Data Based on the Laguerre Expansion Technique

PubMed Central

Jo, Javier A.; Fang, Qiyin; Marcu, Laura

2007-01-01

We report a new deconvolution method for fluorescence lifetime imaging microscopy (FLIM) based on the Laguerre expansion technique. The performance of this method was tested on synthetic and real FLIM images. The following interesting properties of this technique were demonstrated. 1) The fluorescence intensity decay can be estimated simultaneously for all pixels, without a priori assumption of the decay functional form. 2) The computation speed is extremely fast, performing at least two orders of magnitude faster than current algorithms. 3) The estimated maps of Laguerre expansion coefficients provide a new domain for representing FLIM information. 4) The number of images required for the analysis is relatively small, allowing reduction of the acquisition time. These findings indicate that the developed Laguerre expansion technique for FLIM analysis represents a robust and extremely fast deconvolution method that enables practical applications of FLIM in medicine, biology, biochemistry, and chemistry. PMID:19444338

Functional optical coherence tomography: principles and progress

NASA Astrophysics Data System (ADS)

Kim, Jina; Brown, William; Maher, Jason R.; Levinson, Howard; Wax, Adam

2015-05-01

In the past decade, several functional extensions of optical coherence tomography (OCT) have emerged, and this review highlights key advances in instrumentation, theoretical analysis, signal processing and clinical application of these extensions. We review five principal extensions: Doppler OCT (DOCT), polarization-sensitive OCT (PS-OCT), optical coherence elastography (OCE), spectroscopic OCT (SOCT), and molecular imaging OCT. The former three have been further developed with studies in both ex vivo and in vivo human tissues. This review emphasizes the newer techniques of SOCT and molecular imaging OCT, which show excellent potential for clinical application but have yet to be well reviewed in the literature. SOCT elucidates tissue characteristics, such as oxygenation and carcinogenesis, by detecting wavelength-dependent absorption and scattering of light in tissues. While SOCT measures endogenous biochemical distributions, molecular imaging OCT detects exogenous molecular contrast agents. These newer advances in functional OCT broaden the potential clinical application of OCT by providing novel ways to understand tissue activity that cannot be accomplished by other current imaging methodologies.

Functional Optical Coherence Tomography: Principles and Progress

PubMed Central

Kim, Jina; Brown, William; Maher, Jason R.; Levinson, Howard; Wax, Adam

2015-01-01

In the past decade, several functional extensions of optical coherence tomography (OCT) have emerged, and this review highlights key advances in instrumentation, theoretical analysis, signal processing and clinical application of these extensions. We review five principal extensions: Doppler OCT (DOCT), polarization-sensitive OCT (PS-OCT), optical coherence elastography (OCE), spectroscopic OCT (SOCT), and molecular imaging OCT. The former three have been further developed with studies in both ex vivo and in vivo human tissues. This review emphasizes the newer techniques of SOCT and molecular imaging OCT, which show excellent potential for clinical application but have yet to be well reviewed in the literature. SOCT elucidates tissue characteristics, such as oxygenation and carcinogenesis, by detecting wavelength-dependent absorption and scattering of light in tissues. While SOCT measures endogenous biochemical distributions, molecular imaging OCT detects exogenous molecular contrast agents. These newer advances in functional OCT broaden the potential clinical application of OCT by providing novel ways to understand tissue activity that cannot be accomplished by other current imaging methodologies. PMID:25951836

ANALYTIC MODELING OF STARSHADES

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

Cash, Webster

2011-09-01

External occulters, otherwise known as starshades, have been proposed as a solution to one of the highest priority yet technically vexing problems facing astrophysics-the direct imaging and characterization of terrestrial planets around other stars. New apodization functions, developed over the past few years, now enable starshades of just a few tens of meters diameter to occult central stars so efficiently that the orbiting exoplanets can be revealed and other high-contrast imaging challenges addressed. In this paper, an analytic approach to the analysis of these apodization functions is presented. It is used to develop a tolerance analysis suitable for use inmore » designing practical starshades. The results provide a mathematical basis for understanding starshades and a quantitative approach to setting tolerances.« less

Effect of Chinese Herbal Medicine on Molecular Imaging of Neurological Disorders.

PubMed

Yao, Yao; Chen, Ting; Huang, Jing; Zhang, Hong; Tian, Mei

2017-01-01

Chinese herbal medicine has been used to treat a wide variety of neurological disorders including stroke, Alzheimer's disease, and Parkinson's disease. However, its mechanism behind the effectiveness remains unclear. Recently, molecular imaging technology has been applied for this purpose, since it can assess the cellular or molecular function in a living subject by using specific imaging probes and/or radioactive tracers, which enable efficient analysis and monitoring the therapeutic response repetitively. This chapter reviews the in vivo functional and metabolic changes after administration of Chinese herbal medicine in various neurological disorders and provides perspectives on the future evaluations of therapeutic response of Chinese herbal medicine. © 2017 Elsevier Inc. All rights reserved.

Detection of soft-tissue sarcoma recurrence: added value of functional MR imaging techniques at 3.0 T.

PubMed

Del Grande, Filippo; Subhawong, Ty; Weber, Kristy; Aro, Michael; Mugera, Charles; Fayad, Laura M

2014-05-01

To determine the added value of functional magnetic resonance (MR) sequences (dynamic contrast material-enhanced [DCE] and quantitative diffusion-weighted [DW] imaging with apparent diffusion coefficient [ADC] mapping) for the detection of recurrent soft-tissue sarcomas following surgical resection. This retrospective study was approved by the institutional review board. The requirement to obtain informed consent was waived. Thirty-seven patients referred for postoperative surveillance after resection of soft-tissue sarcoma (35 with high-grade sarcoma) were studied. Imaging at 3.0 T included conventional (T1-weighted, fluid-sensitive, and contrast-enhanced T1-weighted imaging) and functional (DCE MR imaging, DW imaging with ADC mapping) sequences. Recurrences were confirmed with biopsy or resection. A disease-free state was determined with at least 6 months of follow-up. Two readers independently recorded the signal and morphologic characteristics with conventional sequences, the presence or absence of arterial enhancement at DCE MR imaging, and ADCs of the surgical bed. The accuracy of conventional MR imaging in the detection of recurrence was compared with that with the addition of functional sequences. The Fisher exact and Wilcoxon rank sum tests were used to define the accuracy of imaging features, the Cohen κ and Lin interclass correlation were used to define interobserver variability, and receiver operating characteristic analysis was used to define a threshold to detect recurrence and assess reader confidence after the addition of functional imaging to conventional sequences. There were six histologically proved recurrences in 37 patients. Sensitivity and specificity of MR imaging in the detection of tumor recurrence were 100% (six of six patients) and 52% (16 of 31 patients), respectively, with conventional sequences, 100% (six of six patients) and 97% (30 of 31 patients) with the addition of DCE MR imaging, and 60% (three of five patients) and 97% (30 of 31 patients) with the addition of DW imaging and ADC mapping. The average ADC of recurrence (1.08 mm(2)/sec ± 0.19) was significantly different from those of postoperative scarring (0.9 mm(2)/sec ± 0.00) and hematomas (2.34 mm(2)/sec ± 0.72) (P = .03 for both). The addition of functional MR sequences to a routine MR protocol, in particular DCE MR imaging, offers a specificity of more than 95% for distinguishing recurrent sarcoma from postsurgical scarring.

FADTTSter: accelerating hypothesis testing with functional analysis of diffusion tensor tract statistics

NASA Astrophysics Data System (ADS)

Noel, Jean; Prieto, Juan C.; Styner, Martin

2017-03-01

Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) is a toolbox for analysis of white matter (WM) fiber tracts. It allows associating diffusion properties along major WM bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these WM tract properties. However, to use this toolbox, a user must have an intermediate knowledge in scripting languages (MATLAB). FADTTSter was created to overcome this issue and make the statistical analysis accessible to any non-technical researcher. FADTTSter is actively being used by researchers at the University of North Carolina. FADTTSter guides non-technical users through a series of steps including quality control of subjects and fibers in order to setup the necessary parameters to run FADTTS. Additionally, FADTTSter implements interactive charts for FADTTS' outputs. This interactive chart enhances the researcher experience and facilitates the analysis of the results. FADTTSter's motivation is to improve usability and provide a new analysis tool to the community that complements FADTTS. Ultimately, by enabling FADTTS to a broader audience, FADTTSter seeks to accelerate hypothesis testing in neuroimaging studies involving heterogeneous clinical data and diffusion tensor imaging. This work is submitted to the Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC.

Processing Cones: A Computational Structure for Image Analysis.

DTIC Science & Technology

1981-12-01

image analysis applications, referred to as a processing cone, is described and sample algorithms are presented. A fundamental characteristic of the structure is its hierarchical organization into two-dimensional arrays of decreasing resolution. In this architecture, a protypical function is defined on a local window of data and applied uniformly to all windows in a parallel manner. Three basic modes of processing are supported in the cone: reduction operations (upward processing), horizontal operations (processing at a single level) and projection operations (downward

Early Detection of Amyloid Plaque in Alzheimer’s Disease via X-Ray Phase CT

DTIC Science & Technology

2013-06-01

fibrils in the x-ray phase contrast CT imaging, as a function over the molar concentrations corresponding to normal, pathologic and Alzheimer’s...panel imagers and the artifact removal using a wavelet -analysis-based algorithm” Med. Phys., 28(3): 812-25, 2001. 4. X Wu and H Liu, “Clinical...and the artifact removal using a wavelet -analysis-based algorithm” Med. Phys., 28(3): 812-25, 2001 12. Tang X, Hsieh J, Nilsen RA, Hagiwara A

Benchmarking the performance of fixed-image receptor digital radiographic systems part 1: a novel method for image quality analysis.

PubMed

Lee, Kam L; Ireland, Timothy A; Bernardo, Michael

2016-06-01

This is the first part of a two-part study in benchmarking the performance of fixed digital radiographic general X-ray systems. This paper concentrates on reporting findings related to quantitative analysis techniques used to establish comparative image quality metrics. A systematic technical comparison of the evaluated systems is presented in part two of this study. A novel quantitative image quality analysis method is presented with technical considerations addressed for peer review. The novel method was applied to seven general radiographic systems with four different makes of radiographic image receptor (12 image receptors in total). For the System Modulation Transfer Function (sMTF), the use of grid was found to reduce veiling glare and decrease roll-off. The major contributor in sMTF degradation was found to be focal spot blurring. For the System Normalised Noise Power Spectrum (sNNPS), it was found that all systems examined had similar sNNPS responses. A mathematical model is presented to explain how the use of stationary grid may cause a difference between horizontal and vertical sNNPS responses.

High resolution image processing on low-cost microcomputers

NASA Technical Reports Server (NTRS)

Miller, R. L.

1993-01-01

Recent advances in microcomputer technology have resulted in systems that rival the speed, storage, and display capabilities of traditionally larger machines. Low-cost microcomputers can provide a powerful environment for image processing. A new software program which offers sophisticated image display and analysis on IBM-based systems is presented. Designed specifically for a microcomputer, this program provides a wide-range of functions normally found only on dedicated graphics systems, and therefore can provide most students, universities and research groups with an affordable computer platform for processing digital images. The processing of AVHRR images within this environment is presented as an example.

A system for the real-time display of radar and video images of targets

NASA Technical Reports Server (NTRS)

Allen, W. W.; Burnside, W. D.

1990-01-01

Described here is a software and hardware system for the real-time display of radar and video images for use in a measurement range. The main purpose is to give the reader a clear idea of the software and hardware design and its functions. This system is designed around a Tektronix XD88-30 graphics workstation, used to display radar images superimposed on video images of the actual target. The system's purpose is to provide a platform for tha analysis and documentation of radar images and their associated targets in a menu-driven, user oriented environment.

A simulator for evaluating methods for the detection of lesion-deficit associations

NASA Technical Reports Server (NTRS)

Megalooikonomou, V.; Davatzikos, C.; Herskovits, E. H.

2000-01-01

Although much has been learned about the functional organization of the human brain through lesion-deficit analysis, the variety of statistical and image-processing methods developed for this purpose precludes a closed-form analysis of the statistical power of these systems. Therefore, we developed a lesion-deficit simulator (LDS), which generates artificial subjects, each of which consists of a set of functional deficits, and a brain image with lesions; the deficits and lesions conform to predefined distributions. We used probability distributions to model the number, sizes, and spatial distribution of lesions, to model the structure-function associations, and to model registration error. We used the LDS to evaluate, as examples, the effects of the complexities and strengths of lesion-deficit associations, and of registration error, on the power of lesion-deficit analysis. We measured the numbers of recovered associations from these simulated data, as a function of the number of subjects analyzed, the strengths and number of associations in the statistical model, the number of structures associated with a particular function, and the prior probabilities of structures being abnormal. The number of subjects required to recover the simulated lesion-deficit associations was found to have an inverse relationship to the strength of associations, and to the smallest probability in the structure-function model. The number of structures associated with a particular function (i.e., the complexity of associations) had a much greater effect on the performance of the analysis method than did the total number of associations. We also found that registration error of 5 mm or less reduces the number of associations discovered by approximately 13% compared to perfect registration. The LDS provides a flexible framework for evaluating many aspects of lesion-deficit analysis.

GeoPAT: A toolbox for pattern-based information retrieval from large geospatial databases

NASA Astrophysics Data System (ADS)

Jasiewicz, Jarosław; Netzel, Paweł; Stepinski, Tomasz

2015-07-01

Geospatial Pattern Analysis Toolbox (GeoPAT) is a collection of GRASS GIS modules for carrying out pattern-based geospatial analysis of images and other spatial datasets. The need for pattern-based analysis arises when images/rasters contain rich spatial information either because of their very high resolution or their very large spatial extent. Elementary units of pattern-based analysis are scenes - patches of surface consisting of a complex arrangement of individual pixels (patterns). GeoPAT modules implement popular GIS algorithms, such as query, overlay, and segmentation, to operate on the grid of scenes. To achieve these capabilities GeoPAT includes a library of scene signatures - compact numerical descriptors of patterns, and a library of distance functions - providing numerical means of assessing dissimilarity between scenes. Ancillary GeoPAT modules use these functions to construct a grid of scenes or to assign signatures to individual scenes having regular or irregular geometries. Thus GeoPAT combines knowledge retrieval from patterns with mapping tasks within a single integrated GIS environment. GeoPAT is designed to identify and analyze complex, highly generalized classes in spatial datasets. Examples include distinguishing between different styles of urban settlements using VHR images, delineating different landscape types in land cover maps, and mapping physiographic units from DEM. The concept of pattern-based spatial analysis is explained and the roles of all modules and functions are described. A case study example pertaining to delineation of landscape types in a subregion of NLCD is given. Performance evaluation is included to highlight GeoPAT's applicability to very large datasets. The GeoPAT toolbox is available for download from

Imaging deductive reasoning and the new paradigm

PubMed Central

Oaksford, Mike

2015-01-01

There has been a great expansion of research into human reasoning at all of Marr’s explanatory levels. There is a tendency for this work to progress within a level largely ignoring the others which can lead to slippage between levels (Chater et al., 2003). It is argued that recent brain imaging research on deductive reasoning—implementational level—has largely ignored the new paradigm in reasoning—computational level (Over, 2009). Consequently, recent imaging results are reviewed with the focus on how they relate to the new paradigm. The imaging results are drawn primarily from a recent meta-analysis by Prado et al. (2011) but further imaging results are also reviewed where relevant. Three main observations are made. First, the main function of the core brain region identified is most likely elaborative, defeasible reasoning not deductive reasoning. Second, the subtraction methodology and the meta-analytic approach may remove all traces of content specific System 1 processes thought to underpin much human reasoning. Third, interpreting the function of the brain regions activated by a task depends on theories of the function that a task engages. When there are multiple interpretations of that function, interpreting what an active brain region is doing is not clear cut. It is concluded that there is a need to more tightly connect brain activation to function, which could be achieved using formalized computational level models and a parametric variation approach. PMID:25774130

Fluid Registration of Diffusion Tensor Images Using Information Theory

PubMed Central

Chiang, Ming-Chang; Leow, Alex D.; Klunder, Andrea D.; Dutton, Rebecca A.; Barysheva, Marina; Rose, Stephen E.; McMahon, Katie L.; de Zubicaray, Greig I.; Toga, Arthur W.; Thompson, Paul M.

2008-01-01

We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or J-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the J-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data. PMID:18390342

Robust w-Estimators for Cryo-EM Class Means

PubMed Central

Huang, Chenxi; Tagare, Hemant D.

2016-01-01

A critical step in cryogenic electron microscopy (cryo-EM) image analysis is to calculate the average of all images aligned to a projection direction. This average, called the “class mean”, improves the signal-to-noise ratio in single particle reconstruction (SPR). The averaging step is often compromised because of outlier images of ice, contaminants, and particle fragments. Outlier detection and rejection in the majority of current cryo-EM methods is done using cross-correlation with a manually determined threshold. Empirical assessment shows that the performance of these methods is very sensitive to the threshold. This paper proposes an alternative: a “w-estimator” of the average image, which is robust to outliers and which does not use a threshold. Various properties of the estimator, such as consistency and influence function are investigated. An extension of the estimator to images with different contrast transfer functions (CTFs) is also provided. Experiments with simulated and real cryo-EM images show that the proposed estimator performs quite well in the presence of outliers. PMID:26841397

Robust w-Estimators for Cryo-EM Class Means.

PubMed

Huang, Chenxi; Tagare, Hemant D

2016-02-01

A critical step in cryogenic electron microscopy (cryo-EM) image analysis is to calculate the average of all images aligned to a projection direction. This average, called the class mean, improves the signal-to-noise ratio in single-particle reconstruction. The averaging step is often compromised because of the outlier images of ice, contaminants, and particle fragments. Outlier detection and rejection in the majority of current cryo-EM methods are done using cross-correlation with a manually determined threshold. Empirical assessment shows that the performance of these methods is very sensitive to the threshold. This paper proposes an alternative: a w-estimator of the average image, which is robust to outliers and which does not use a threshold. Various properties of the estimator, such as consistency and influence function are investigated. An extension of the estimator to images with different contrast transfer functions is also provided. Experiments with simulated and real cryo-EM images show that the proposed estimator performs quite well in the presence of outliers.

A workflow for the automatic segmentation of organelles in electron microscopy image stacks

PubMed Central

Perez, Alex J.; Seyedhosseini, Mojtaba; Deerinck, Thomas J.; Bushong, Eric A.; Panda, Satchidananda; Tasdizen, Tolga; Ellisman, Mark H.

2014-01-01

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime. PMID:25426032

Accurate computer-aided quantification of left ventricular parameters: experience in 1555 cardiac magnetic resonance studies from the Framingham Heart Study.

PubMed

Hautvast, Gilion L T F; Salton, Carol J; Chuang, Michael L; Breeuwer, Marcel; O'Donnell, Christopher J; Manning, Warren J

2012-05-01

Quantitative analysis of short-axis functional cardiac magnetic resonance images can be performed using automatic contour detection methods. The resulting myocardial contours must be reviewed and possibly corrected, which can be time-consuming, particularly when performed across all cardiac phases. We quantified the impact of manual contour corrections on both analysis time and quantitative measurements obtained from left ventricular short-axis cine images acquired from 1555 participants of the Framingham Heart Study Offspring cohort using computer-aided contour detection methods. The total analysis time for a single case was 7.6 ± 1.7 min for an average of 221 ± 36 myocardial contours per participant. This included 4.8 ± 1.6 min for manual contour correction of 2% of all automatically detected endocardial contours and 8% of all automatically detected epicardial contours. However, the impact of these corrections on global left ventricular parameters was limited, introducing differences of 0.4 ± 4.1 mL for end-diastolic volume, -0.3 ± 2.9 mL for end-systolic volume, 0.7 ± 3.1 mL for stroke volume, and 0.3 ± 1.8% for ejection fraction. We conclude that left ventricular functional parameters can be obtained under 5 min from short-axis functional cardiac magnetic resonance images using automatic contour detection methods. Manual correction more than doubles analysis time, with minimal impact on left ventricular volumes and ejection fraction. Copyright © 2011 Wiley Periodicals, Inc.

Analyzing microtomography data with Python and the scikit-image library.

PubMed

Gouillart, Emmanuelle; Nunez-Iglesias, Juan; van der Walt, Stéfan

2017-01-01

The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit-image is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. scikit-image users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. scikit-image combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy.

PubMed

Jesse, Stephen; Kalinin, Sergei V

2009-02-25

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

Novel Approach to Three-Dimensional Echocardiographic Quantification of Right Ventricular Volumes and Function from Focused Views.

PubMed

Medvedofsky, Diego; Addetia, Karima; Patel, Amit R; Sedlmeier, Anke; Baumann, Rolf; Mor-Avi, Victor; Lang, Roberto M

2015-10-01

Echocardiographic assessment of the right ventricle is difficult because of its complex shape. Three-dimensional echocardiographic (3DE) imaging allows more accurate and reproducible analysis of the right ventricle than two-dimensional methodology. However, three-dimensional volumetric analysis has been hampered by difficulties obtaining consistently high-quality coronal views, required by the existing software packages. The aim of this study was to test a new approach for volumetric analysis without coronal views by using instead right ventricle-focused three-dimensional acquisition with multiple short-axis views extracted from the same data set. Transthoracic 3DE and cardiovascular magnetic resonance (CMR) images were prospectively obtained on the same day in 147 patients with wide ranges of right ventricular (RV) size and function. RV volumes and ejection fraction were measured from 3DE images using the new software and compared with CMR reference values. Comparisons included linear regression and Bland-Altman analyses. Repeated measurements were performed to assess measurement variability. Sixteen patients were excluded because of suboptimal image quality (89% feasibility). RV volumes and ejection fraction obtained with the new 3DE technique were in good agreement with CMR (end-diastolic volume, r = 0.95; end-systolic volume, r = 0.96; ejection fraction, r = 0.83). Biases were, respectively, -6 ± 11%, 0 ± 15%, and -7 ± 17% of the mean measured values. In a subset of patients with suboptimal 3DE images, the new analysis resulted in significantly improved accuracy against CMR and reproducibility, compared with previously used coronal view-based techniques. The time required for the 3DE analysis was approximately 4 min. The new software is fast, reproducible, and accurate compared with CMR over a wide range of RV size and function. Because right ventricle-focused 3DE acquisition is feasible in most patients, this approach may be applicable to a broader population of patients who can benefit from RV volumetric assessment. Copyright © 2015 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

A general framework for regularized, similarity-based image restoration.

PubMed

Kheradmand, Amin; Milanfar, Peyman

2014-12-01

Any image can be represented as a function defined on a weighted graph, in which the underlying structure of the image is encoded in kernel similarity and associated Laplacian matrices. In this paper, we develop an iterative graph-based framework for image restoration based on a new definition of the normalized graph Laplacian. We propose a cost function, which consists of a new data fidelity term and regularization term derived from the specific definition of the normalized graph Laplacian. The normalizing coefficients used in the definition of the Laplacian and associated regularization term are obtained using fast symmetry preserving matrix balancing. This results in some desired spectral properties for the normalized Laplacian such as being symmetric, positive semidefinite, and returning zero vector when applied to a constant image. Our algorithm comprises of outer and inner iterations, where in each outer iteration, the similarity weights are recomputed using the previous estimate and the updated objective function is minimized using inner conjugate gradient iterations. This procedure improves the performance of the algorithm for image deblurring, where we do not have access to a good initial estimate of the underlying image. In addition, the specific form of the cost function allows us to render the spectral analysis for the solutions of the corresponding linear equations. In addition, the proposed approach is general in the sense that we have shown its effectiveness for different restoration problems, including deblurring, denoising, and sharpening. Experimental results verify the effectiveness of the proposed algorithm on both synthetic and real examples.

On-line 3-dimensional confocal imaging in vivo.

PubMed

Li, J; Jester, J V; Cavanagh, H D; Black, T D; Petroll, W M

2000-09-01

In vivo confocal microscopy through focusing (CMTF) can provide a 3-D stack of high-resolution corneal images and allows objective measurements of corneal sublayer thickness and backscattering. However, current systems require time-consuming off-line image processing and analysis on multiple software platforms. Furthermore, there is a trade off between the CMTF speed and measurement precision. The purpose of this study was to develop a novel on-line system for in vivo corneal imaging and analysis that overcomes these limitations. A tandem scanning confocal microscope (TSCM) was used for corneal imaging. The TSCM video camera was interfaced directly to a PC image acquisition board to implement real-time digitization. Software was developed to allow in vivo 2-D imaging, CMTF image acquisition, interactive 3-D reconstruction, and analysis of CMTF data to be performed on line in a single user-friendly environment. A procedure was also incorporated to separate the odd/even video fields, thereby doubling the CMTF sampling rate and theoretically improving the precision of CMTF thickness measurements by a factor of two. In vivo corneal examinations of a normal human and a photorefractive keratectomy patient are presented to demonstrate the capabilities of the new system. Improvements in the convenience, speed, and functionality of in vivo CMTF image acquisition, display, and analysis are demonstrated. This is the first full-featured software package designed for in vivo TSCM imaging of the cornea, which performs both 2-D and 3-D image acquisition, display, and processing as well as CMTF analysis. The use of a PC platform and incorporation of easy to use, on line, and interactive features should help to improve the clinical utility of this technology.

Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation.

PubMed

Asner, G P; Martin, R E; Knapp, D E; Tupayachi, R; Anderson, C B; Sinca, F; Vaughn, N R; Llactayo, W

2017-01-27

Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change. Copyright © 2017, American Association for the Advancement of Science.

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

PubMed Central

Hu, Mao-Gui; Wang, Jin-Feng; Ge, Yong

2009-01-01

Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intra-urban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolution-enhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well in detail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics. PMID:22291530

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research

PubMed Central

Goscinski, Wojtek J.; McIntosh, Paul; Felzmann, Ulrich; Maksimenko, Anton; Hall, Christopher J.; Gureyev, Timur; Thompson, Darren; Janke, Andrew; Galloway, Graham; Killeen, Neil E. B.; Raniga, Parnesh; Kaluza, Owen; Ng, Amanda; Poudel, Govinda; Barnes, David G.; Nguyen, Toan; Bonnington, Paul; Egan, Gary F.

2014-01-01

The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) is a national imaging and visualization facility established by Monash University, the Australian Synchrotron, the Commonwealth Scientific Industrial Research Organization (CSIRO), and the Victorian Partnership for Advanced Computing (VPAC), with funding from the National Computational Infrastructure and the Victorian Government. The MASSIVE facility provides hardware, software, and expertise to drive research in the biomedical sciences, particularly advanced brain imaging research using synchrotron x-ray and infrared imaging, functional and structural magnetic resonance imaging (MRI), x-ray computer tomography (CT), electron microscopy and optical microscopy. The development of MASSIVE has been based on best practice in system integration methodologies, frameworks, and architectures. The facility has: (i) integrated multiple different neuroimaging analysis software components, (ii) enabled cross-platform and cross-modality integration of neuroinformatics tools, and (iii) brought together neuroimaging databases and analysis workflows. MASSIVE is now operational as a nationally distributed and integrated facility for neuroinfomatics and brain imaging research. PMID:24734019

Regional Lung Ventilation Analysis Using Temporally Resolved Magnetic Resonance Imaging.

PubMed

Kolb, Christoph; Wetscherek, Andreas; Buzan, Maria Teodora; Werner, René; Rank, Christopher M; Kachelrie, Marc; Kreuter, Michael; Dinkel, Julien; Heuel, Claus Peter; Maier-Hein, Klaus

We propose a computer-aided method for regional ventilation analysis and observation of lung diseases in temporally resolved magnetic resonance imaging (4D MRI). A shape model-based segmentation and registration workflow was used to create an atlas-derived reference system in which regional tissue motion can be quantified and multimodal image data can be compared regionally. Model-based temporal registration of the lung surfaces in 4D MRI data was compared with the registration of 4D computed tomography (CT) images. A ventilation analysis was performed on 4D MR images of patients with lung fibrosis; 4D MR ventilation maps were compared with corresponding diagnostic 3D CT images of the patients and 4D CT maps of subjects without impaired lung function (serving as reference). Comparison between the computed patient-specific 4D MR regional ventilation maps and diagnostic CT images shows good correlation in conspicuous regions. Comparison to 4D CT-derived ventilation maps supports the plausibility of the 4D MR maps. Dynamic MRI-based flow-volume loops and spirograms further visualize the free-breathing behavior. The proposed methods allow for 4D MR-based regional analysis of tissue dynamics and ventilation in spontaneous breathing and comparison of patient data. The proposed atlas-based reference coordinate system provides an automated manner of annotating and comparing multimodal lung image data.

Consumer opinion on social policy approaches to promoting positive body image: Airbrushed media images and disclaimer labels.

PubMed

Paraskeva, Nicole; Lewis-Smith, Helena; Diedrichs, Phillippa C

2017-02-01

Disclaimer labels on airbrushed media images have generated political attention and advocacy as a social policy approach to promoting positive body image. Experimental research suggests that labelling is ineffective and consumers' viewpoints have been overlooked. A mixed-method study explored British consumers' ( N = 1555, aged 11-78 years) opinions on body image and social policy approaches. Thematic analysis indicated scepticism about the effectiveness of labelling images. Quantitatively, adults, although not adolescents, reported that labelling was unlikely to improve body image. Appearance diversity in media and reorienting social norms from appearance to function and health were perceived as effective strategies. Social policy and research implications are discussed.

Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis

PubMed

Gao, Xin; Zhang, Wenjing; Yao, Li; Xiao, Yuan; Liu, Lu; Liu, Jieke; Li, Siyi; Tao, Bo; Shah, Chandan; Gong, Qiyong; Sweeney, John; Lui, Su

2017-12-05

Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta­-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular ­gyrus. The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia. 2017 Joule Inc., or its licensors

Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis.

PubMed

Gao, Xin; Zhang, Wenjing; Yao, Li; Xiao, Yuan; Liu, Lu; Liu, Jieke; Li, Siyi; Tao, Bo; Shah, Chandan; Gong, Qiyong; Sweeney, John A; Lui, Su

2018-03-01

Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular gyrus. The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia.

Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis.

PubMed

Gao, Xin; Zhang, Wenjing; Yao, Li; Xiao, Yuan; Liu, Lu; Liu, Jieke; Li, Siyi; Tao, Bo; Shah, Chandan; Gong, Qiyong; Sweeney, John A; Lui, Su

2017-12-15

Neuroimaging studies have shown both structural and functional abnormalities in patients with schizophrenia. Recently, studies have begun to explore the association between structural and functional grey matter abnormalities. By conducting a meta-analysis on morphometric and functional imaging studies of grey matter alterations in drug-free patients, the present study aims to examine the degree of overlap between brain regions with anatomic and functional changes in patients with schizophrenia. We performed a systematic search of PubMed, Embase, Web of Science and the Cochrane Library to identify relevant publications. A multimodal analysis was then conducted using Seed-based d Mapping software. Exploratory analyses included jackknife, subgroup and meta-regression analyses. We included 15 structural MRI studies comprising 486 drug-free patients and 485 healthy controls, and 16 functional MRI studies comprising 403 drug-free patients and 428 controls in our meta-analysis. Drug-free patients were examined to reduce pharmacological effects on the imaging data. Multimodal analysis showed considerable overlap between anatomic and functional changes, mainly in frontotemporal regions, bilateral medial posterior cingulate/paracingulate gyrus, bilateral insula, basal ganglia and left cerebellum. There were also brain regions showing only anatomic changes in the right superior frontal gyrus, left supramarginal gyrus, right lingual gyrus and functional alternations involving the right angular gyrus. The methodological aspects, patient characteristics and clinical variables of the included studies were heterogeneous, and we cannot exclude medication effects. The present study showed overlapping anatomic and functional brain abnormalities mainly in the default mode (DMN) and auditory networks (AN) in drug-free patients with schizophrenia. However, the pattern of changes differed in these networks. Decreased grey matter was associated with decreased activation within the DMN, whereas it was associated with increased activation within the AN. These discrete patterns suggest different pathophysiological changes impacting structural and functional associations within different neural networks in patients with schizophrenia.

Testing hypotheses of bat baculum function with 3D models derived from microCT

PubMed Central

Herdina, Anna Nele; Kelly, Diane A; Jahelková, Helena; Lina, Peter H C; Horáček, Ivan; Metscher, Brian D

2015-01-01

The baculum (os penis) has been extensively studied as a taxon-specific character in bats and other mammals but its mechanical function is still unclear. There is a wide consensus in the literature that the baculum is probably a sexually selected character. Using a novel approach combining postmortem manipulation and three-dimensional (3D) imaging, we tested two functional hypotheses in the common noctule bat Nyctalus noctula, the common pipistrelle Pipistrellus pipistrellus, and Nathusius’ pipistrelle Pipistrellus nathusii: (i) whether the baculum can protect the distal urethra and urethral opening from compression during erection and copulation; and (ii) whether the baculum and corpora cavernosa form a functional unit to support both the penile shaft and the more distal glans tip. In freshly dead or frozen and thawed bats, we compared flaccid penises with artificially ‘erect’ penises that were inflated with 10% formalin. Penises were stained with alcoholic iodine and imaged with a lab-based high-resolution x-ray microtomography system. Analysis of the 3D images enabled us to compare the changes in relative positions of the baculum, corpora cavernosa, urethra, and corpus spongiosum with one another between flaccid and ‘erect’ penises. Our results support both functional hypotheses, indicating that the baculum probably performs two different roles during erection. Our approach should prove valuable for comparing and testing the functions of different baculum morphologies in bats and other mammals. Moreover, we have validated an essential component of the groundwork necessary to extend this approach with finite element analysis for quantitative 3D biomechanical modeling of penis function. PMID:25655647

Nuclear cardiograph and scintigraphy

NASA Technical Reports Server (NTRS)

Mclaughlin, P.

1975-01-01

Extensive advances in the technology of detectors, data analysis systems, and tracers used have resulted in greatly expanded applications of radioisotopes to the assessment of cardiac function and disease. The development of nuclear cardiology has proceeded along four lines: (1) radionuclide angiography, (2) myocardial perfusion imaging, (3) intracoronary microsphere imaging, and (4) regional myocardial blood flow determination using inert gases.

Correlation between Gray/White Matter Volume and Cognition in Healthy Elderly People

ERIC Educational Resources Information Center

Taki, Yasuyuki; Kinomura, Shigeo; Sato, Kazunori; Goto, Ryoi; Wu, Kai; Kawashima, Ryuta; Fukuda, Hiroshi

2011-01-01

This study applied volumetric analysis and voxel-based morphometry (VBM) of brain magnetic resonance (MR) images to assess whether correlations exist between global and regional gray/white matter volume and the cognitive functions of semantic memory and short-term memory, which are relatively well preserved with aging, using MR image data from 109…

Exact image theory for the problem of dielectric/magnetic slab

NASA Technical Reports Server (NTRS)

Lindell, I. V.

1987-01-01

Exact image method, recently introduced for the exact solution of electromagnetic field problems involving homogeneous half spaces and microstrip-like geometries, is developed for the problem of homogeneous slab of dielectric and/or magnetic material in free space. Expressions for image sources, creating the exact reflected and transmitted fields, are given and their numerical evaluation is demonstrated. Nonradiating modes, guided by the slab and responsible for the loss of convergence of the image functions, are considered and extracted. The theory allows, for example, an analysis of finite ground planes in microstrip antenna structures.

A simplified and powerful image processing methods to separate Thai jasmine rice and sticky rice varieties

NASA Astrophysics Data System (ADS)

Khondok, Piyoros; Sakulkalavek, Aparporn; Suwansukho, Kajpanya

2018-03-01

A simplified and powerful image processing procedures to separate the paddy of KHAW DOK MALI 105 or Thai jasmine rice and the paddy of sticky rice RD6 varieties were proposed. The procedures consist of image thresholding, image chain coding and curve fitting using polynomial function. From the fitting, three parameters of each variety, perimeters, area, and eccentricity, were calculated. Finally, the overall parameters were determined by using principal component analysis. The result shown that these procedures can be significantly separate both varieties.

Estimation of Characteristics of Echo Envelope Using RF Echo Signal from the Liver

NASA Astrophysics Data System (ADS)

Yamaguchi, Tadashi; Hachiya, Hiroyuki; Kamiyama, Naohisa; Ikeda, Kazuki; Moriyasu, Norifumi

2001-05-01

To realize quantitative diagnosis of liver cirrhosis, we have been analyzing the probability density function (PDF) of echo amplitude using B-mode images. However, the B-mode image is affected by the various signal and image processing techniques used in the diagnosis equipment, so a detailed and quantitative analysis is very difficult. In this paper, we analyze the PDF of echo amplitude using RF echo signal and B-mode images of normal and cirrhotic livers, and compare both results to examine the validity of the RF echo signal.

Automated MicroSPECT/MicroCT Image Analysis of the Mouse Thyroid Gland.

PubMed

Cheng, Peng; Hollingsworth, Brynn; Scarberry, Daniel; Shen, Daniel H; Powell, Kimerly; Smart, Sean C; Beech, John; Sheng, Xiaochao; Kirschner, Lawrence S; Menq, Chia-Hsiang; Jhiang, Sissy M

2017-11-01

The ability of thyroid follicular cells to take up iodine enables the use of radioactive iodine (RAI) for imaging and targeted killing of RAI-avid thyroid cancer following thyroidectomy. To facilitate identifying novel strategies to improve 131 I therapeutic efficacy for patients with RAI refractory disease, it is desired to optimize image acquisition and analysis for preclinical mouse models of thyroid cancer. A customized mouse cradle was designed and used for microSPECT/CT image acquisition at 1 hour (t1) and 24 hours (t24) post injection of 123 I, which mainly reflect RAI influx/efflux equilibrium and RAI retention in the thyroid, respectively. FVB/N mice with normal thyroid glands and TgBRAF V600E mice with thyroid tumors were imaged. In-house CTViewer software was developed to streamline image analysis with new capabilities, along with display of 3D voxel-based 123 I gamma photon intensity in MATLAB. The customized mouse cradle facilitates consistent tissue configuration among image acquisitions such that rigid body registration can be applied to align serial images of the same mouse via the in-house CTViewer software. CTViewer is designed specifically to streamline SPECT/CT image analysis with functions tailored to quantify thyroid radioiodine uptake. Automatic segmentation of thyroid volumes of interest (VOI) from adjacent salivary glands in t1 images is enabled by superimposing the thyroid VOI from the t24 image onto the corresponding aligned t1 image. The extent of heterogeneity in 123 I accumulation within thyroid VOIs can be visualized by 3D display of voxel-based 123 I gamma photon intensity. MicroSPECT/CT image acquisition and analysis for thyroidal RAI uptake is greatly improved by the cradle and the CTViewer software, respectively. Furthermore, the approach of superimposing thyroid VOIs from t24 images to select thyroid VOIs on corresponding aligned t1 images can be applied to studies in which the target tissue has differential radiotracer retention from surrounding tissues.

Bioinformatic tools for inferring functional information from plant microarray data: tools for the first steps.

PubMed

Page, Grier P; Coulibaly, Issa

2008-01-01

Microarrays are a very powerful tool for quantifying the amount of RNA in samples; however, their ability to query essentially every gene in a genome, which can number in the tens of thousands, presents analytical and interpretative problems. As a result, a variety of software and web-based tools have been developed to help with these issues. This article highlights and reviews some of the tools for the first steps in the analysis of a microarray study. We have tried for a balance between free and commercial systems. We have organized the tools by topics including image processing tools (Section 2), power analysis tools (Section 3), image analysis tools (Section 4), database tools (Section 5), databases of functional information (Section 6), annotation tools (Section 7), statistical and data mining tools (Section 8), and dissemination tools (Section 9).

Application of argon atmospheric cold plasma for indium tin oxide (ITO) based diodes

NASA Astrophysics Data System (ADS)

Akbari Nia, S.; Jalili, Y. Seyed; Salar Elahi, A.

2017-09-01

Transparent Conductive Oxide (TCO) layers due to transparency, high conductivity and hole injection capability have attracted a lot of attention. One of these layers is Indium Tin Oxide (ITO). ITO due to low resistance, transparency in the visible spectrum and its proper work function is widely used in the manufacture of organic light emitting diodes and solar cells. One way for improving the ITO surface is plasma treatment. In this paper, changes in surface morphology, by applying argon atmospheric pressure cold plasma, was studied through Atomic Force Microscopic (AFM) image analysis and Fourier Transform Infrared Spectroscopy (FTIR) analysis. FTIR analysis showed functional groups were not added or removed, but chemical bond angle and bonds strength on the surface were changed and also AFM images showed that surface roughness was increased. These factors lead to the production of diodes with enhanced Ohmic contact and injection mechanism which are more appropriate in industrial applications.

Brain Responses to Dynamic Facial Expressions: A Normative Meta-Analysis.

PubMed

Zinchenko, Oksana; Yaple, Zachary A; Arsalidou, Marie

2018-01-01

Identifying facial expressions is crucial for social interactions. Functional neuroimaging studies show that a set of brain areas, such as the fusiform gyrus and amygdala, become active when viewing emotional facial expressions. The majority of functional magnetic resonance imaging (fMRI) studies investigating face perception typically employ static images of faces. However, studies that use dynamic facial expressions (e.g., videos) are accumulating and suggest that a dynamic presentation may be more sensitive and ecologically valid for investigating faces. By using quantitative fMRI meta-analysis the present study examined concordance of brain regions associated with viewing dynamic facial expressions. We analyzed data from 216 participants that participated in 14 studies, which reported coordinates for 28 experiments. Our analysis revealed bilateral fusiform and middle temporal gyri, left amygdala, left declive of the cerebellum and the right inferior frontal gyrus. These regions are discussed in terms of their relation to models of face processing.

Left ventricular function assessment using a fast 3D gradient echo pulse sequence: comparison to standard multi-breath hold 2D steady state free precession imaging and accounting for papillary muscles and trabeculations.

PubMed

Sievers, Burkhard; Schrader, Sebastian; Rehwald, Wolfgang; Hunold, Peter; Barkhausen, Joerg; Erbel, Raimund

2011-06-01

Papillary muscles and trabeculae for ventricular function analysis are known to significantly contribute to accurate volume and mass measurements. Fast imaging techniques such as three-dimensional steady-state free precession (3D SSFP) are increasingly being used to speed up imaging time, but sacrifice spatial resolution. It is unknown whether 3D SSFP, despite its reduced spatial resolution, allows for exact delineation of papillary muscles and trabeculations. We therefore compared 3D SSFP ventricular function measurements to those measured from standard multi-breath hold two-dimensional steady-state free precession cine images (standard 2D SSFP). 14 healthy subjects and 14 patients with impaired left ventricularfunction underwent 1.5 Tesla cine imaging. A stack of short axis images covering the left ventricle was acquired with 2D SSFP and 3D SSFP. Left ventricular volumes, ejection fraction, and mass were determined. Analysis was performed by substracting papillary muscles and trabeculae from left ventricular volumes. In addition, reproducibility was assessed. EDV, ESV, EF, and mass were not significantly different between 2D SSFP and 3D SSFP (mean difference healthy subjects: -0.06 +/- 3.2 ml, 0.54 +/- 2.2 ml, -0.45 +/- 1.8%, and 1.13 +/- 0.8 g, respectively; patients: 1.36 +/- 2.8 ml, -0.15 3.5 ml, 0.86 +/- 2.5%, and 0.91 +/- 0.9 g, respectively; P > or = 0.095). Intra- and interobserver variability was not different for 2D SSFP (P > or = 0.64 and P > or = 0.397) and 3D SSFP (P > or = 0.53 and P > or = 0.47). Differences in volumes, EF, and mass measurements between 3D SSFP and standard 2D SSFP are very small, and not statistically significant. 3D SSFP may be used for accurate ventricular function assessment when papillary muscles and trabeculations are to be taken into account.

Track-weighted functional connectivity (TW-FC): a tool for characterizing the structural-functional connections in the brain.

PubMed

Calamante, Fernando; Masterton, Richard A J; Tournier, Jacques-Donald; Smith, Robert E; Willats, Lisa; Raffelt, David; Connelly, Alan

2013-04-15

MRI provides a powerful tool for studying the functional and structural connections in the brain non-invasively. The technique of functional connectivity (FC) exploits the intrinsic temporal correlations of slow spontaneous signal fluctuations to characterise brain functional networks. In addition, diffusion MRI fibre-tracking can be used to study the white matter structural connections. In recent years, there has been considerable interest in combining these two techniques to provide an overall structural-functional description of the brain. In this work we applied the recently proposed super-resolution track-weighted imaging (TWI) methodology to demonstrate how whole-brain fibre-tracking data can be combined with FC data to generate a track-weighted (TW) FC map of FC networks. The method was applied to data from 8 healthy volunteers, and illustrated with (i) FC networks obtained using a seeded connectivity-based analysis (seeding in the precuneus/posterior cingulate cortex, PCC, known to be part of the default mode network), and (ii) with FC networks generated using independent component analysis (in particular, the default mode, attention, visual, and sensory-motor networks). TW-FC maps showed high intensity in white matter structures connecting the nodes of the FC networks. For example, the cingulum bundles show the strongest TW-FC values in the PCC seeded-based analysis, due to their major role in the connection between medial frontal cortex and precuneus/posterior cingulate cortex; similarly the superior longitudinal fasciculus was well represented in the attention network, the optic radiations in the visual network, and the corticospinal tract and corpus callosum in the sensory-motor network. The TW-FC maps highlight the white matter connections associated with a given FC network, and their intensity in a given voxel reflects the functional connectivity of the part of the nodes of the network linked by the structural connections traversing that voxel. They therefore contain a different (and novel) image contrast from that of the images used to generate them. The results shown in this study illustrate the potential of the TW-FC approach for the fusion of structural and functional data into a single quantitative image. This technique could therefore have important applications in neuroscience and neurology, such as for voxel-based comparison studies. Copyright © 2012 Elsevier Inc. All rights reserved.

Functional Magnetic Resonance Imaging with Concurrent Urodynamic Testing Identifies Brain Structures Involved in Micturition Cycle in Patients with Multiple Sclerosis.

PubMed

Khavari, Rose; Karmonik, Christof; Shy, Michael; Fletcher, Sophie; Boone, Timothy

2017-02-01

Neurogenic lower urinary tract dysfunction, which is common in patients with multiple sclerosis, has a significant impact on quality of life. In this study we sought to determine brain activity processes during the micturition cycle in female patients with multiple sclerosis and neurogenic lower urinary tract dysfunction. We report brain activity on functional magnetic resonance imaging and simultaneous urodynamic testing in 23 ambulatory female patients with multiple sclerosis. Individual functional magnetic resonance imaging activation maps at strong desire to void and at initiation of voiding were calculated and averaged at Montreal Neuroimaging Institute. Areas of significant activation were identified in these average maps. Subgroup analysis was performed in patients with elicitable neurogenic detrusor overactivity or detrusor-sphincter dyssynergia. Group analysis of all patients at strong desire to void yielded areas of activation in regions associated with executive function (frontal gyrus), emotional regulation (cingulate gyrus) and motor control (putamen, cerebellum and precuneus). Comparison of the average change in activation between previously reported healthy controls and patients with multiple sclerosis showed predominantly stronger, more focal activation in the former and lower, more diffused activation in the latter. Patients with multiple sclerosis who had demonstrable neurogenic detrusor overactivity and detrusor-sphincter dyssynergia showed a trend toward distinct brain activation at full urge and at initiation of voiding respectively. We successfully studied brain activation during the entire micturition cycle in female patients with neurogenic lower urinary tract dysfunction and multiple sclerosis using a concurrent functional magnetic resonance imaging/urodynamic testing platform. Understanding the central neural processes involved in specific parts of micturition in patients with neurogenic lower urinary tract dysfunction may identify areas of interest for future intervention. Copyright © 2017 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Dosimetric feasibility of 4DCT-ventilation imaging guided proton therapy for locally advanced non-small-cell lung cancer.

PubMed

Huang, Qijie; Jabbour, Salma K; Xiao, Zhiyan; Yue, Ning; Wang, Xiao; Cao, Hongbin; Kuang, Yu; Zhang, Yin; Nie, Ke

2018-04-25

The principle aim of this study is to incorporate 4DCT ventilation imaging into functional treatment planning that preserves high-functioning lung with both double scattering and scanning beam techniques in proton therapy. Eight patients with locally advanced non-small-cell lung cancer were included in this study. Deformable image registration was performed for each patient on their planning 4DCTs and the resultant displacement vector field with Jacobian analysis was used to identify the high-, medium- and low-functional lung regions. Five plans were designed for each patient: a regular photon IMRT vs. anatomic proton plans without consideration of functional ventilation information using double scattering proton therapy (DSPT) and intensity modulated proton therapy (IMPT) vs. functional proton plans with avoidance of high-functional lung using both DSPT and IMPT. Dosimetric parameters were compared in terms of tumor coverage, plan heterogeneity, and avoidance of normal tissues. Our results showed that both DSPT and IMPT plans gave superior dose advantage to photon IMRTs in sparing low dose regions of the total lung in terms of V5 (volume receiving 5Gy). The functional DSPT only showed marginal benefit in sparing high-functioning lung in terms of V5 or V20 (volume receiving 20Gy) compared to anatomical plans. Yet, the functional planning in IMPT delivery, can further reduce the low dose in high-functioning lung without degrading the PTV dosimetric coverages, compared to anatomical proton planning. Although the doses to some critical organs might increase during functional planning, the necessary constraints were all met. Incorporating 4DCT ventilation imaging into functional proton therapy is feasible. The functional proton plans, in intensity modulated proton delivery, are effective to further preserve high-functioning lung regions without degrading the PTV coverage.

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

PubMed

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

2007-01-01

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

VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations: VQone MATLAB toolbox.

PubMed

Nuutinen, Mikko; Virtanen, Toni; Rummukainen, Olli; Häkkinen, Jukka

2016-03-01

This article presents VQone, a graphical experiment builder, written as a MATLAB toolbox, developed for image and video quality ratings. VQone contains the main elements needed for the subjective image and video quality rating process. This includes building and conducting experiments and data analysis. All functions can be controlled through graphical user interfaces. The experiment builder includes many standardized image and video quality rating methods. Moreover, it enables the creation of new methods or modified versions from standard methods. VQone is distributed free of charge under the terms of the GNU general public license and allows code modifications to be made so that the program's functions can be adjusted according to a user's requirements. VQone is available for download from the project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

Further fMRI Validation of the Visual Half Field Technique as an Indicator of Language Laterality: A Large-Group Analysis

ERIC Educational Resources Information Center

Van der Haegen, Lise; Cai, Qing; Seurinck, Ruth; Brysbaert, Marc

2011-01-01

The best established lateralized cerebral function is speech production, with the majority of the population having left hemisphere dominance. An important question is how to best assess the laterality of this function. Neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI) are increasingly used in clinical settings to…

MO-FG-202-04: Gantry-Resolved Linac QA for VMAT: A Comprehensive and Efficient System Using An Electronic Portal Imaging Device

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

Zwan, B J; University of Newcastle, Newcastle, NSW; Barnes, M

2016-06-15

Purpose: To automate gantry-resolved linear accelerator (linac) quality assurance (QA) for volumetric modulated arc therapy (VMAT) using an electronic portal imaging device (EPID). Methods: A QA system for VMAT was developed that uses an EPID, frame-grabber assembly and in-house developed image processing software. The system relies solely on the analysis of EPID image frames acquired without the presence of a phantom. Images were acquired at 8.41 frames per second using a frame grabber and ancillary acquisition computer. Each image frame was tagged with a gantry angle from the linac’s on-board gantry angle encoder. Arc-dynamic QA plans were designed to assessmore » the performance of each individual linac component during VMAT. By analysing each image frame acquired during the QA deliveries the following eight machine performance characteristics were measured as a function of gantry angle: MLC positional accuracy, MLC speed constancy, MLC acceleration constancy, MLC-gantry synchronisation, beam profile constancy, dose rate constancy, gantry speed constancy, dose-gantry angle synchronisation and mechanical sag. All tests were performed on a Varian iX linear accelerator equipped with a 120 leaf Millennium MLC and an aS1000 EPID (Varian Medical Systems, Palo Alto, CA, USA). Results: Machine performance parameters were measured as a function of gantry angle using EPID imaging and compared to machine log files and the treatment plan. Data acquisition is currently underway at 3 centres, incorporating 7 treatment units, at 2 weekly measurement intervals. Conclusion: The proposed system can be applied for streamlined linac QA and commissioning for VMAT. The set of test plans developed can be used to assess the performance of each individual components of the treatment machine during VMAT deliveries as a function of gantry angle. The methodology does not require the setup of any additional phantom or measurement equipment and the analysis is fully automated to allow for regular routine testing.« less

Functional approach to high-throughput plant growth analysis

PubMed Central

2013-01-01

Method Taking advantage of the current rapid development in imaging systems and computer vision algorithms, we present HPGA, a high-throughput phenotyping platform for plant growth modeling and functional analysis, which produces better understanding of energy distribution in regards of the balance between growth and defense. HPGA has two components, PAE (Plant Area Estimation) and GMA (Growth Modeling and Analysis). In PAE, by taking the complex leaf overlap problem into consideration, the area of every plant is measured from top-view images in four steps. Given the abundant measurements obtained with PAE, in the second module GMA, a nonlinear growth model is applied to generate growth curves, followed by functional data analysis. Results Experimental results on model plant Arabidopsis thaliana show that, compared to an existing approach, HPGA reduces the error rate of measuring plant area by half. The application of HPGA on the cfq mutant plants under fluctuating light reveals the correlation between low photosynthetic rates and small plant area (compared to wild type), which raises a hypothesis that knocking out cfq changes the sensitivity of the energy distribution under fluctuating light conditions to repress leaf growth. Availability HPGA is available at http://www.msu.edu/~jinchen/HPGA. PMID:24565437

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

All you need to know about the tricuspid valve: Tricuspid valve imaging and tricuspid regurgitation analysis.

PubMed

Huttin, Olivier; Voilliot, Damien; Mandry, Damien; Venner, Clément; Juillière, Yves; Selton-Suty, Christine

2016-01-01

The acknowledgment of tricuspid regurgitation (TR) as a stand-alone and progressive entity, worsening the prognosis of patients whatever its aetiology, has led to renewed interest in the tricuspid-right ventricular complex. The tricuspid valve (TV) is a complex, dynamic and changing structure. As the TV is not easy to analyse, three-dimensional imaging, cardiac magnetic resonance imaging and computed tomography scans may add to two-dimensional transthoracic and transoesophageal echocardiographic data in the analysis of TR. Not only the severity of TR, but also its mechanisms, the mode of leaflet coaptation, the degree of tricuspid annulus enlargement and tenting, and the haemodynamic consequences for right atrial and right ventricular morphology and function have to be taken into account. TR is functional and is a satellite of left-sided heart disease and/or elevated pulmonary artery pressure most of the time; a particular form is characterized by TR worsening after left-sided valve surgery, which has been shown to impair patient prognosis. A better description of TV anatomy and function by multimodality imaging should help with the appropriate selection of patients who will benefit from either surgical TV repair/replacement or a percutaneous procedure for TR, especially among patients who are to undergo or have undergone primary left-sided valvular surgery. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

G protein-coupled receptor internalization assays in the high-content screening format.

PubMed

Haasen, Dorothea; Schnapp, Andreas; Valler, Martin J; Heilker, Ralf

2006-01-01

High-content screening (HCS), a combination of fluorescence microscopic imaging and automated image analysis, has become a frequently applied tool to study test compound effects in cellular disease-modeling systems. This chapter describes the measurement of G protein-coupled receptor (GPCR) internalization in the HCS format using a high-throughput, confocal cellular imaging device. GPCRs are the most successful group of therapeutic targets on the pharmaceutical market. Accordingly, the search for compounds that interfere with GPCR function in a specific and selective way is a major focus of the pharmaceutical industry today. This chapter describes methods for the ligand-induced internalization of GPCRs labeled previously with either a fluorophore-conjugated ligand or an antibody directed against an N-terminal tag of the GPCR. Both labeling techniques produce robust assay formats. Complementary to other functional GPCR drug discovery assays, internalization assays enable a pharmacological analysis of test compounds. We conclude that GPCR internalization assays represent a valuable medium/high-throughput screening format to determine the cellular activity of GPCR ligands.

Improvement of white matter and functional connectivity abnormalities by repetitive transcranial magnetic stimulation in crossed aphasia in dextral.

PubMed

Lu, Haitao; Wu, Haiyan; Cheng, Hewei; Wei, Dongjie; Wang, Xiaoyan; Fan, Yong; Zhang, Hao; Zhang, Tong

2014-01-01

As a special aphasia, the occurrence of crossed aphasia in dextral (CAD) is unusual. This study aims to improve the language ability by applying 1 Hz repetitive transcranial magnetic stimulation (rTMS). We studied multiple modality imaging of structural connectivity (diffusion tensor imaging), functional connectivity (resting fMRI), PET, and neurolinguistic analysis on a patient with CAD. Furthermore, we applied rTMS of 1 Hz for 40 times and observed the language function improvement. The results indicated that a significantly reduced structural and function connectivity was found in DTI and fMRI data compared with the control. The PET imaging showed hypo-metabolism in right hemisphere and left cerebellum. In conclusion, one of the mechanisms of CAD is that right hemisphere is the language dominance. Stimulating left Wernicke area could improve auditory comprehension, stimulating left Broca's area could enhance expression, and the results outlasted 6 months by 1 Hz rTMS balancing the excitability inter-hemisphere in CAD.

[Functional magnetic resonance imaging of brain of college students with internet addiction].

PubMed

DU, Wanping; Liu, Jun; Gao, Xunping; Li, Lingjiang; Li, Weihui; Li, Xin; Zhang, Yan; Zhou, Shunke

2011-08-01

To explore the functional locations of brain regions related to internet addiction (IA)with task-functional magnetic resonance imaging (fMRI). Nineteen college students who had internet game addition and 19 controls accepted the stimuli of videos via computer. The 3.0 Tesla MRI was used to record the Results of echo plannar imaging. The block design method was used. Intragroup and intergroup analysis Results in the 2 groups were obtained. The differences between the 2 groups were analyzed. The internet game videos markedly activated the brain regions of the college students who had or had no internet game addiction. Compared with the control group, the IA group showed increased activation in the right superior parietal lobule, right insular lobe, right precuneus, right cingulated gyrus, and right superior temporal gyrus. Internet game tasks can activate the vision, space, attention and execution center which are composed of temporal occipital gyrus and frontal parietal gyrus. Abnormal brain function and lateral activation of the right brain may exist in IA.

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.

TH-E-BRF-02: 4D-CT Ventilation Image-Based IMRT Plans Are Dosimetrically Comparable to SPECT Ventilation Image-Based Plans

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

Kida, S; University of Tokyo Hospital, Bunkyo, Tokyo; Bal, M

Purpose: An emerging lung ventilation imaging method based on 4D-CT can be used in radiotherapy to selectively avoid irradiating highly-functional lung regions, which may reduce pulmonary toxicity. Efforts to validate 4DCT ventilation imaging have been focused on comparison with other imaging modalities including SPECT and xenon CT. The purpose of this study was to compare 4D-CT ventilation image-based functional IMRT plans with SPECT ventilation image-based plans as reference. Methods: 4D-CT and SPECT ventilation scans were acquired for five thoracic cancer patients in an IRB-approved prospective clinical trial. The ventilation images were created by quantitative analysis of regional volume changes (amore » surrogate for ventilation) using deformable image registration of the 4D-CT images. A pair of 4D-CT ventilation and SPECT ventilation image-based IMRT plans was created for each patient. Regional ventilation information was incorporated into lung dose-volume objectives for IMRT optimization by assigning different weights on a voxel-by-voxel basis. The objectives and constraints of the other structures in the plan were kept identical. The differences in the dose-volume metrics have been evaluated and tested by a paired t-test. SPECT ventilation was used to calculate the lung functional dose-volume metrics (i.e., mean dose, V20 and effective dose) for both 4D-CT ventilation image-based and SPECT ventilation image-based plans. Results: Overall there were no statistically significant differences in any dose-volume metrics between the 4D-CT and SPECT ventilation imagebased plans. For example, the average functional mean lung dose of the 4D-CT plans was 26.1±9.15 (Gy), which was comparable to 25.2±8.60 (Gy) of the SPECT plans (p = 0.89). For other critical organs and PTV, nonsignificant differences were found as well. Conclusion: This study has demonstrated that 4D-CT ventilation image-based functional IMRT plans are dosimetrically comparable to SPECT ventilation image-based plans, providing evidence to use 4D-CT ventilation imaging for clinical applications. Supported in part by Free to Breathe Young Investigator Research Grant and NIH/NCI R01 CA 093626. The authors thank Philips Radiation Oncology Systems for the Pinnacle3 treatment planning systems.« less

Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in Diffusion MRI.

PubMed

Cheng, Jian; Basser, Peter J

2018-01-01

In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI), a tensor field or a spherical function field (e.g., an orientation distribution function field), can be estimated from measured diffusion weighted images. In this paper, inspired by the microscopic theoretical treatment of phases in liquid crystals, we introduce a novel mathematical framework, called Director Field Analysis (DFA), to study local geometric structural information of white matter based on the reconstructed tensor field or spherical function field: (1) We propose a set of mathematical tools to process general director data, which consists of dyadic tensors that have orientations but no direction. (2) We propose Orientational Order (OO) and Orientational Dispersion (OD) indices to describe the degree of alignment and dispersion of a spherical function in a single voxel or in a region, respectively; (3) We also show how to construct a local orthogonal coordinate frame in each voxel exhibiting anisotropic diffusion; (4) Finally, we define three indices to describe three types of orientational distortion (splay, bend, and twist) in a local spatial neighborhood, and a total distortion index to describe distortions of all three types. To our knowledge, this is the first work to quantitatively describe orientational distortion (splay, bend, and twist) in general spherical function fields from DTI or HARDI data. The proposed DFA and its related mathematical tools can be used to process not only diffusion MRI data but also general director field data, and the proposed scalar indices are useful for detecting local geometric changes of white matter for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The related codes and a tutorial for DFA will be released in DMRITool. Copyright © 2017 Elsevier B.V. All rights reserved.

Extended depth of field imaging for high speed object analysis

NASA Technical Reports Server (NTRS)

Frost, Keith (Inventor); Ortyn, William (Inventor); Basiji, David (Inventor); Bauer, Richard (Inventor); Liang, Luchuan (Inventor); Hall, Brian (Inventor); Perry, David (Inventor)

2011-01-01

A high speed, high-resolution flow imaging system is modified to achieve extended depth of field imaging. An optical distortion element is introduced into the flow imaging system. Light from an object, such as a cell, is distorted by the distortion element, such that a point spread function (PSF) of the imaging system is invariant across an extended depth of field. The distorted light is spectrally dispersed, and the dispersed light is used to simultaneously generate a plurality of images. The images are detected, and image processing is used to enhance the detected images by compensating for the distortion, to achieve extended depth of field images of the object. The post image processing preferably involves de-convolution, and requires knowledge of the PSF of the imaging system, as modified by the optical distortion element.

Pathophysiology of Degenerative Mitral Regurgitation: New 3-Dimensional Imaging Insights.

PubMed

Antoine, Clemence; Mantovani, Francesca; Benfari, Giovanni; Mankad, Sunil V; Maalouf, Joseph F; Michelena, Hector I; Enriquez-Sarano, Maurice

2018-01-01

Despite its high prevalence, little is known about mechanisms of mitral regurgitation in degenerative mitral valve disease apart from the leaflet prolapse itself. Mitral valve is a complex structure, including mitral annulus, mitral leaflets, papillary muscles, chords, and left ventricular walls. All these structures are involved in physiological and pathological functioning of this valvuloventricular complex but up to now were difficult to analyze because of inherent limitations of 2-dimensional imaging. The advent of 3-dimensional echocardiography, computed tomography, and cardiac magnetic resonance imaging overcoming these limitations provides new insights into mechanistic analysis of degenerative mitral regurgitation. This review will detail the contribution of quantitative and qualitative dynamic analysis of mitral annulus and mitral leaflets by new imaging methods in the understanding of degenerative mitral regurgitation pathophysiology. © 2018 American Heart Association, Inc.

Identifying the arterial input function from dynamic contrast-enhanced magnetic resonance images using an apex-seeking technique

NASA Astrophysics Data System (ADS)

Martel, Anne L.

2004-04-01

In order to extract quantitative information from dynamic contrast-enhanced MR images (DCE-MRI) it is usually necessary to identify an arterial input function. This is not a trivial problem if there are no major vessels present in the field of view. Most existing techniques rely on operator intervention or use various curve parameters to identify suitable pixels but these are often specific to the anatomical region or the acquisition method used. They also require the signal from several pixels to be averaged in order to improve the signal to noise ratio, however this introduces errors due to partial volume effects. We have described previously how factor analysis can be used to automatically separate arterial and venous components from DCE-MRI studies of the brain but although that method works well for single slice images through the brain when the blood brain barrier technique is intact, it runs into problems for multi-slice images with more complex dynamics. This paper will describe a factor analysis method that is more robust in such situations and is relatively insensitive to the number of physiological components present in the data set. The technique is very similar to that used to identify spectral end-members from multispectral remote sensing images.

Principles of PET/MR Imaging.

PubMed

Disselhorst, Jonathan A; Bezrukov, Ilja; Kolb, Armin; Parl, Christoph; Pichler, Bernd J

2014-06-01

Hybrid PET/MR systems have rapidly progressed from the prototype stage to systems that are increasingly being used in the clinics. This review provides an overview of developments in hybrid PET/MR systems and summarizes the current state of the art in PET/MR instrumentation, correction techniques, and data analysis. The strong magnetic field requires considerable changes in the manner by which PET images are acquired and has led, among others, to the development of new PET detectors, such as silicon photomultipliers. During more than a decade of active PET/MR development, several system designs have been described. The technical background of combined PET/MR systems is explained and related challenges are discussed. The necessity for PET attenuation correction required new methods based on MR data. Therefore, an overview of recent developments in this field is provided. Furthermore, MR-based motion correction techniques for PET are discussed, as integrated PET/MR systems provide a platform for measuring motion with high temporal resolution without additional instrumentation. The MR component in PET/MR systems can provide functional information about disease processes or brain function alongside anatomic images. Against this background, we point out new opportunities for data analysis in this new field of multimodal molecular imaging. © 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science

PubMed Central

2016-01-01

When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574303

Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science.

PubMed

Turner, Robert

2016-10-05

When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. © 2016 The Authors.

Functional mapping of language networks in the normal brain using a word-association task.

PubMed

Ghosh, Shantanu; Basu, Amrita; Kumaran, Senthil S; Khushu, Subash

2010-08-01

Language functions are known to be affected in diverse neurological conditions, including ischemic stroke, traumatic brain injury, and brain tumors. Because language networks are extensive, interpretation of functional data depends on the task completed during evaluation. The aim was to map the hemodynamic consequences of word association using functional magnetic resonance imaging (fMRI) in normal human subjects. Ten healthy subjects underwent fMRI scanning with a postlexical access semantic association task vs lexical processing task. The fMRI protocol involved a T2*-weighted gradient-echo echo-planar imaging (GE-EPI) sequence (TR 4523 ms, TE 64 ms, flip angle 90°) with alternate baseline and activation blocks. A total of 78 scans were taken (interscan interval = 3 s) with a total imaging time of 587 s. Functional data were processed in Statistical Parametric Mapping software (SPM2) with 8-mm Gaussian kernel by convolving the blood oxygenation level-dependent (BOLD) signal with an hemodynamic response function estimated by general linear method to generate SPM{t} and SPM{F} maps. Single subject analysis of the functional data (FWE-corrected, P≤0.001) revealed extensive activation in the frontal lobes, with overlaps among middle frontal gyrus (MFG), superior, and inferior frontal gyri. BOLD activity was also found in the medial frontal gyrus, middle occipital gyrus (MOG), anterior fusiform gyrus, superior and inferior parietal lobules, and to a smaller extent, the thalamus and right anterior cerebellum. Group analysis (FWE-corrected, P≤0.001) revealed neural recruitment of bilateral lingual gyri, left MFG, bilateral MOG, left superior occipital gyrus, left fusiform gyrus, bilateral thalami, and right cerebellar areas. Group data analysis revealed a cerebellar-occipital-fusiform-thalamic network centered around bilateral lingual gyri for word association, thereby indicating how these areas facilitate language comprehension by activating a semantic association network of words processed postlexical access. This finding is important when assessing the extent of cognitive damage and/or recovery and can be used for presurgical planning after optimization.

Towards an Analysis of Visual Images in School Science Textbooks and Press Articles about Science and Technology

NASA Astrophysics Data System (ADS)

Dimopoulos, Kostas; Koulaidis, Vasilis; Sklaveniti, Spyridoula

2003-04-01

This paper aims at presenting the application of a grid for the analysis of the pedagogic functions of visual images included in school science textbooks and daily press articles about science and technology. The analysis is made using the dimensions of content specialisation (classification) and social-pedagogic relationships (framing) promoted by the images as well as the elaboration and abstraction of the corresponding visual code (formality), thus combining pedagogical and socio-semiotic perspectives. The grid is applied to the analysis of 2819 visual images collected from school science textbooks and another 1630 visual images additionally collected from the press. The results show that the science textbooks in comparison to the press material: a) use ten times more images, b) use more images so as to familiarise their readers with the specialised techno-scientific content and codes, and c) tend to create a sense of higher empowerment for their readers by using the visual mode. Furthermore, as the educational level of the school science textbooks (i.e., from primary to lower secondary level) rises, the content specialisation projected by the visual images and the elaboration and abstraction of the corresponding visual code also increases. The above results have implications for the terms and conditions for the effective exploitation of visual material as the educational level rises as well as for the effective incorporation of visual images from press material into science classes.

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

PubMed

Milchenko, Mikhail; Snyder, Abraham Z; LaMontagne, Pamela; Shimony, Joshua S; Benzinger, Tammie L; Fouke, Sarah Jost; Marcus, Daniel S

2016-07-01

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

PubMed Central

Fan, Chong; Wu, Chaoyun; Li, Grand; Ma, Jun

2017-01-01

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the high-resolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. PMID:28208837