Science.gov

Sample records for image analysis

  1. Retinal Imaging and Image Analysis

    PubMed Central

    Abràmoff, Michael D.; Garvin, Mona K.; Sonka, Milan

    2011-01-01

    Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships. PMID:21743764

  2. Image-analysis library

    NASA Technical Reports Server (NTRS)

    1980-01-01

    MATHPAC image-analysis library is collection of general-purpose mathematical and statistical routines and special-purpose data-analysis and pattern-recognition routines for image analysis. MATHPAC library consists of Linear Algebra, Optimization, Statistical-Summary, Densities and Distribution, Regression, and Statistical-Test packages.

  3. Electronic image analysis

    NASA Astrophysics Data System (ADS)

    Gahm, J.; Grosskopf, R.; Jaeger, H.; Trautwein, F.

    1980-12-01

    An electronic system for image analysis was developed on the basis of low and medium cost integrated circuits. The printed circuit boards were designed, using the principles of modern digital electronics and data processing. The system consists of modules for automatic, semiautomatic and visual image analysis. They can be used for microscopical and macroscopical observations. Photographs can be evaluated, too. The automatic version is controlled by software modules adapted to various applications. The result is a system for image analysis suitable for many different measurement problems. The features contained in large image areas can be measured. For automatic routine analysis controlled by processing calculators the necessary software and hardware modules are available.

  4. Basics of image analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging technology has emerged as a powerful tool for quality and safety inspection of food and agricultural products and in precision agriculture over the past decade. Image analysis is a critical step in implementing hyperspectral imaging technology; it is aimed to improve the qualit...

  5. Image Analysis of Foods.

    PubMed

    Russ, John C

    2015-09-01

    The structure of foods, both natural and processed ones, is controlled by many variables ranging from biology to chemistry and mechanical forces. The structure also controls many of the properties of the food, including consumer acceptance, taste, mouthfeel, appearance, and so on, and nutrition. Imaging provides an important tool for measuring the structure of foods. This includes 2-dimensional (2D) images of surfaces and sections, for example, viewed in a microscope, as well as 3-dimensional (3D) images of internal structure as may be produced by confocal microscopy, or computed tomography and magnetic resonance imaging. The use of images also guides robotics for harvesting and sorting. Processing of images may be needed to calibrate colors, reduce noise, enhance detail, and delineate structure and dimensions. Measurement of structural information such as volume fraction and internal surface areas, as well as the analysis of object size, location, and shape in both 2- and 3-dimensional images is illustrated and described, with primary references and examples from a wide range of applications. PMID:26270611

  6. Picosecond Imaging Circuit Analysis

    NASA Astrophysics Data System (ADS)

    Kash, Jeffrey A.

    1998-03-01

    With ever-increasing complexity, probing the internal operation of a silicon IC becomes more challenging. Present methods of internal probing are becoming obsolete. We have discovered that a very weak picosecond pulse of light is emitted by each FET in a CMOS circuit whenever the circuit changes logic state. This pulsed emission can be simultaneously imaged and time resolved, using a technique we have named Picosecond Imaging Circuit Analysis (PICA). With a suitable imaging detector, PICA allows time resolved measurement on thousands of devices simultaneously. Computer videos made from measurements on real IC's will be shown. These videos, along with a more quantitative evaluation of the light emission, permit the complete operation of an IC to be measured in a non-invasive way with picosecond time resolution.

  7. Image analysis library software development

    NASA Technical Reports Server (NTRS)

    Guseman, L. F., Jr.; Bryant, J.

    1977-01-01

    The Image Analysis Library consists of a collection of general purpose mathematical/statistical routines and special purpose data analysis/pattern recognition routines basic to the development of image analysis techniques for support of current and future Earth Resources Programs. Work was done to provide a collection of computer routines and associated documentation which form a part of the Image Analysis Library.

  8. Medical Image Analysis Facility

    NASA Technical Reports Server (NTRS)

    1978-01-01

    To improve the quality of photos sent to Earth by unmanned spacecraft. NASA's Jet Propulsion Laboratory (JPL) developed a computerized image enhancement process that brings out detail not visible in the basic photo. JPL is now applying this technology to biomedical research in its Medical lrnage Analysis Facility, which employs computer enhancement techniques to analyze x-ray films of internal organs, such as the heart and lung. A major objective is study of the effects of I stress on persons with heart disease. In animal tests, computerized image processing is being used to study coronary artery lesions and the degree to which they reduce arterial blood flow when stress is applied. The photos illustrate the enhancement process. The upper picture is an x-ray photo in which the artery (dotted line) is barely discernible; in the post-enhancement photo at right, the whole artery and the lesions along its wall are clearly visible. The Medical lrnage Analysis Facility offers a faster means of studying the effects of complex coronary lesions in humans, and the research now being conducted on animals is expected to have important application to diagnosis and treatment of human coronary disease. Other uses of the facility's image processing capability include analysis of muscle biopsy and pap smear specimens, and study of the microscopic structure of fibroprotein in the human lung. Working with JPL on experiments are NASA's Ames Research Center, the University of Southern California School of Medicine, and Rancho Los Amigos Hospital, Downey, California.

  9. Digital Image Analysis of Cereals

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Image analysis is the extraction of meaningful information from images, mainly digital images by means of digital processing techniques. The field was established in the 1950s and coincides with the advent of computer technology, as image analysis is profoundly reliant on computer processing. As t...

  10. Radar image analysis utilizing junctive image metamorphosis

    NASA Astrophysics Data System (ADS)

    Krueger, Peter G.; Gouge, Sally B.; Gouge, Jim O.

    1998-09-01

    A feasibility study was initiated to investigate the ability of algorithms developed for medical sonogram image analysis, to be trained for extraction of cartographic information from synthetic aperture radar imagery. BioComputer Research Inc. has applied proprietary `junctive image metamorphosis' algorithms to cancer cell recognition and identification in ultrasound prostate images. These algorithms have been shown to support automatic radar image feature detection and identification. Training set images were used to develop determinants for representative point, line and area features, which were used on test images to identify and localize the features of interest. The software is computationally conservative; operating on a PC platform in real time. The algorithms are robust; having applicability to be trained for feature recognition on any digital imagery, not just those formed from reflected energy, such as sonograms and radar images. Applications include land mass characterization, feature identification, target recognition, and change detection.

  11. Statistical image analysis of longitudinal RAVENS images

    PubMed Central

    Lee, Seonjoo; Zipunnikov, Vadim; Reich, Daniel S.; Pham, Dzung L.

    2015-01-01

    Regional analysis of volumes examined in normalized space (RAVENS) are transformation images used in the study of brain morphometry. In this paper, RAVENS images are analyzed using a longitudinal variant of voxel-based morphometry (VBM) and longitudinal functional principal component analysis (LFPCA) for high-dimensional images. We demonstrate that the latter overcomes the limitations of standard longitudinal VBM analyses, which does not separate registration errors from other longitudinal changes and baseline patterns. This is especially important in contexts where longitudinal changes are only a small fraction of the overall observed variability, which is typical in normal aging and many chronic diseases. Our simulation study shows that LFPCA effectively separates registration error from baseline and longitudinal signals of interest by decomposing RAVENS images measured at multiple visits into three components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the irreversible changes over multiple visits, and a subject-visit specific imaging deviation. We describe strategies to identify baseline/longitudinal variation and registration errors combined with covariates of interest. Our analysis suggests that specific regional brain atrophy and ventricular enlargement are associated with multiple sclerosis (MS) disease progression. PMID:26539071

  12. Reflections on ultrasound image analysis.

    PubMed

    Alison Noble, J

    2016-10-01

    Ultrasound (US) image analysis has advanced considerably in twenty years. Progress in ultrasound image analysis has always been fundamental to the advancement of image-guided interventions research due to the real-time acquisition capability of ultrasound and this has remained true over the two decades. But in quantitative ultrasound image analysis - which takes US images and turns them into more meaningful clinical information - thinking has perhaps more fundamentally changed. From roots as a poor cousin to Computed Tomography (CT) and Magnetic Resonance (MR) image analysis, both of which have richer anatomical definition and thus were better suited to the earlier eras of medical image analysis which were dominated by model-based methods, ultrasound image analysis has now entered an exciting new era, assisted by advances in machine learning and the growing clinical and commercial interest in employing low-cost portable ultrasound devices outside traditional hospital-based clinical settings. This short article provides a perspective on this change, and highlights some challenges ahead and potential opportunities in ultrasound image analysis which may both have high impact on healthcare delivery worldwide in the future but may also, perhaps, take the subject further away from CT and MR image analysis research with time. PMID:27503078

  13. Spotlight-8 Image Analysis Software

    NASA Technical Reports Server (NTRS)

    Klimek, Robert; Wright, Ted

    2006-01-01

    Spotlight is a cross-platform GUI-based software package designed to perform image analysis on sequences of images generated by combustion and fluid physics experiments run in a microgravity environment. Spotlight can perform analysis on a single image in an interactive mode or perform analysis on a sequence of images in an automated fashion. Image processing operations can be employed to enhance the image before various statistics and measurement operations are performed. An arbitrarily large number of objects can be analyzed simultaneously with independent areas of interest. Spotlight saves results in a text file that can be imported into other programs for graphing or further analysis. Spotlight can be run on Microsoft Windows, Linux, and Apple OS X platforms.

  14. Oncological image analysis: medical and molecular image analysis

    NASA Astrophysics Data System (ADS)

    Brady, Michael

    2007-03-01

    This paper summarises the work we have been doing on joint projects with GE Healthcare on colorectal and liver cancer, and with Siemens Molecular Imaging on dynamic PET. First, we recall the salient facts about cancer and oncological image analysis. Then we introduce some of the work that we have done on analysing clinical MRI images of colorectal and liver cancer, specifically the detection of lymph nodes and segmentation of the circumferential resection margin. In the second part of the paper, we shift attention to the complementary aspect of molecular image analysis, illustrating our approach with some recent work on: tumour acidosis, tumour hypoxia, and multiply drug resistant tumours.

  15. Hyperspectral image analysis. A tutorial.

    PubMed

    Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa

    2015-10-01

    This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case. PMID:26481986

  16. Histopathological Image Analysis: A Review

    PubMed Central

    Gurcan, Metin N.; Boucheron, Laura; Can, Ali; Madabhushi, Anant; Rajpoot, Nasir; Yener, Bulent

    2010-01-01

    Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement to the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe. PMID:20671804

  17. 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

  18. Image Analysis in Surgical Pathology.

    PubMed

    Lloyd, Mark C; Monaco, James P; Bui, Marilyn M

    2016-06-01

    Digitization of glass slides of surgical pathology samples facilitates a number of value-added capabilities beyond what a pathologist could previously do with a microscope. Image analysis is one of the most fundamental opportunities to leverage the advantages that digital pathology provides. The ability to quantify aspects of a digital image is an extraordinary opportunity to collect data with exquisite accuracy and reliability. In this review, we describe the history of image analysis in pathology and the present state of technology processes as well as examples of research and clinical use. PMID:27241112

  19. Image analysis for DNA sequencing

    NASA Astrophysics Data System (ADS)

    Palaniappan, Kannappan; Huang, Thomas S.

    1991-07-01

    There is a great deal of interest in automating the process of DNA (deoxyribonucleic acid) sequencing to support the analysis of genomic DNA such as the Human and Mouse Genome projects. In one class of gel-based sequencing protocols autoradiograph images are generated in the final step and usually require manual interpretation to reconstruct the DNA sequence represented by the image. The need to handle a large volume of sequence information necessitates automation of the manual autoradiograph reading step through image analysis in order to reduce the length of time required to obtain sequence data and reduce transcription errors. Various adaptive image enhancement, segmentation and alignment methods were applied to autoradiograph images. The methods are adaptive to the local characteristics of the image such as noise, background signal, or presence of edges. Once the two-dimensional data is converted to a set of aligned one-dimensional profiles waveform analysis is used to determine the location of each band which represents one nucleotide in the sequence. Different classification strategies including a rule-based approach are investigated to map the profile signals, augmented with the original two-dimensional image data as necessary, to textual DNA sequence information.

  20. Anmap: Image and data analysis

    NASA Astrophysics Data System (ADS)

    Alexander, Paul; Waldram, Elizabeth; Titterington, David; Rees, Nick

    2014-11-01

    Anmap analyses and processes images and spectral data. Originally written for use in radio astronomy, much of its functionality is applicable to other disciplines; additional algorithms and analysis procedures allow direct use in, for example, NMR imaging and spectroscopy. Anmap emphasizes the analysis of data to extract quantitative results for comparison with theoretical models and/or other experimental data. To achieve this, Anmap provides a wide range of tools for analysis, fitting and modelling (including standard image and data processing algorithms). It also provides a powerful environment for users to develop their own analysis/processing tools either by combining existing algorithms and facilities with the very powerful command (scripting) language or by writing new routines in FORTRAN that integrate seamlessly with the rest of Anmap.

  1. Image analysis and quantitative morphology.

    PubMed

    Mandarim-de-Lacerda, Carlos Alberto; Fernandes-Santos, Caroline; Aguila, Marcia Barbosa

    2010-01-01

    Quantitative studies are increasingly found in the literature, particularly in the fields of development/evolution, pathology, and neurosciences. Image digitalization converts tissue images into a numeric form by dividing them into very small regions termed picture elements or pixels. Image analysis allows automatic morphometry of digitalized images, and stereology aims to understand the structural inner three-dimensional arrangement based on the analysis of slices showing two-dimensional information. To quantify morphological structures in an unbiased and reproducible manner, appropriate isotropic and uniform random sampling of sections, and updated stereological tools are needed. Through the correct use of stereology, a quantitative study can be performed with little effort; efficiency in stereology means as little counting as possible (little work), low cost (section preparation), but still good accuracy. This short text provides a background guide for non-expert morphologists. PMID:19960334

  2. Multispectral Imaging Broadens Cellular Analysis

    NASA Technical Reports Server (NTRS)

    2007-01-01

    Amnis Corporation, a Seattle-based biotechnology company, developed ImageStream to produce sensitive fluorescence images of cells in flow. The company responded to an SBIR solicitation from Ames Research Center, and proposed to evaluate several methods of extending the depth of field for its ImageStream system and implement the best as an upgrade to its commercial products. This would allow users to view whole cells at the same time, rather than just one section of each cell. Through Phase I and II SBIR contracts, Ames provided Amnis the funding the company needed to develop this extended functionality. For NASA, the resulting high-speed image flow cytometry process made its way into Medusa, a life-detection instrument built to collect, store, and analyze sample organisms from erupting hydrothermal vents, and has the potential to benefit space flight health monitoring. On the commercial end, Amnis has implemented the process in ImageStream, combining high-resolution microscopy and flow cytometry in a single instrument, giving researchers the power to conduct quantitative analyses of individual cells and cell populations at the same time, in the same experiment. ImageStream is also built for many other applications, including cell signaling and pathway analysis; classification and characterization of peripheral blood mononuclear cell populations; quantitative morphology; apoptosis (cell death) assays; gene expression analysis; analysis of cell conjugates; molecular distribution; and receptor mapping and distribution.

  3. Quantitative histogram analysis of images

    NASA Astrophysics Data System (ADS)

    Holub, Oliver; Ferreira, Sérgio T.

    2006-11-01

    A routine for histogram analysis of images has been written in the object-oriented, graphical development environment LabVIEW. The program converts an RGB bitmap image into an intensity-linear greyscale image according to selectable conversion coefficients. This greyscale image is subsequently analysed by plots of the intensity histogram and probability distribution of brightness, and by calculation of various parameters, including average brightness, standard deviation, variance, minimal and maximal brightness, mode, skewness and kurtosis of the histogram and the median of the probability distribution. The program allows interactive selection of specific regions of interest (ROI) in the image and definition of lower and upper threshold levels (e.g., to permit the removal of a constant background signal). The results of the analysis of multiple images can be conveniently saved and exported for plotting in other programs, which allows fast analysis of relatively large sets of image data. The program file accompanies this manuscript together with a detailed description of two application examples: The analysis of fluorescence microscopy images, specifically of tau-immunofluorescence in primary cultures of rat cortical and hippocampal neurons, and the quantification of protein bands by Western-blot. The possibilities and limitations of this kind of analysis are discussed. Program summaryTitle of program: HAWGC Catalogue identifier: ADXG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXG_v1_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computers: Mobile Intel Pentium III, AMD Duron Installations: No installation necessary—Executable file together with necessary files for LabVIEW Run-time engine Operating systems or monitors under which the program has been tested: WindowsME/2000/XP Programming language used: LabVIEW 7.0 Memory required to execute with typical data:˜16MB for starting and ˜160MB used for

  4. A computational image analysis glossary for biologists.

    PubMed

    Roeder, Adrienne H K; Cunha, Alexandre; Burl, Michael C; Meyerowitz, Elliot M

    2012-09-01

    Recent advances in biological imaging have resulted in an explosion in the quality and quantity of images obtained in a digital format. Developmental biologists are increasingly acquiring beautiful and complex images, thus creating vast image datasets. In the past, patterns in image data have been detected by the human eye. Larger datasets, however, necessitate high-throughput objective analysis tools to computationally extract quantitative information from the images. These tools have been developed in collaborations between biologists, computer scientists, mathematicians and physicists. In this Primer we present a glossary of image analysis terms to aid biologists and briefly discuss the importance of robust image analysis in developmental studies. PMID:22872081

  5. Target identification by image analysis.

    PubMed

    Fetz, V; Prochnow, H; Brönstrup, M; Sasse, F

    2016-05-01

    Covering: 1997 to the end of 2015Each biologically active compound induces phenotypic changes in target cells that are characteristic for its mode of action. These phenotypic alterations can be directly observed under the microscope or made visible by labelling structural elements or selected proteins of the cells with dyes. A comparison of the cellular phenotype induced by a compound of interest with the phenotypes of reference compounds with known cellular targets allows predicting its mode of action. While this approach has been successfully applied to the characterization of natural products based on a visual inspection of images, recent studies used automated microscopy and analysis software to increase speed and to reduce subjective interpretation. In this review, we give a general outline of the workflow for manual and automated image analysis, and we highlight natural products whose bacterial and eucaryotic targets could be identified through such approaches. PMID:26777141

  6. Planning applications in image analysis

    NASA Technical Reports Server (NTRS)

    Boddy, Mark; White, Jim; Goldman, Robert; Short, Nick, Jr.

    1994-01-01

    We describe two interim results from an ongoing effort to automate the acquisition, analysis, archiving, and distribution of satellite earth science data. Both results are applications of Artificial Intelligence planning research to the automatic generation of processing steps for image analysis tasks. First, we have constructed a linear conditional planner (CPed), used to generate conditional processing plans. Second, we have extended an existing hierarchical planning system to make use of durations, resources, and deadlines, thus supporting the automatic generation of processing steps in time and resource-constrained environments.

  7. Quantitative multi-image analysis for biomedical Raman spectroscopic imaging.

    PubMed

    Hedegaard, Martin A B; Bergholt, Mads S; Stevens, Molly M

    2016-05-01

    Imaging by Raman spectroscopy enables unparalleled label-free insights into cell and tissue composition at the molecular level. With established approaches limited to single image analysis, there are currently no general guidelines or consensus on how to quantify biochemical components across multiple Raman images. Here, we describe a broadly applicable methodology for the combination of multiple Raman images into a single image for analysis. This is achieved by removing image specific background interference, unfolding the series of Raman images into a single dataset, and normalisation of each Raman spectrum to render comparable Raman images. Multivariate image analysis is finally applied to derive the contributing 'pure' biochemical spectra for relative quantification. We present our methodology using four independently measured Raman images of control cells and four images of cells treated with strontium ions from substituted bioactive glass. We show that the relative biochemical distribution per area of the cells can be quantified. In addition, using k-means clustering, we are able to discriminate between the two cell types over multiple Raman images. This study shows a streamlined quantitative multi-image analysis tool for improving cell/tissue characterisation and opens new avenues in biomedical Raman spectroscopic imaging. PMID:26833935

  8. Grid computing in image analysis

    PubMed Central

    2011-01-01

    Diagnostic surgical pathology or tissue–based diagnosis still remains the most reliable and specific diagnostic medical procedure. The development of whole slide scanners permits the creation of virtual slides and to work on so-called virtual microscopes. In addition to interactive work on virtual slides approaches have been reported that introduce automated virtual microscopy, which is composed of several tools focusing on quite different tasks. These include evaluation of image quality and image standardization, analysis of potential useful thresholds for object detection and identification (segmentation), dynamic segmentation procedures, adjustable magnification to optimize feature extraction, and texture analysis including image transformation and evaluation of elementary primitives. Grid technology seems to possess all features to efficiently target and control the specific tasks of image information and detection in order to obtain a detailed and accurate diagnosis. Grid technology is based upon so-called nodes that are linked together and share certain communication rules in using open standards. Their number and functionality can vary according to the needs of a specific user at a given point in time. When implementing automated virtual microscopy with Grid technology, all of the five different Grid functions have to be taken into account, namely 1) computation services, 2) data services, 3) application services, 4) information services, and 5) knowledge services. Although all mandatory tools of automated virtual microscopy can be implemented in a closed or standardized open system, Grid technology offers a new dimension to acquire, detect, classify, and distribute medical image information, and to assure quality in tissue–based diagnosis. PMID:21516880

  9. Automated image analysis of uterine cervical images

    NASA Astrophysics Data System (ADS)

    Li, Wenjing; Gu, Jia; Ferris, Daron; Poirson, Allen

    2007-03-01

    Cervical Cancer is the second most common cancer among women worldwide and the leading cause of cancer mortality of women in developing countries. If detected early and treated adequately, cervical cancer can be virtually prevented. Cervical precursor lesions and invasive cancer exhibit certain morphologic features that can be identified during a visual inspection exam. Digital imaging technologies allow us to assist the physician with a Computer-Aided Diagnosis (CAD) system. In colposcopy, epithelium that turns white after application of acetic acid is called acetowhite epithelium. Acetowhite epithelium is one of the major diagnostic features observed in detecting cancer and pre-cancerous regions. Automatic extraction of acetowhite regions from cervical images has been a challenging task due to specular reflection, various illumination conditions, and most importantly, large intra-patient variation. This paper presents a multi-step acetowhite region detection system to analyze the acetowhite lesions in cervical images automatically. First, the system calibrates the color of the cervical images to be independent of screening devices. Second, the anatomy of the uterine cervix is analyzed in terms of cervix region, external os region, columnar region, and squamous region. Third, the squamous region is further analyzed and subregions based on three levels of acetowhite are identified. The extracted acetowhite regions are accompanied by color scores to indicate the different levels of acetowhite. The system has been evaluated by 40 human subjects' data and demonstrates high correlation with experts' annotations.

  10. Neural network ultrasound image analysis

    NASA Astrophysics Data System (ADS)

    Schneider, Alexander C.; Brown, David G.; Pastel, Mary S.

    1993-09-01

    Neural network based analysis of ultrasound image data was carried out on liver scans of normal subjects and those diagnosed with diffuse liver disease. In a previous study, ultrasound images from a group of normal volunteers, Gaucher's disease patients, and hepatitis patients were obtained by Garra et al., who used classical statistical methods to distinguish from among these three classes. In the present work, neural network classifiers were employed with the same image features found useful in the previous study for this task. Both standard backpropagation neural networks and a recently developed biologically-inspired network called Dystal were used. Classification performance as measured by the area under a receiver operating characteristic curve was generally excellent for the back propagation networks and was roughly comparable to that of classical statistical discriminators tested on the same data set and documented in the earlier study. Performance of the Dystal network was significantly inferior; however, this may be due to the choice of network parameter. Potential methods for enhancing network performance was identified.

  11. Spreadsheet-like image analysis

    NASA Astrophysics Data System (ADS)

    Wilson, Paul

    1992-08-01

    This report describes the design of a new software system being built by the Army to support and augment automated nondestructive inspection (NDI) on-line equipment implemented by the Army for detection of defective manufactured items. The new system recalls and post-processes (off-line) the NDI data sets archived by the on-line equipment for the purpose of verifying the correctness of the inspection analysis paradigms, of developing better analysis paradigms and to gather statistics on the defects of the items inspected. The design of the system is similar to that of a spreadsheet, i.e., an array of cells which may be programmed to contain functions with arguments being data from other cells and whose resultant is the output of that cell's function. Unlike a spreadsheet, the arguments and the resultants of a cell may be a matrix such as a two-dimensional matrix of picture elements (pixels). Functions include matrix mathematics, neural networks and image processing as well as those ordinarily found in spreadsheets. The system employs all of the common environmental supports of the Macintosh computer, which is the hardware platform. The system allows the resultant of a cell to be displayed in any of multiple formats such as a matrix of numbers, text, an image, or a chart. Each cell is a window onto the resultant. Like a spreadsheet if the input value of any cell is changed its effect is cascaded into the resultants of all cells whose functions use that value directly or indirectly. The system encourages the user to play what-of games, as ordinary spreadsheets do.

  12. IMAGE ANALYSIS ALGORITHMS FOR DUAL MODE IMAGING SYSTEMS

    SciTech Connect

    Robinson, Sean M.; Jarman, Kenneth D.; Miller, Erin A.; Misner, Alex C.; Myjak, Mitchell J.; Pitts, W. Karl; Seifert, Allen; Seifert, Carolyn E.; Woodring, Mitchell L.

    2010-06-11

    The level of detail discernable in imaging techniques has generally excluded them from consideration as verification tools in inspection regimes where information barriers are mandatory. However, if a balance can be struck between sufficient information barriers and feature extraction to verify or identify objects of interest, imaging may significantly advance verification efforts. This paper describes the development of combined active (conventional) radiography and passive (auto) radiography techniques for imaging sensitive items assuming that comparison images cannot be furnished. Three image analysis algorithms are presented, each of which reduces full image information to non-sensitive feature information and ultimately is intended to provide only a yes/no response verifying features present in the image. These algorithms are evaluated on both their technical performance in image analysis and their application with or without an explicitly constructed information barrier. The first algorithm reduces images to non-invertible pixel intensity histograms, retaining only summary information about the image that can be used in template comparisons. This one-way transform is sufficient to discriminate between different image structures (in terms of area and density) without revealing unnecessary specificity. The second algorithm estimates the attenuation cross-section of objects of known shape based on transition characteristics around the edge of the object’s image. The third algorithm compares the radiography image with the passive image to discriminate dense, radioactive material from point sources or inactive dense material. By comparing two images and reporting only a single statistic from the combination thereof, this algorithm can operate entirely behind an information barrier stage. Together with knowledge of the radiography system, the use of these algorithms in combination can be used to improve verification capability to inspection regimes and improve

  13. FFDM image quality assessment using computerized image texture analysis

    NASA Astrophysics Data System (ADS)

    Berger, Rachelle; Carton, Ann-Katherine; Maidment, Andrew D. A.; Kontos, Despina

    2010-04-01

    Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility, the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p<=0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p<=0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.

  14. Image analysis applications for grain science

    NASA Astrophysics Data System (ADS)

    Zayas, Inna Y.; Steele, James L.

    1991-02-01

    Morphometrical features of single grain kernels or particles were used to discriminate two visibly similar wheat varieties foreign material in wheat hardsoft and spring-winter wheat classes and whole from broken corn kernels. Milled fractions of hard and soft wheat were evaluated using textural image analysis. Color image analysis of sound and mold damaged corn kernels yielded high recognition rates. The studies collectively demonstrate the potential for automated classification and assessment of grain quality using image analysis.

  15. Automatic processing, analysis, and recognition of images

    NASA Astrophysics Data System (ADS)

    Abrukov, Victor S.; Smirnov, Evgeniy V.; Ivanov, Dmitriy G.

    2004-11-01

    New approaches and computer codes (A&CC) for automatic processing, analysis and recognition of images are offered. The A&CC are based on presentation of object image as a collection of pixels of various colours and consecutive automatic painting of distinguished itself parts of the image. The A&CC have technical objectives centred on such direction as: 1) image processing, 2) image feature extraction, 3) image analysis and some others in any consistency and combination. The A&CC allows to obtain various geometrical and statistical parameters of object image and its parts. Additional possibilities of the A&CC usage deal with a usage of artificial neural networks technologies. We believe that A&CC can be used at creation of the systems of testing and control in a various field of industry and military applications (airborne imaging systems, tracking of moving objects), in medical diagnostics, at creation of new software for CCD, at industrial vision and creation of decision-making system, etc. The opportunities of the A&CC are tested at image analysis of model fires and plumes of the sprayed fluid, ensembles of particles, at a decoding of interferometric images, for digitization of paper diagrams of electrical signals, for recognition of the text, for elimination of a noise of the images, for filtration of the image, for analysis of the astronomical images and air photography, at detection of objects.

  16. Satellite image analysis using neural networks

    NASA Technical Reports Server (NTRS)

    Sheldon, Roger A.

    1990-01-01

    The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

  17. Microscopy image segmentation tool: Robust image data analysis

    SciTech Connect

    Valmianski, Ilya Monton, Carlos; Schuller, Ivan K.

    2014-03-15

    We present a software package called Microscopy Image Segmentation Tool (MIST). MIST is designed for analysis of microscopy images which contain large collections of small regions of interest (ROIs). Originally developed for analysis of porous anodic alumina scanning electron images, MIST capabilities have been expanded to allow use in a large variety of problems including analysis of biological tissue, inorganic and organic film grain structure, as well as nano- and meso-scopic structures. MIST provides a robust segmentation algorithm for the ROIs, includes many useful analysis capabilities, and is highly flexible allowing incorporation of specialized user developed analysis. We describe the unique advantages MIST has over existing analysis software. In addition, we present a number of diverse applications to scanning electron microscopy, atomic force microscopy, magnetic force microscopy, scanning tunneling microscopy, and fluorescent confocal laser scanning microscopy.

  18. Image registration with uncertainty analysis

    DOEpatents

    Simonson, Katherine M.

    2011-03-22

    In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

  19. Digital-image processing and image analysis of glacier ice

    USGS Publications Warehouse

    Fitzpatrick, Joan J.

    2013-01-01

    This document provides a methodology for extracting grain statistics from 8-bit color and grayscale images of thin sections of glacier ice—a subset of physical properties measurements typically performed on ice cores. This type of analysis is most commonly used to characterize the evolution of ice-crystal size, shape, and intercrystalline spatial relations within a large body of ice sampled by deep ice-coring projects from which paleoclimate records will be developed. However, such information is equally useful for investigating the stress state and physical responses of ice to stresses within a glacier. The methods of analysis presented here go hand-in-hand with the analysis of ice fabrics (aggregate crystal orientations) and, when combined with fabric analysis, provide a powerful method for investigating the dynamic recrystallization and deformation behaviors of bodies of ice in motion. The procedures described in this document compose a step-by-step handbook for a specific image acquisition and data reduction system built in support of U.S. Geological Survey ice analysis projects, but the general methodology can be used with any combination of image processing and analysis software. The specific approaches in this document use the FoveaPro 4 plug-in toolset to Adobe Photoshop CS5 Extended but it can be carried out equally well, though somewhat less conveniently, with software such as the image processing toolbox in MATLAB, Image-Pro Plus, or ImageJ.

  20. Millimeter-wave sensor image analysis

    NASA Technical Reports Server (NTRS)

    Wilson, William J.; Suess, Helmut

    1989-01-01

    Images of an airborne, scanning, radiometer operating at a frequency of 98 GHz, have been analyzed. The mm-wave images were obtained in 1985/1986 using the JPL mm-wave imaging sensor. The goal of this study was to enhance the information content of these images and make their interpretation easier for human analysis. In this paper, a visual interpretative approach was used for information extraction from the images. This included application of nonlinear transform techniques for noise reduction and for color, contrast and edge enhancement. Results of the techniques on selected mm-wave images are presented.

  1. Image processing software for imaging spectrometry data analysis

    NASA Technical Reports Server (NTRS)

    Mazer, Alan; Martin, Miki; Lee, Meemong; Solomon, Jerry E.

    1988-01-01

    Imaging spectrometers simultaneously collect image data in hundreds of spectral channels, from the near-UV to the IR, and can thereby provide direct surface materials identification by means resembling laboratory reflectance spectroscopy. Attention is presently given to a software system, the Spectral Analysis Manager (SPAM) for the analysis of imaging spectrometer data. SPAM requires only modest computational resources and is composed of one main routine and a set of subroutine libraries. Additions and modifications are relatively easy, and special-purpose algorithms have been incorporated that are tailored to geological applications.

  2. Quantitative analysis of digital microscope images.

    PubMed

    Wolf, David E; Samarasekera, Champika; Swedlow, Jason R

    2013-01-01

    This chapter discusses quantitative analysis of digital microscope images and presents several exercises to provide examples to explain the concept. This chapter also presents the basic concepts in quantitative analysis for imaging, but these concepts rest on a well-established foundation of signal theory and quantitative data analysis. This chapter presents several examples for understanding the imaging process as a transformation from sample to image and the limits and considerations of quantitative analysis. This chapter introduces to the concept of digitally correcting the images and also focuses on some of the more critical types of data transformation and some of the frequently encountered issues in quantization. Image processing represents a form of data processing. There are many examples of data processing such as fitting the data to a theoretical curve. In all these cases, it is critical that care is taken during all steps of transformation, processing, and quantization. PMID:23931513

  3. Multiscale Analysis of Solar Image Data

    NASA Astrophysics Data System (ADS)

    Young, C. A.; Myers, D. C.

    2001-12-01

    It is often said that the blessing and curse of solar physics is that there is too much data. Solar missions such as Yohkoh, SOHO and TRACE have shown us the Sun with amazing clarity but have also cursed us with an increased amount of higher complexity data than previous missions. We have improved our view of the Sun yet we have not improved our analysis techniques. The standard techniques used for analysis of solar images generally consist of observing the evolution of features in a sequence of byte scaled images or a sequence of byte scaled difference images. The determination of features and structures in the images are done qualitatively by the observer. There is little quantitative and objective analysis done with these images. Many advances in image processing techniques have occured in the past decade. Many of these methods are possibly suited for solar image analysis. Multiscale/Multiresolution methods are perhaps the most promising. These methods have been used to formulate the human ability to view and comprehend phenomena on different scales. So these techniques could be used to quantitify the imaging processing done by the observers eyes and brains. In this work we present a preliminary analysis of multiscale techniques applied to solar image data. Specifically, we explore the use of the 2-d wavelet transform and related transforms with EIT, LASCO and TRACE images. This work was supported by NASA contract NAS5-00220.

  4. A 3D image analysis tool for SPECT imaging

    NASA Astrophysics Data System (ADS)

    Kontos, Despina; Wang, Qiang; Megalooikonomou, Vasileios; Maurer, Alan H.; Knight, Linda C.; Kantor, Steve; Fisher, Robert S.; Simonian, Hrair P.; Parkman, Henry P.

    2005-04-01

    We have developed semi-automated and fully-automated tools for the analysis of 3D single-photon emission computed tomography (SPECT) images. The focus is on the efficient boundary delineation of complex 3D structures that enables accurate measurement of their structural and physiologic properties. We employ intensity based thresholding algorithms for interactive and semi-automated analysis. We also explore fuzzy-connectedness concepts for fully automating the segmentation process. We apply the proposed tools to SPECT image data capturing variation of gastric accommodation and emptying. These image analysis tools were developed within the framework of a noninvasive scintigraphic test to measure simultaneously both gastric emptying and gastric volume after ingestion of a solid or a liquid meal. The clinical focus of the particular analysis was to probe associations between gastric accommodation/emptying and functional dyspepsia. Employing the proposed tools, we outline effectively the complex three dimensional gastric boundaries shown in the 3D SPECT images. We also perform accurate volume calculations in order to quantitatively assess the gastric mass variation. This analysis was performed both with the semi-automated and fully-automated tools. The results were validated against manual segmentation performed by a human expert. We believe that the development of an automated segmentation tool for SPECT imaging of the gastric volume variability will allow for other new applications of SPECT imaging where there is a need to evaluate complex organ function or tumor masses.

  5. Image Reconstruction Using Analysis Model Prior.

    PubMed

    Han, Yu; Du, Huiqian; Lam, Fan; Mei, Wenbo; Fang, Liping

    2016-01-01

    The analysis model has been previously exploited as an alternative to the classical sparse synthesis model for designing image reconstruction methods. Applying a suitable analysis operator on the image of interest yields a cosparse outcome which enables us to reconstruct the image from undersampled data. In this work, we introduce additional prior in the analysis context and theoretically study the uniqueness issues in terms of analysis operators in general position and the specific 2D finite difference operator. We establish bounds on the minimum measurement numbers which are lower than those in cases without using analysis model prior. Based on the idea of iterative cosupport detection (ICD), we develop a novel image reconstruction model and an effective algorithm, achieving significantly better reconstruction performance. Simulation results on synthetic and practical magnetic resonance (MR) images are also shown to illustrate our theoretical claims. PMID:27379171

  6. Image Reconstruction Using Analysis Model Prior

    PubMed Central

    Han, Yu; Du, Huiqian; Lam, Fan; Mei, Wenbo; Fang, Liping

    2016-01-01

    The analysis model has been previously exploited as an alternative to the classical sparse synthesis model for designing image reconstruction methods. Applying a suitable analysis operator on the image of interest yields a cosparse outcome which enables us to reconstruct the image from undersampled data. In this work, we introduce additional prior in the analysis context and theoretically study the uniqueness issues in terms of analysis operators in general position and the specific 2D finite difference operator. We establish bounds on the minimum measurement numbers which are lower than those in cases without using analysis model prior. Based on the idea of iterative cosupport detection (ICD), we develop a novel image reconstruction model and an effective algorithm, achieving significantly better reconstruction performance. Simulation results on synthetic and practical magnetic resonance (MR) images are also shown to illustrate our theoretical claims. PMID:27379171

  7. Description, Recognition and Analysis of Biological Images

    SciTech Connect

    Yu Donggang; Jin, Jesse S.; Luo Suhuai; Pham, Tuan D.; Lai Wei

    2010-01-25

    Description, recognition and analysis biological images plays an important role for human to describe and understand the related biological information. The color images are separated by color reduction. A new and efficient linearization algorithm is introduced based on some criteria of difference chain code. A series of critical points is got based on the linearized lines. The series of curvature angle, linearity, maximum linearity, convexity, concavity and bend angle of linearized lines are calculated from the starting line to the end line along all smoothed contours. The useful method can be used for shape description and recognition. The analysis, decision, classification of the biological images are based on the description of morphological structures, color information and prior knowledge, which are associated each other. The efficiency of the algorithms is described based on two applications. One application is the description, recognition and analysis of color flower images. Another one is related to the dynamic description, recognition and analysis of cell-cycle images.

  8. Accuracy in Quantitative 3D Image Analysis

    PubMed Central

    Bassel, George W.

    2015-01-01

    Quantitative 3D imaging is becoming an increasingly popular and powerful approach to investigate plant growth and development. With the increased use of 3D image analysis, standards to ensure the accuracy and reproducibility of these data are required. This commentary highlights how image acquisition and postprocessing can introduce artifacts into 3D image data and proposes steps to increase both the accuracy and reproducibility of these analyses. It is intended to aid researchers entering the field of 3D image processing of plant cells and tissues and to help general readers in understanding and evaluating such data. PMID:25804539

  9. Optical Analysis of Microscope Images

    NASA Astrophysics Data System (ADS)

    Biles, Jonathan R.

    Microscope images were analyzed with coherent and incoherent light using analog optical techniques. These techniques were found to be useful for analyzing large numbers of nonsymbolic, statistical microscope images. In the first part phase coherent transparencies having 20-100 human multiple myeloma nuclei were simultaneously photographed at 100 power magnification using high resolution holographic film developed to high contrast. An optical transform was obtained by focussing the laser onto each nuclear image and allowing the diffracted light to propagate onto a one dimensional photosensor array. This method reduced the data to the position of the first two intensity minima and the intensity of successive maxima. These values were utilized to estimate the four most important cancer detection clues of nuclear size, shape, darkness, and chromatin texture. In the second part, the geometric and holographic methods of phase incoherent optical processing were investigated for pattern recognition of real-time, diffuse microscope images. The theory and implementation of these processors was discussed in view of their mutual problems of dimness, image bias, and detector resolution. The dimness problem was solved by either using a holographic correlator or a speckle free laser microscope. The latter was built using a spinning tilted mirror which caused the speckle to change so quickly that it averaged out during the exposure. To solve the bias problem low image bias templates were generated by four techniques: microphotography of samples, creation of typical shapes by computer graphics editor, transmission holography of photoplates of samples, and by spatially coherent color image bias removal. The first of these templates was used to perform correlations with bacteria images. The aperture bias was successfully removed from the correlation with a video frame subtractor. To overcome the limited detector resolution it is necessary to discover some analog nonlinear intensity

  10. Objective analysis of image quality of video image capture systems

    NASA Astrophysics Data System (ADS)

    Rowberg, Alan H.

    1990-07-01

    As Picture Archiving and Communication System (PACS) technology has matured, video image capture has become a common way of capturing digital images from many modalities. While digital interfaces, such as those which use the ACR/NEMA standard, will become more common in the future, and are preferred because of the accuracy of image transfer, video image capture will be the dominant method in the short term, and may continue to be used for some time because of the low cost and high speed often associated with such devices. Currently, virtually all installed systems use methods of digitizing the video signal that is produced for display on the scanner viewing console itself. A series of digital test images have been developed for display on either a GE CT9800 or a GE Signa MRI scanner. These images have been captured with each of five commercially available image capture systems, and the resultant images digitally transferred on floppy disk to a PC1286 computer containing Optimast' image analysis software. Here the images can be displayed in a comparative manner for visual evaluation, in addition to being analyzed statistically. Each of the images have been designed to support certain tests, including noise, accuracy, linearity, gray scale range, stability, slew rate, and pixel alignment. These image capture systems vary widely in these characteristics, in addition to the presence or absence of other artifacts, such as shading and moire pattern. Other accessories such as video distribution amplifiers and noise filters can also add or modify artifacts seen in the captured images, often giving unusual results. Each image is described, together with the tests which were performed using them. One image contains alternating black and white lines, each one pixel wide, after equilibration strips ten pixels wide. While some systems have a slew rate fast enough to track this correctly, others blur it to an average shade of gray, and do not resolve the lines, or give

  11. Scale-Specific Multifractal Medical Image Analysis

    PubMed Central

    Braverman, Boris

    2013-01-01

    Fractal geometry has been applied widely in the analysis of medical images to characterize the irregular complex tissue structures that do not lend themselves to straightforward analysis with traditional Euclidean geometry. In this study, we treat the nonfractal behaviour of medical images over large-scale ranges by considering their box-counting fractal dimension as a scale-dependent parameter rather than a single number. We describe this approach in the context of the more generalized Rényi entropy, in which we can also compute the information and correlation dimensions of images. In addition, we describe and validate a computational improvement to box-counting fractal analysis. This improvement is based on integral images, which allows the speedup of any box-counting or similar fractal analysis algorithm, including estimation of scale-dependent dimensions. Finally, we applied our technique to images of invasive breast cancer tissue from 157 patients to show a relationship between the fractal analysis of these images over certain scale ranges and pathologic tumour grade (a standard prognosticator for breast cancer). Our approach is general and can be applied to any medical imaging application in which the complexity of pathological image structures may have clinical value. PMID:24023588

  12. Materials characterization through quantitative digital image analysis

    SciTech Connect

    J. Philliber; B. Antoun; B. Somerday; N. Yang

    2000-07-01

    A digital image analysis system has been developed to allow advanced quantitative measurement of microstructural features. This capability is maintained as part of the microscopy facility at Sandia, Livermore. The system records images digitally, eliminating the use of film. Images obtained from other sources may also be imported into the system. Subsequent digital image processing enhances image appearance through the contrast and brightness adjustments. The system measures a variety of user-defined microstructural features--including area fraction, particle size and spatial distributions, grain sizes and orientations of elongated particles. These measurements are made in a semi-automatic mode through the use of macro programs and a computer controlled translation stage. A routine has been developed to create large montages of 50+ separate images. Individual image frames are matched to the nearest pixel to create seamless montages. Results from three different studies are presented to illustrate the capabilities of the system.

  13. Factor Analysis of the Image Correlation Matrix.

    ERIC Educational Resources Information Center

    Kaiser, Henry F.; Cerny, Barbara A.

    1979-01-01

    Whether to factor the image correlation matrix or to use a new model with an alpha factor analysis of it is mentioned, with particular reference to the determinacy problem. It is pointed out that the distribution of the images is sensibly multivariate normal, making for "better" factor analyses. (Author/CTM)

  14. Viewing angle analysis of integral imaging

    NASA Astrophysics Data System (ADS)

    Wang, Hong-Xia; Wu, Chun-Hong; Yang, Yang; Zhang, Lan

    2007-12-01

    Integral imaging (II) is a technique capable of displaying 3D images with continuous parallax in full natural color. It is becoming the most perspective technique in developing next generation three-dimensional TV (3DTV) and visualization field due to its outstanding advantages. However, most of conventional integral images are restricted by its narrow viewing angle. One reason is that the range in which a reconstructed integral image can be displayed with consistent parallax is limited. The other is that the aperture of system is finite. By far many methods , an integral imaging method to enhance the viewing angle of integral images has been proposed. Nevertheless, except Ren's MVW (Maximum Viewing Width) most of these methods involve complex hardware and modifications of optical system, which usually bring other disadvantages and make operation more difficult. At the same time the cost of these systems should be higher. In order to simplify optical systems, this paper systematically analyzes the viewing angle of traditional integral images instead of modified ones. Simultaneously for the sake of cost the research was based on computer generated integral images (CGII). With the analysis result we can know clearly how the viewing angle can be enhanced and how the image overlap or image flipping can be avoided. The result also promotes the development of optical instruments. Based on theoretical analysis, preliminary calculation was done to demonstrate how the other viewing properties which are closely related with the viewing angle, such as viewing distance, viewing zone, lens pitch, and etc. affect the viewing angle.

  15. Depth-based selective image reconstruction using spatiotemporal image analysis

    NASA Astrophysics Data System (ADS)

    Haga, Tetsuji; Sumi, Kazuhiko; Hashimoto, Manabu; Seki, Akinobu

    1999-03-01

    In industrial plants, a remote monitoring system which removes physical tour inspection is often considered desirable. However the image sequence given from the mobile inspection robot is hard to see because interested objects are often partially occluded by obstacles such as pillars or fences. Our aim is to improve the image sequence that increases the efficiency and reliability of remote visual inspection. We propose a new depth-based image processing technique, which removes the needless objects from the foreground and recovers the occluded background electronically. Our algorithm is based on spatiotemporal analysis that enables fine and dense depth estimation, depth-based precise segmentation, and accurate interpolation. We apply this technique to a real time sequence given from the mobile inspection robot. The resulted image sequence is satisfactory in that the operator can make correct visual inspection with less fatigue.

  16. A Robust Actin Filaments Image Analysis Framework.

    PubMed

    Alioscha-Perez, Mitchel; Benadiba, Carine; Goossens, Katty; Kasas, Sandor; Dietler, Giovanni; Willaert, Ronnie; Sahli, Hichem

    2016-08-01

    The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in

  17. A Robust Actin Filaments Image Analysis Framework

    PubMed Central

    Alioscha-Perez, Mitchel; Benadiba, Carine; Goossens, Katty; Kasas, Sandor; Dietler, Giovanni; Willaert, Ronnie; Sahli, Hichem

    2016-01-01

    The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a ‘cartoon’ part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the ‘cartoon’ image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts

  18. Infrared image processing and data analysis

    NASA Astrophysics Data System (ADS)

    Ibarra-Castanedo, C.; González, D.; Klein, M.; Pilla, M.; Vallerand, S.; Maldague, X.

    2004-12-01

    Infrared thermography in nondestructive testing provides images (thermograms) in which zones of interest (defects) appear sometimes as subtle signatures. In this context, raw images are not often appropriate since most will be missed. In some other cases, what is needed is a quantitative analysis such as for defect detection and characterization. In this paper, presentation is made of various methods of data analysis required either at preprocessing and/or processing images. References from literature are provided for briefly discussed known methods while novelties are elaborated in more details within the text which include also experimental results.

  19. Linear digital imaging system fidelity analysis

    NASA Technical Reports Server (NTRS)

    Park, Stephen K.

    1989-01-01

    The combined effects of imaging gathering, sampling and reconstruction are analyzed in terms of image fidelity. The analysis is based upon a standard end-to-end linear system model which is sufficiently general so that the results apply to most line-scan and sensor-array imaging systems. Shift-variant sampling effects are accounted for with an expected value analysis based upon the use of a fixed deterministic input scene which is randomly shifted (mathematically) relative to the sampling grid. This random sample-scene phase approach has been used successfully by the author and associates in several previous related papers.

  20. 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

  1. 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. PMID:25133202

  2. Image texture analysis of crushed wheat kernels

    NASA Astrophysics Data System (ADS)

    Zayas, Inna Y.; Martin, C. R.; Steele, James L.; Dempster, Richard E.

    1992-03-01

    The development of new approaches for wheat hardness assessment may impact the grain industry in marketing, milling, and breeding. This study used image texture features for wheat hardness evaluation. Application of digital imaging to grain for grading purposes is principally based on morphometrical (shape and size) characteristics of the kernels. A composite sample of 320 kernels for 17 wheat varieties were collected after testing and crushing with a single kernel hardness characterization meter. Six wheat classes where represented: HRW, HRS, SRW, SWW, Durum, and Club. In this study, parameters which characterize texture or spatial distribution of gray levels of an image were determined and used to classify images of crushed wheat kernels. The texture parameters of crushed wheat kernel images were different depending on class, hardness and variety of the wheat. Image texture analysis of crushed wheat kernels showed promise for use in class, hardness, milling quality, and variety discrimination.

  3. Breast tomosynthesis imaging configuration analysis.

    PubMed

    Rayford, Cleveland E; Zhou, Weihua; Chen, Ying

    2013-01-01

    Traditional two-dimensional (2D) X-ray mammography is the most commonly used method for breast cancer diagnosis. Recently, a three-dimensional (3D) Digital Breast Tomosynthesis (DBT) system has been invented, which is likely to challenge the current mammography technology. The DBT system provides stunning 3D information, giving physicians increased detail of anatomical information, while reducing the chance of false negative screening. In this research, two reconstruction algorithms, Back Projection (BP) and Shift-And-Add (SAA), were used to investigate and compare View Angle (VA) and the number of projection images (N) with parallel imaging configurations. In addition, in order to better determine which method displayed better-quality imaging, Modulation Transfer Function (MTF) analyses were conducted with both algorithms, ultimately producing results which improve upon better breast cancer detection. Research studies find evidence that early detection of the disease is the best way to conquer breast cancer, and earlier detection results in the increase of life span for the affected person. PMID:23900440

  4. Breast cancer histopathology image analysis: a review.

    PubMed

    Veta, Mitko; Pluim, Josien P W; van Diest, Paul J; Viergever, Max A

    2014-05-01

    This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients. PMID:24759275

  5. Principal component analysis of scintimammographic images.

    PubMed

    Bonifazzi, Claudio; Cinti, Maria Nerina; Vincentis, Giuseppe De; Finos, Livio; Muzzioli, Valerio; Betti, Margherita; Nico, Lanconelli; Tartari, Agostino; Pani, Roberto

    2006-01-01

    The recent development of new gamma imagers based on scintillation array with high spatial resolution, has strongly improved the possibility of detecting sub-centimeter cancer in Scintimammography. However, Compton scattering contamination remains the main drawback since it limits the sensitivity of tumor detection. Principal component image analysis (PCA), recently introduced in scintimam nographic imaging, is a data reduction technique able to represent the radiation emitted from chest, breast healthy and damaged tissues as separated images. From these images a Scintimammography can be obtained where the Compton contamination is "removed". In the present paper we compared the PCA reconstructed images with the conventional scintimammographic images resulting from the photopeak (Ph) energy window. Data coming from a clinical trial were used. For both kinds of images the tumor presence was quantified by evaluating the t-student statistics for independent sample as a measure of the signal-to-noise ratio (SNR). Since the absence of Compton scattering, the PCA reconstructed images shows a better noise suppression and allows a more reliable diagnostics in comparison with the images obtained by the photopeak energy window, reducing the trend in producing false positive. PMID:17646004

  6. Image analysis in comparative genomic hybridization

    SciTech Connect

    Lundsteen, C.; Maahr, J.; Christensen, B.

    1995-01-01

    Comparative genomic hybridization (CGH) is a new technique by which genomic imbalances can be detected by combining in situ suppression hybridization of whole genomic DNA and image analysis. We have developed software for rapid, quantitative CGH image analysis by a modification and extension of the standard software used for routine karyotyping of G-banded metaphase spreads in the Magiscan chromosome analysis system. The DAPI-counterstained metaphase spread is karyotyped interactively. Corrections for image shifts between the DAPI, FITC, and TRITC images are done manually by moving the three images relative to each other. The fluorescence background is subtracted. A mean filter is applied to smooth the FITC and TRITC images before the fluorescence ratio between the individual FITC and TRITC-stained chromosomes is computed pixel by pixel inside the area of the chromosomes determined by the DAPI boundaries. Fluorescence intensity ratio profiles are generated, and peaks and valleys indicating possible gains and losses of test DNA are marked if they exceed ratios below 0.75 and above 1.25. By combining the analysis of several metaphase spreads, consistent findings of gains and losses in all or almost all spreads indicate chromosomal imbalance. Chromosomal imbalances are detected either by visual inspection of fluorescence ratio (FR) profiles or by a statistical approach that compares FR measurements of the individual case with measurements of normal chromosomes. The complete analysis of one metaphase can be carried out in approximately 10 minutes. 8 refs., 7 figs., 1 tab.

  7. Quantitative analysis of qualitative images

    NASA Astrophysics Data System (ADS)

    Hockney, David; Falco, Charles M.

    2005-03-01

    We show optical evidence that demonstrates artists as early as Jan van Eyck and Robert Campin (c1425) used optical projections as aids for producing their paintings. We also have found optical evidence within works by later artists, including Bermejo (c1475), Lotto (c1525), Caravaggio (c1600), de la Tour (c1650), Chardin (c1750) and Ingres (c1825), demonstrating a continuum in the use of optical projections by artists, along with an evolution in the sophistication of that use. However, even for paintings where we have been able to extract unambiguous, quantitative evidence of the direct use of optical projections for producing certain of the features, this does not mean that paintings are effectively photographs. Because the hand and mind of the artist are intimately involved in the creation process, understanding these complex images requires more than can be obtained from only applying the equations of geometrical optics.

  8. Hybrid µCT-FMT imaging and image analysis

    PubMed Central

    Zafarnia, Sara; Babler, Anne; Jahnen-Dechent, Willi; Lammers, Twan; Lederle, Wiltrud; Kiessling, Fabian

    2015-01-01

    Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (µCT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal µCT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints. PMID:26066033

  9. Particle Pollution Estimation Based on Image Analysis.

    PubMed

    Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

    2016-01-01

    Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction. PMID:26828757

  10. Particle Pollution Estimation Based on Image Analysis

    PubMed Central

    Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

    2016-01-01

    Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction. PMID:26828757

  11. Membrane composition analysis by imaging mass spectrometry

    SciTech Connect

    Boxer, S G; Kraft, M L; Longo, M; Hutcheon, I D; Weber, P K

    2006-03-29

    Membranes on solid supports offer an ideal format for imaging. Secondary ion mass spectrometry (SIMS) can be used to obtain composition information on membrane-associated components. Using the NanoSIMS50, images of composition variations in membrane domains can be obtained with a lateral resolution better than 100 nm. By suitable calibration, these variations in composition can be translated into a quantitative analysis of the membrane composition. Progress towards imaging small phase-separated lipid domains, membrane-associated proteins and natural biological membranes will be described.

  12. 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.

  13. Data analysis for GOPEX image frames

    NASA Technical Reports Server (NTRS)

    Levine, B. M.; Shaik, K. S.; Yan, T.-Y.

    1993-01-01

    The data analysis based on the image frames received at the Solid State Imaging (SSI) camera of the Galileo Optical Experiment (GOPEX) demonstration conducted between 9-16 Dec. 1992 is described. Laser uplink was successfully established between the ground and the Galileo spacecraft during its second Earth-gravity-assist phase in December 1992. SSI camera frames were acquired which contained images of detected laser pulses transmitted from the Table Mountain Facility (TMF), Wrightwood, California, and the Starfire Optical Range (SOR), Albuquerque, New Mexico. Laser pulse data were processed using standard image-processing techniques at the Multimission Image Processing Laboratory (MIPL) for preliminary pulse identification and to produce public release images. Subsequent image analysis corrected for background noise to measure received pulse intensities. Data were plotted to obtain histograms on a daily basis and were then compared with theoretical results derived from applicable weak-turbulence and strong-turbulence considerations. Processing steps are described and the theories are compared with the experimental results. Quantitative agreement was found in both turbulence regimes, and better agreement would have been found, given more received laser pulses. Future experiments should consider methods to reliably measure low-intensity pulses, and through experimental planning to geometrically locate pulse positions with greater certainty.

  14. VAICo: visual analysis for image comparison.

    PubMed

    Schmidt, Johanna; Gröller, M Eduard; Bruckner, Stefan

    2013-12-01

    Scientists, engineers, and analysts are confronted with ever larger and more complex sets of data, whose analysis poses special challenges. In many situations it is necessary to compare two or more datasets. Hence there is a need for comparative visualization tools to help analyze differences or similarities among datasets. In this paper an approach for comparative visualization for sets of images is presented. Well-established techniques for comparing images frequently place them side-by-side. A major drawback of such approaches is that they do not scale well. Other image comparison methods encode differences in images by abstract parameters like color. In this case information about the underlying image data gets lost. This paper introduces a new method for visualizing differences and similarities in large sets of images which preserves contextual information, but also allows the detailed analysis of subtle variations. Our approach identifies local changes and applies cluster analysis techniques to embed them in a hierarchy. The results of this process are then presented in an interactive web application which allows users to rapidly explore the space of differences and drill-down on particular features. We demonstrate the flexibility of our approach by applying it to multiple distinct domains. PMID:24051775

  15. A pairwise image analysis with sparse decomposition

    NASA Astrophysics Data System (ADS)

    Boucher, A.; Cloppet, F.; Vincent, N.

    2013-02-01

    This paper aims to detect the evolution between two images representing the same scene. The evolution detection problem has many practical applications, especially in medical images. Indeed, the concept of a patient "file" implies the joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The research presented in this paper is carried out within the application context of the development of computer assisted diagnosis (CAD) applied to mammograms. It is performed on already registered pair of images. As the registration is never perfect, we must develop a comparison method sufficiently adapted to detect real small differences between comparable tissues. In many applications, the assessment of similarity used during the registration step is also used for the interpretation step that yields to prompt suspicious regions. In our case registration is assumed to match the spatial coordinates of similar anatomical elements. In this paper, in order to process the medical images at tissue level, the image representation is based on elementary patterns, therefore seeking patterns, not pixels. Besides, as the studied images have low entropy, the decomposed signal is expressed in a parsimonious way. Parsimonious representations are known to help extract the significant structures of a signal, and generate a compact version of the data. This change of representation should allow us to compare the studied images in a short time, thanks to the low weight of the images thus represented, while maintaining a good representativeness. The good precision of our results show the approach efficiency.

  16. Image analysis of insulation mineral fibres.

    PubMed

    Talbot, H; Lee, T; Jeulin, D; Hanton, D; Hobbs, L W

    2000-12-01

    We present two methods for measuring the diameter and length of man-made vitreous fibres based on the automated image analysis of scanning electron microscopy images. The fibres we want to measure are used in materials such as glass wool, which in turn are used for thermal and acoustic insulation. The measurement of the diameters and lengths of these fibres is used by the glass wool industry for quality control purposes. To obtain reliable quality estimators, the measurement of several hundred images is necessary. These measurements are usually obtained manually by operators. Manual measurements, although reliable when performed by skilled operators, are slow due to the need for the operators to rest often to retain their ability to spot faint fibres on noisy backgrounds. Moreover, the task of measuring thousands of fibres every day, even with the help of semi-automated image analysis systems, is dull and repetitive. The need for an automated procedure which could replace manual measurements is quite real. For each of the two methods that we propose to accomplish this task, we present the sample preparation, the microscope setting and the image analysis algorithms used for the segmentation of the fibres and for their measurement. We also show how a statistical analysis of the results can alleviate most measurement biases, and how we can estimate the true distribution of fibre lengths by diameter class by measuring only the lengths of the fibres visible in the field of view. PMID:11106965

  17. Automated eXpert Spectral Image Analysis

    Energy Science and Technology Software Center (ESTSC)

    2003-11-25

    AXSIA performs automated factor analysis of hyperspectral images. In such images, a complete spectrum is collected an each point in a 1-, 2- or 3- dimensional spatial array. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful information. Multivariate factor analysis techniques have proven effective for extracting the essential information from high dimensional data sets into a limtedmore » number of factors that describe the spectral characteristics and spatial distributions of the pure components comprising the sample. AXSIA provides tools to estimate different types of factor models including Singular Value Decomposition (SVD), Principal Component Analysis (PCA), PCA with factor rotation, and Alternating Least Squares-based Multivariate Curve Resolution (MCR-ALS). As part of the analysis process, AXSIA can automatically estimate the number of pure components that comprise the data and can scale the data to account for Poisson noise. The data analysis methods are fundamentally based on eigenanalysis of the data crossproduct matrix coupled with orthogonal eigenvector rotation and constrained alternating least squares refinement. A novel method for automatically determining the number of significant components, which is based on the eigenvalues of the crossproduct matrix, has also been devised and implemented. The data can be compressed spectrally via PCA and spatially through wavelet transforms, and algorithms have been developed that perform factor analysis in the transform domain while retaining full spatial and spectral resolution in the final result. These latter innovations enable the analysis of larger-than core-memory spectrum-images. AXSIA was designed to perform automated chemical phase analysis of spectrum-images acquired by a variety of chemical imaging techniques. Successful applications include Energy Dispersive X-ray Spectroscopy, X

  18. Objective facial photograph analysis using imaging software.

    PubMed

    Pham, Annette M; Tollefson, Travis T

    2010-05-01

    Facial analysis is an integral part of the surgical planning process. Clinical photography has long been an invaluable tool in the surgeon's practice not only for accurate facial analysis but also for enhancing communication between the patient and surgeon, for evaluating postoperative results, for medicolegal documentation, and for educational and teaching opportunities. From 35-mm slide film to the digital technology of today, clinical photography has benefited greatly from technological advances. With the development of computer imaging software, objective facial analysis becomes easier to perform and less time consuming. Thus, while the original purpose of facial analysis remains the same, the process becomes much more efficient and allows for some objectivity. Although clinical judgment and artistry of technique is never compromised, the ability to perform objective facial photograph analysis using imaging software may become the standard in facial plastic surgery practices in the future. PMID:20511080

  19. Motion Analysis From Television Images

    NASA Astrophysics Data System (ADS)

    Silberberg, George G.; Keller, Patrick N.

    1982-02-01

    The Department of Defense ranges have relied on photographic instrumentation for gathering data of firings for all types of ordnance. A large inventory of cameras are available on the market that can be used for these tasks. A new set of optical instrumentation is beginning to appear which, in many cases, can directly replace photographic cameras for a great deal of the work being performed now. These are television cameras modified so they can stop motion, see in the dark, perform under hostile environments, and provide real time information. This paper discusses techniques for modifying television cameras so they can be used for motion analysis.

  20. Endoscopic image analysis in semantic space.

    PubMed

    Kwitt, R; Vasconcelos, N; Rasiwasia, N; Uhl, A; Davis, B; Häfner, M; Wrba, F

    2012-10-01

    A novel approach to the design of a semantic, low-dimensional, encoding for endoscopic imagery is proposed. This encoding is based on recent advances in scene recognition, where semantic modeling of image content has gained considerable attention over the last decade. While the semantics of scenes are mainly comprised of environmental concepts such as vegetation, mountains or sky, the semantics of endoscopic imagery are medically relevant visual elements, such as polyps, special surface patterns, or vascular structures. The proposed semantic encoding differs from the representations commonly used in endoscopic image analysis (for medical decision support) in that it establishes a semantic space, where each coordinate axis has a clear human interpretation. It is also shown to establish a connection to Riemannian geometry, which enables principled solutions to a number of problems that arise in both physician training and clinical practice. This connection is exploited by leveraging results from information geometry to solve problems such as (1) recognition of important semantic concepts, (2) semantically-focused image browsing, and (3) estimation of the average-case semantic encoding for a collection of images that share a medically relevant visual detail. The approach can provide physicians with an easily interpretable, semantic encoding of visual content, upon which further decisions, or operations, can be naturally carried out. This is contrary to the prevalent practice in endoscopic image analysis for medical decision support, where image content is primarily captured by discriminative, high-dimensional, appearance features, which possess discriminative power but lack human interpretability. PMID:22717411

  1. Endoscopic Image Analysis in Semantic Space

    PubMed Central

    Kwitt, R.; Vasconcelos, N.; Rasiwasia, N.; Uhl, A.; Davis, B.; Häfner, M.; Wrba, F.

    2013-01-01

    A novel approach to the design of a semantic, low-dimensional, encoding for endoscopic imagery is proposed. This encoding is based on recent advances in scene recognition, where semantic modeling of image content has gained considerable attention over the last decade. While the semantics of scenes are mainly comprised of environmental concepts such as vegetation, mountains or sky, the semantics of endoscopic imagery are medically relevant visual elements, such as polyps, special surface patterns, or vascular structures. The proposed semantic encoding differs from the representations commonly used in endoscopic image analysis (for medical decision support) in that it establishes a semantic space, where each coordinate axis has a clear human interpretation. It is also shown to establish a connection to Riemannian geometry, which enables principled solutions to a number of problems that arise in both physician training and clinical practice. This connection is exploited by leveraging results from information geometry to solve problems such as 1) recognition of important semantic concepts, 2) semantically-focused image browsing, and 3) estimation of the average-case semantic encoding for a collection of images that share a medically relevant visual detail. The approach can provide physicians with an easily interpretable, semantic encoding of visual content, upon which further decisions, or operations, can be naturally carried out. This is contrary to the prevalent practice in endoscopic image analysis for medical decision support, where image content is primarily captured by discriminative, high-dimensional, appearance features, which possess discriminative power but lack human interpretability. PMID:22717411

  2. Analysis of an interferometric Stokes imaging polarimeter

    NASA Astrophysics Data System (ADS)

    Murali, Sukumar

    Estimation of Stokes vector components from an interferometric fringe encoded image is a novel way of measuring the State Of Polarization (SOP) distribution across a scene. Imaging polarimeters employing interferometric techniques encode SOP in- formation across a scene in a single image in the form of intensity fringes. The lack of moving parts and use of a single image eliminates the problems of conventional polarimetry - vibration, spurious signal generation due to artifacts, beam wander, and need for registration routines. However, interferometric polarimeters are limited by narrow bandpass and short exposure time operations which decrease the Signal to Noise Ratio (SNR) defined as the ratio of the mean photon count to the standard deviation in the detected image. A simulation environment for designing an Interferometric Stokes Imaging polarimeter (ISIP) and a detector with noise effects is created and presented. Users of this environment are capable of imaging an object with defined SOP through an ISIP onto a detector producing a digitized image output. The simulation also includes bandpass imaging capabilities, control of detector noise, and object brightness levels. The Stokes images are estimated from a fringe encoded image of a scene by means of a reconstructor algorithm. A spatial domain methodology involving the idea of a unit cell and slide approach is applied to the reconstructor model developed using Mueller calculus. The validation of this methodology and effectiveness compared to a discrete approach is demonstrated with suitable examples. The pixel size required to sample the fringes and minimum unit cell size required for reconstruction are investigated using condition numbers. The importance of the PSF of fore-optics (telescope) used in imaging the object is investigated and analyzed using a point source imaging example and a Nyquist criteria is presented. Reconstruction of fringe modulated images in the presence of noise involves choosing an

  3. Unsupervised hyperspectral image analysis using independent component analysis (ICA)

    SciTech Connect

    S. S. Chiang; I. W. Ginsberg

    2000-06-30

    In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.

  4. Curvelet Based Offline Analysis of SEM Images

    PubMed Central

    Shirazi, Syed Hamad; Haq, Nuhman ul; Hayat, Khizar; Naz, Saeeda; Haque, Ihsan ul

    2014-01-01

    Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm. PMID:25089617

  5. Medical image analysis with artificial neural networks.

    PubMed

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. PMID:20713305

  6. 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.

  7. Measuring toothbrush interproximal penetration using image analysis

    NASA Astrophysics Data System (ADS)

    Hayworth, Mark S.; Lyons, Elizabeth K.

    1994-09-01

    An image analysis method of measuring the effectiveness of a toothbrush in reaching the interproximal spaces of teeth is described. Artificial teeth are coated with a stain that approximates real plaque and then brushed with a toothbrush on a brushing machine. The teeth are then removed and turned sideways so that the interproximal surfaces can be imaged. The areas of stain that have been removed within masked regions that define the interproximal regions are measured and reported. These areas correspond to the interproximal areas of the tooth reached by the toothbrush bristles. The image analysis method produces more precise results (10-fold decrease in standard deviation) in a fraction (22%) of the time as compared to our prior visual grading method.

  8. COMPUTER ANALYSIS OF PLANAR GAMMA CAMERA IMAGES

    EPA Science Inventory



    COMPUTER ANALYSIS OF PLANAR GAMMA CAMERA IMAGES

    T Martonen1 and J Schroeter2

    1Experimental Toxicology Division, National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, NC 27711 USA and 2Curriculum in Toxicology, Unive...

  9. Scale Free Reduced Rank Image Analysis.

    ERIC Educational Resources Information Center

    Horst, Paul

    In the traditional Guttman-Harris type image analysis, a transformation is applied to the data matrix such that each column of the transformed data matrix is the best least squares estimate of the corresponding column of the data matrix from the remaining columns. The model is scale free. However, it assumes (1) that the correlation matrix is…

  10. Using Image Analysis to Build Reading Comprehension

    ERIC Educational Resources Information Center

    Brown, Sarah Drake; Swope, John

    2010-01-01

    Content area reading remains a primary concern of history educators. In order to better prepare students for encounters with text, the authors propose the use of two image analysis strategies tied with a historical theme to heighten student interest in historical content and provide a basis for improved reading comprehension.

  11. Morphometry of spermatozoa using semiautomatic image analysis.

    PubMed Central

    Jagoe, J R; Washbrook, N P; Hudson, E A

    1986-01-01

    Human sperm heads were detected and tracked using semiautomatic image analysis. Measurements of size and shape on two specimens from each of 26 men showed that the major component of variability both within and between subjects was the number of small elongated sperm heads. Variability of the computed features between subjects was greater than that between samples from the same subject. PMID:3805320

  12. Expert system for imaging spectrometer analysis results

    NASA Technical Reports Server (NTRS)

    Borchardt, Gary C.

    1985-01-01

    Information on an expert system for imaging spectrometer analysis results is outlined. Implementation requirements, the Simple Tool for Automated Reasoning (STAR) program that provides a software environment for the development and operation of rule-based expert systems, STAR data structures, and rule-based identification of surface materials are among the topics outlined.

  13. Good relationships between computational image analysis and radiological physics

    SciTech Connect

    Arimura, Hidetaka; Kamezawa, Hidemi; Jin, Ze; Nakamoto, Takahiro; Soufi, Mazen

    2015-09-30

    Good relationships between computational image analysis and radiological physics have been constructed for increasing the accuracy of medical diagnostic imaging and radiation therapy in radiological physics. Computational image analysis has been established based on applied mathematics, physics, and engineering. This review paper will introduce how computational image analysis is useful in radiation therapy with respect to radiological physics.

  14. Good relationships between computational image analysis and radiological physics

    NASA Astrophysics Data System (ADS)

    Arimura, Hidetaka; Kamezawa, Hidemi; Jin, Ze; Nakamoto, Takahiro; Soufi, Mazen

    2015-09-01

    Good relationships between computational image analysis and radiological physics have been constructed for increasing the accuracy of medical diagnostic imaging and radiation therapy in radiological physics. Computational image analysis has been established based on applied mathematics, physics, and engineering. This review paper will introduce how computational image analysis is useful in radiation therapy with respect to radiological physics.

  15. Frequency domain analysis of knock images

    NASA Astrophysics Data System (ADS)

    Qi, Yunliang; He, Xin; Wang, Zhi; Wang, Jianxin

    2014-12-01

    High speed imaging-based knock analysis has mainly focused on time domain information, e.g. the spark triggered flame speed, the time when end gas auto-ignition occurs and the end gas flame speed after auto-ignition. This study presents a frequency domain analysis on the knock images recorded using a high speed camera with direct photography in a rapid compression machine (RCM). To clearly visualize the pressure wave oscillation in the combustion chamber, the images were high-pass-filtered to extract the luminosity oscillation. The luminosity spectrum was then obtained by applying fast Fourier transform (FFT) to three basic colour components (red, green and blue) of the high-pass-filtered images. Compared to the pressure spectrum, the luminosity spectra better identify the resonant modes of pressure wave oscillation. More importantly, the resonant mode shapes can be clearly visualized by reconstructing the images based on the amplitudes of luminosity spectra at the corresponding resonant frequencies, which agree well with the analytical solutions for mode shapes of gas vibration in a cylindrical cavity.

  16. ImageJ: Image processing and analysis in Java

    NASA Astrophysics Data System (ADS)

    Rasband, W. S.

    2012-06-01

    ImageJ is a public domain Java image processing program inspired by NIH Image. It can display, edit, analyze, process, save and print 8-bit, 16-bit and 32-bit images. It can read many image formats including TIFF, GIF, JPEG, BMP, DICOM, FITS and "raw". It supports "stacks", a series of images that share a single window. It is multithreaded, so time-consuming operations such as image file reading can be performed in parallel with other operations.

  17. Multiscale likelihood analysis and image reconstruction

    NASA Astrophysics Data System (ADS)

    Willett, Rebecca M.; Nowak, Robert D.

    2003-11-01

    The nonparametric multiscale polynomial and platelet methods presented here are powerful new tools for signal and image denoising and reconstruction. Unlike traditional wavelet-based multiscale methods, these methods are both well suited to processing Poisson or multinomial data and capable of preserving image edges. At the heart of these new methods lie multiscale signal decompositions based on polynomials in one dimension and multiscale image decompositions based on what the authors call platelets in two dimensions. Platelets are localized functions at various positions, scales and orientations that can produce highly accurate, piecewise linear approximations to images consisting of smooth regions separated by smooth boundaries. Polynomial and platelet-based maximum penalized likelihood methods for signal and image analysis are both tractable and computationally efficient. Polynomial methods offer near minimax convergence rates for broad classes of functions including Besov spaces. Upper bounds on the estimation error are derived using an information-theoretic risk bound based on squared Hellinger loss. Simulations establish the practical effectiveness of these methods in applications such as density estimation, medical imaging, and astronomy.

  18. Recent advances in morphological cell image analysis.

    PubMed

    Chen, Shengyong; Zhao, Mingzhu; Wu, Guang; Yao, Chunyan; Zhang, Jianwei

    2012-01-01

    This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed. PMID:22272215

  19. Analysis of imaging system performance capabilities

    NASA Astrophysics Data System (ADS)

    Haim, Harel; Marom, Emanuel

    2013-06-01

    Present performance analysis of optical imaging systems based on results obtained with classic one-dimensional (1D) resolution targets (such as the USAF resolution chart) are significantly different than those obtained with a newly proposed 2D target [1]. We hereby prove such claim and show how the novel 2D target should be used for correct characterization of optical imaging systems in terms of resolution and contrast. We apply thereafter the consequences of these observations on the optimal design of some two-dimensional barcode structures.

  20. Autonomous Image Analysis for Future Mars Missions

    NASA Technical Reports Server (NTRS)

    Gulick, V. C.; Morris, R. L.; Ruzon, M. A.; Bandari, E.; Roush, T. L.

    1999-01-01

    To explore high priority landing sites and to prepare for eventual human exploration, future Mars missions will involve rovers capable of traversing tens of kilometers. However, the current process by which scientists interact with a rover does not scale to such distances. Specifically, numerous command cycles are required to complete even simple tasks, such as, pointing the spectrometer at a variety of nearby rocks. In addition, the time required by scientists to interpret image data before new commands can be given and the limited amount of data that can be downlinked during a given command cycle constrain rover mobility and achievement of science goals. Experience with rover tests on Earth supports these concerns. As a result, traverses to science sites as identified in orbital images would require numerous science command cycles over a period of many weeks, months or even years, perhaps exceeding rover design life and other constraints. Autonomous onboard science analysis can address these problems in two ways. First, it will allow the rover to preferentially transmit "interesting" images, defined as those likely to have higher science content. Second, the rover will be able to anticipate future commands. For example, a rover might autonomously acquire and return spectra of "interesting" rocks along with a high-resolution image of those rocks in addition to returning the context images in which they were detected. Such approaches, coupled with appropriate navigational software, help to address both the data volume and command cycle bottlenecks that limit both rover mobility and science yield. We are developing fast, autonomous algorithms to enable such intelligent on-board decision making by spacecraft. Autonomous algorithms developed to date have the ability to identify rocks and layers in a scene, locate the horizon, and compress multi-spectral image data. We are currently investigating the possibility of reconstructing a 3D surface from a sequence of images

  1. Morphological analysis of infrared images for waterjets

    NASA Astrophysics Data System (ADS)

    Gong, Yuxin; Long, Aifang

    2013-03-01

    High-speed waterjet has been widely used in industries and been investigated as a model of free shearing turbulence. This paper presents an investigation involving the flow visualization of high speed water jet, the noise reduction of the raw thermogram using a high-pass morphological filter ? and a median filter; the image enhancement using white top-hat filter; and the image segmentation using the multiple thresholding method. The image processing results by the designed morphological filters, ? - top-hat, were proved being ideal for further quantitative and in-depth analysis and can be used as a new morphological filter bank that may be of general implications for the analogous work

  2. Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox

    PubMed Central

    Lacerda, Luis Miguel; Ferreira, Hugo Alexandre

    2015-01-01

    Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods. The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19–73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results. It was observed both a high inter-hemispheric symmetry

  3. Pain related inflammation analysis using infrared images

    NASA Astrophysics Data System (ADS)

    Bhowmik, Mrinal Kanti; Bardhan, Shawli; Das, Kakali; Bhattacharjee, Debotosh; Nath, Satyabrata

    2016-05-01

    Medical Infrared Thermography (MIT) offers a potential non-invasive, non-contact and radiation free imaging modality for assessment of abnormal inflammation having pain in the human body. The assessment of inflammation mainly depends on the emission of heat from the skin surface. Arthritis is a disease of joint damage that generates inflammation in one or more anatomical joints of the body. Osteoarthritis (OA) is the most frequent appearing form of arthritis, and rheumatoid arthritis (RA) is the most threatening form of them. In this study, the inflammatory analysis has been performed on the infrared images of patients suffering from RA and OA. For the analysis, a dataset of 30 bilateral knee thermograms has been captured from the patient of RA and OA by following a thermogram acquisition standard. The thermograms are pre-processed, and areas of interest are extracted for further processing. The investigation of the spread of inflammation is performed along with the statistical analysis of the pre-processed thermograms. The objectives of the study include: i) Generation of a novel thermogram acquisition standard for inflammatory pain disease ii) Analysis of the spread of the inflammation related to RA and OA using K-means clustering. iii) First and second order statistical analysis of pre-processed thermograms. The conclusion reflects that, in most of the cases, RA oriented inflammation affects bilateral knees whereas inflammation related to OA present in the unilateral knee. Also due to the spread of inflammation in OA, contralateral asymmetries are detected through the statistical analysis.

  4. Image analysis for measuring rod network properties

    NASA Astrophysics Data System (ADS)

    Kim, Dongjae; Choi, Jungkyu; Nam, Jaewook

    2015-12-01

    In recent years, metallic nanowires have been attracting significant attention as next-generation flexible transparent conductive films. The performance of films depends on the network structure created by nanowires. Gaining an understanding of their structure, such as connectivity, coverage, and alignment of nanowires, requires the knowledge of individual nanowires inside the microscopic images taken from the film. Although nanowires are flexible up to a certain extent, they are usually depicted as rigid rods in many analysis and computational studies. Herein, we propose a simple and straightforward algorithm based on the filtering in the frequency domain for detecting the rod-shape objects inside binary images. The proposed algorithm uses a specially designed filter in the frequency domain to detect image segments, namely, the connected components aligned in a certain direction. Those components are post-processed to be combined under a given merging rule in a single rod object. In this study, the microscopic properties of the rod networks relevant to the analysis of nanowire networks were measured for investigating the opto-electric performance of transparent conductive films and their alignment distribution, length distribution, and area fraction. To verify and find the optimum parameters for the proposed algorithm, numerical experiments were performed on synthetic images with predefined properties. By selecting proper parameters, the algorithm was used to investigate silver nanowire transparent conductive films fabricated by the dip coating method.

  5. Scalable histopathological image analysis via active learning.

    PubMed

    Zhu, Yan; Zhang, Shaoting; Liu, Wei; Metaxas, Dimitris N

    2014-01-01

    Training an effective and scalable system for medical image analysis usually requires a large amount of labeled data, which incurs a tremendous annotation burden for pathologists. Recent progress in active learning can alleviate this issue, leading to a great reduction on the labeling cost without sacrificing the predicting accuracy too much. However, most existing active learning methods disregard the "structured information" that may exist in medical images (e.g., data from individual patients), and make a simplifying assumption that unlabeled data is independently and identically distributed. Both may not be suitable for real-world medical images. In this paper, we propose a novel batch-mode active learning method which explores and leverages such structured information in annotations of medical images to enforce diversity among the selected data, therefore maximizing the information gain. We formulate the active learning problem as an adaptive submodular function maximization problem subject to a partition matroid constraint, and further present an efficient greedy algorithm to achieve a good solution with a theoretically proven bound. We demonstrate the efficacy of our algorithm on thousands of histopathological images of breast microscopic tissues. PMID:25320821

  6. Multiresolution simulated annealing for brain image analysis

    NASA Astrophysics Data System (ADS)

    Loncaric, Sven; Majcenic, Zoran

    1999-05-01

    Analysis of biomedical images is an important step in quantification of various diseases such as human spontaneous intracerebral brain hemorrhage (ICH). In particular, the study of outcome in patients having ICH requires measurements of various ICH parameters such as hemorrhage volume and their change over time. A multiresolution probabilistic approach for segmentation of CT head images is presented in this work. This method views the segmentation problem as a pixel labeling problem. In this application the labels are: background, skull, brain tissue, and ICH. The proposed method is based on the Maximum A-Posteriori (MAP) estimation of the unknown pixel labels. The MAP method maximizes the a-posterior probability of segmented image given the observed (input) image. Markov random field (MRF) model has been used for the posterior distribution. The MAP estimation of the segmented image has been determined using the simulated annealing (SA) algorithm. The SA algorithm is used to minimize the energy function associated with MRF posterior distribution function. A multiresolution SA (MSA) has been developed to speed up the annealing process. MSA is presented in detail in this work. A knowledge-based classification based on the brightness, size, shape and relative position toward other regions is performed at the end of the procedure. The regions are identified as background, skull, brain, ICH and calcifications.

  7. The synthesis and analysis of color images

    NASA Technical Reports Server (NTRS)

    Wandell, Brian A.

    1987-01-01

    A method is described for performing the synthesis and analysis of digital color images. The method is based on two principles. First, image data are represented with respect to the separate physical factors, surface reflectance and the spectral power distribution of the ambient light, that give rise to the perceived color of an object. Second, the encoding is made efficiently by using a basis expansion for the surface spectral reflectance and spectral power distribution of the ambient light that takes advantage of the high degree of correlation across the visible wavelengths normally found in such functions. Within this framework, the same basic methods can be used to synthesize image data for color display monitors and printed materials, and to analyze image data into estimates of the spectral power distribution and surface spectral reflectances. The method can be applied to a variety of tasks. Examples of applications include the color balancing of color images, and the identification of material surface spectral reflectance when the lighting cannot be completely controlled.

  8. The synthesis and analysis of color images

    NASA Technical Reports Server (NTRS)

    Wandell, B. A.

    1985-01-01

    A method is described for performing the synthesis and analysis of digital color images. The method is based on two principles. First, image data are represented with respect to the separate physical factors, surface reflectance and the spectral power distribution of the ambient light, that give rise to the perceived color of an object. Second, the encoding is made efficient by using a basis expansion for the surface spectral reflectance and spectral power distribution of the ambient light that takes advantage of the high degree of correlation across the visible wavelengths normally found in such functions. Within this framework, the same basic methods can be used to synthesize image data for color display monitors and printed materials, and to analyze image data into estimates of the spectral power distribution and surface spectral reflectances. The method can be applied to a variety of tasks. Examples of applications include the color balancing of color images, and the identification of material surface spectral reflectance when the lighting cannot be completely controlled.

  9. Quantitative image analysis of celiac disease

    PubMed Central

    Ciaccio, Edward J; Bhagat, Govind; Lewis, Suzanne K; Green, Peter H

    2015-01-01

    We outline the use of quantitative techniques that are currently used for analysis of celiac disease. Image processing techniques can be useful to statistically analyze the pixular data of endoscopic images that is acquired with standard or videocapsule endoscopy. It is shown how current techniques have evolved to become more useful for gastroenterologists who seek to understand celiac disease and to screen for it in suspected patients. New directions for focus in the development of methodology for diagnosis and treatment of this disease are suggested. It is evident that there are yet broad areas where there is potential to expand the use of quantitative techniques for improved analysis in suspected or known celiac disease patients. PMID:25759524

  10. Imaging Brain Dynamics Using Independent Component Analysis

    PubMed Central

    Jung, Tzyy-Ping; Makeig, Scott; McKeown, Martin J.; Bell, Anthony J.; Lee, Te-Won; Sejnowski, Terrence J.

    2010-01-01

    The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain. PMID:20824156

  11. Theoretical analysis of multispectral image segmentation criteria.

    PubMed

    Kerfoot, I B; Bresler, Y

    1999-01-01

    Markov random field (MRF) image segmentation algorithms have been extensively studied, and have gained wide acceptance. However, almost all of the work on them has been experimental. This provides a good understanding of the performance of existing algorithms, but not a unified explanation of the significance of each component. To address this issue, we present a theoretical analysis of several MRF image segmentation criteria. Standard methods of signal detection and estimation are used in the theoretical analysis, which quantitatively predicts the performance at realistic noise levels. The analysis is decoupled into the problems of false alarm rate, parameter selection (Neyman-Pearson and receiver operating characteristics), detection threshold, expected a priori boundary roughness, and supervision. Only the performance inherent to a criterion, with perfect global optimization, is considered. The analysis indicates that boundary and region penalties are very useful, while distinct-mean penalties are of questionable merit. Region penalties are far more important for multispectral segmentation than for greyscale. This observation also holds for Gauss-Markov random fields, and for many separable within-class PDFs. To validate the analysis, we present optimization algorithms for several criteria. Theoretical and experimental results agree fairly well. PMID:18267494

  12. Multispectral laser imaging for advanced food analysis

    NASA Astrophysics Data System (ADS)

    Senni, L.; Burrascano, P.; Ricci, M.

    2016-07-01

    A hardware-software apparatus for food inspection capable of realizing multispectral NIR laser imaging at four different wavelengths is herein discussed. The system was designed to operate in a through-transmission configuration to detect the presence of unwanted foreign bodies inside samples, whether packed or unpacked. A modified Lock-In technique was employed to counterbalance the significant signal intensity attenuation due to transmission across the sample and to extract the multispectral information more efficiently. The NIR laser wavelengths used to acquire the multispectral images can be varied to deal with different materials and to focus on specific aspects. In the present work the wavelengths were selected after a preliminary analysis to enhance the image contrast between foreign bodies and food in the sample, thus identifying the location and nature of the defects. Experimental results obtained from several specimens, with and without packaging, are presented and the multispectral image processing as well as the achievable spatial resolution of the system are discussed.

  13. Image analysis of Renaissance copperplate prints

    NASA Astrophysics Data System (ADS)

    Hedges, S. Blair

    2008-02-01

    From the fifteenth to the nineteenth centuries, prints were a common form of visual communication, analogous to photographs. Copperplate prints have many finely engraved black lines which were used to create the illusion of continuous tone. Line densities generally are 100-2000 lines per square centimeter and a print can contain more than a million total engraved lines 20-300 micrometers in width. Because hundreds to thousands of prints were made from a single copperplate over decades, variation among prints can have historical value. The largest variation is plate-related, which is the thinning of lines over successive editions as a result of plate polishing to remove time-accumulated corrosion. Thinning can be quantified with image analysis and used to date undated prints and books containing prints. Print-related variation, such as over-inking of the print, is a smaller but significant source. Image-related variation can introduce bias if images were differentially illuminated or not in focus, but improved imaging technology can limit this variation. The Print Index, the percentage of an area composed of lines, is proposed as a primary measure of variation. Statistical methods also are proposed for comparing and identifying prints in the context of a print database.

  14. Nursing image: an evolutionary concept analysis.

    PubMed

    Rezaei-Adaryani, Morteza; Salsali, Mahvash; Mohammadi, Eesa

    2012-12-01

    A long-term challenge to the nursing profession is the concept of image. In this study, we used the Rodgers' evolutionary concept analysis approach to analyze the concept of nursing image (NI). The aim of this concept analysis was to clarify the attributes, antecedents, consequences, and implications associated with the concept. We performed an integrative internet-based literature review to retrieve English literature published from 1980-2011. Findings showed that NI is a multidimensional, all-inclusive, paradoxical, dynamic, and complex concept. The media, invisibility, clothing style, nurses' behaviors, gender issues, and professional organizations are the most important antecedents of the concept. We found that NI is pivotal in staff recruitment and nursing shortage, resource allocation to nursing, nurses' job performance, workload, burnout and job dissatisfaction, violence against nurses, public trust, and salaries available to nurses. An in-depth understanding of the NI concept would assist nurses to eliminate negative stereotypes and build a more professional image for the nurse and the profession. PMID:23343236

  15. Shannon information and ROC analysis in imaging.

    PubMed

    Clarkson, Eric; Cushing, Johnathan B

    2015-07-01

    Shannon information (SI) and the ideal-observer receiver operating characteristic (ROC) curve are two different methods for analyzing the performance of an imaging system for a binary classification task, such as the detection of a variable signal embedded within a random background. In this work we describe a new ROC curve, the Shannon information receiver operator curve (SIROC), that is derived from the SI expression for a binary classification task. We then show that the ideal-observer ROC curve and the SIROC have many properties in common, and are equivalent descriptions of the optimal performance of an observer on the task. This equivalence is described mathematically by an integral transform that maps the ideal-observer ROC curve onto the SIROC. This then leads to an integral transform relating the minimum probability of error, as a function of the odds against a signal, to the conditional entropy, as a function of the same variable. This last relation then gives us the complete mathematical equivalence between ideal-observer ROC analysis and SI analysis of the classification task for a given imaging system. We also find that there is a close relationship between the area under the ideal-observer ROC curve, which is often used as a figure of merit for imaging systems and the area under the SIROC. Finally, we show that the relationships between the two curves result in new inequalities relating SI to ROC quantities for the ideal observer. PMID:26367158

  16. Simple Low Level Features for Image Analysis

    NASA Astrophysics Data System (ADS)

    Falcoz, Paolo

    As human beings, we perceive the world around us mainly through our eyes, and give what we see the status of “reality”; as such we historically tried to create ways of recording this reality so we could augment or extend our memory. From early attempts in photography like the image produced in 1826 by the French inventor Nicéphore Niépce (Figure 2.1) to the latest high definition camcorders, the number of recorded pieces of reality increased exponentially, posing the problem of managing all that information. Most of the raw video material produced today has lost its memory augmentation function, as it will hardly ever be viewed by any human; pervasive CCTVs are an example. They generate an enormous amount of data each day, but there is not enough “human processing power” to view them. Therefore the need for effective automatic image analysis tools is great, and a lot effort has been put in it, both from the academia and the industry. In this chapter, a review of some of the most important image analysis tools are presented.

  17. Wavelet-based image analysis system for soil texture analysis

    NASA Astrophysics Data System (ADS)

    Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

    2003-05-01

    Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

  18. PAMS photo image retrieval prototype alternatives analysis

    SciTech Connect

    Conner, M.L.

    1996-04-30

    Photography and Audiovisual Services uses a system called the Photography and Audiovisual Management System (PAMS) to perform order entry and billing services. The PAMS system utilizes Revelation Technologies database management software, AREV. Work is currently in progress to link the PAMS AREV system to a Microsoft SQL Server database engine to provide photograph indexing and query capabilities. The link between AREV and SQLServer will use a technique called ``bonding.`` This photograph imaging subsystem will interface to the PAMS system and handle the image capture and retrieval portions of the project. The intent of this alternatives analysis is to examine the software and hardware alternatives available to meet the requirements for this project, and identify a cost-effective solution.

  19. Uses of software in digital image analysis: a forensic report

    NASA Astrophysics Data System (ADS)

    Sharma, Mukesh; Jha, Shailendra

    2010-02-01

    Forensic image analysis is required an expertise to interpret the content of an image or the image itself in legal matters. Major sub-disciplines of forensic image analysis with law enforcement applications include photo-grammetry, photographic comparison, content analysis and image authentication. It has wide applications in forensic science range from documenting crime scenes to enhancing faint or indistinct patterns such as partial fingerprints. The process of forensic image analysis can involve several different tasks, regardless of the type of image analysis performed. Through this paper authors have tried to explain these tasks, which are described in to three categories: Image Compression, Image Enhancement & Restoration and Measurement Extraction. With the help of examples like signature comparison, counterfeit currency comparison and foot-wear sole impression using the software Canvas and Corel Draw.

  20. Analysis of katabatic flow using infrared imaging

    NASA Astrophysics Data System (ADS)

    Grudzielanek, M.; Cermak, J.

    2013-12-01

    We present a novel high-resolution IR method which is developed, tested and used for the analysis of katabatic flow. Modern thermal imaging systems allow for the recording of infrared picture sequences and thus the monitoring and analysis of dynamic processes. In order to identify, visualize and analyze dynamic air flow using infrared imaging, a highly reactive 'projection' surface is needed along the air flow. Here, a design for these types of analysis is proposed and evaluated. Air flow situations with strong air temperature gradients and fluctuations, such as katabatic flow, are particularly suitable for this new method. The method is applied here to analyze nocturnal cold air flows on gentle slopes. In combination with traditional methods the vertical and temporal dynamics of cold air flow are analyzed. Several assumptions on cold air flow dynamics can be confirmed explicitly for the first time. By observing the cold air flow in terms of frequency, size and period of the cold air fluctuations, drops are identified and organized in a newly derived classification system of cold air flow phases. In addition, new flow characteristics are detected, like sharp cold air caps and turbulence inside the drops. Vertical temperature gradients inside cold air drops and their temporal evolution are presented in high resolution Hovmöller-type diagrams and sequenced time lapse infrared videos.

  1. Research on automatic human chromosome image analysis

    NASA Astrophysics Data System (ADS)

    Ming, Delie; Tian, Jinwen; Liu, Jian

    2007-11-01

    Human chromosome karyotyping is one of the essential tasks in cytogenetics, especially in genetic syndrome diagnoses. In this thesis, an automatic procedure is introduced for human chromosome image analysis. According to different status of touching and overlapping chromosomes, several segmentation methods are proposed to achieve the best results. Medial axis is extracted by the middle point algorithm. Chromosome band is enhanced by the algorithm based on multiscale B-spline wavelets, extracted by average gray profile, gradient profile and shape profile, and calculated by the WDD (Weighted Density Distribution) descriptors. The multilayer classifier is used in classification. Experiment results demonstrate that the algorithms perform well.

  2. Machine learning for medical images analysis.

    PubMed

    Criminisi, A

    2016-10-01

    This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. PMID:27374127

  3. Image reconstruction from Pulsed Fast Neutron Analysis

    NASA Astrophysics Data System (ADS)

    Bendahan, Joseph; Feinstein, Leon; Keeley, Doug; Loveman, Rob

    1999-06-01

    Pulsed Fast Neutron Analysis (PFNA) has been demonstrated to detect drugs and explosives in trucks and large cargo containers. PFNA uses a collimated beam of nanosecond-pulsed fast neutrons that interact with the cargo contents to produce gamma rays characteristic to their elemental composition. By timing the arrival of the emitted radiation to an array of gamma-ray detectors a three-dimensional elemental density map or image of the cargo is created. The process to determine the elemental densities is complex and requires a number of steps. The first step consists of extracting from the characteristic gamma-ray spectra the counts associated with the elements of interest. Other steps are needed to correct for physical quantities such as gamma-ray production cross sections and angular distributions. The image processing includes also phenomenological corrections that take into account the neutron attenuation through the cargo, and the attenuation of the gamma rays from the point they were generated to the gamma-ray detectors. Additional processing is required to map the elemental densities from the data acquisition system of coordinates to a rectilinear system. This paper describes the image processing used to compute the elemental densities from the counts observed in the gamma-ray detectors.

  4. Image reconstruction from Pulsed Fast Neutron Analysis

    SciTech Connect

    Bendahan, Joseph; Feinstein, Leon; Keeley, Doug; Loveman, Rob

    1999-06-10

    Pulsed Fast Neutron Analysis (PFNA) has been demonstrated to detect drugs and explosives in trucks and large cargo containers. PFNA uses a collimated beam of nanosecond-pulsed fast neutrons that interact with the cargo contents to produce gamma rays characteristic to their elemental composition. By timing the arrival of the emitted radiation to an array of gamma-ray detectors a three-dimensional elemental density map or image of the cargo is created. The process to determine the elemental densities is complex and requires a number of steps. The first step consists of extracting from the characteristic gamma-ray spectra the counts associated with the elements of interest. Other steps are needed to correct for physical quantities such as gamma-ray production cross sections and angular distributions. The image processing includes also phenomenological corrections that take into account the neutron attenuation through the cargo, and the attenuation of the gamma rays from the point they were generated to the gamma-ray detectors. Additional processing is required to map the elemental densities from the data acquisition system of coordinates to a rectilinear system. This paper describes the image processing used to compute the elemental densities from the counts observed in the gamma-ray detectors.

  5. Soil Surface Roughness through Image Analysis

    NASA Astrophysics Data System (ADS)

    Tarquis, A. M.; Saa-Requejo, A.; Valencia, J. L.; Moratiel, R.; Paz-Gonzalez, A.; Agro-Environmental Modeling

    2011-12-01

    Soil erosion is a complex phenomenon involving the detachment and transport of soil particles, storage and runoff of rainwater, and infiltration. The relative magnitude and importance of these processes depends on several factors being one of them surface micro-topography, usually quantified trough soil surface roughness (SSR). SSR greatly affects surface sealing and runoff generation, yet little information is available about the effect of roughness on the spatial distribution of runoff and on flow concentration. The methods commonly used to measure SSR involve measuring point elevation using a pin roughness meter or laser, both of which are labor intensive and expensive. Lately a simple and inexpensive technique based on percentage of shadow in soil surface image has been developed to determine SSR in the field in order to obtain measurement for wide spread application. One of the first steps in this technique is image de-noising and thresholding to estimate the percentage of black pixels in the studied area. In this work, a series of soil surface images have been analyzed applying several de-noising wavelet analysis and thresholding algorithms to study the variation in percentage of shadows and the shadows size distribution. Funding provided by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no. AGL2010- 21501/AGR and by Xunta de Galicia through project no INCITE08PXIB1621 are greatly appreciated.

  6. Noise analysis in laser speckle contrast imaging

    NASA Astrophysics Data System (ADS)

    Yuan, Shuai; Chen, Yu; Dunn, Andrew K.; Boas, David A.

    2010-02-01

    Laser speckle contrast imaging (LSCI) is becoming an established method for full-field imaging of blood flow dynamics in animal models. A reliable quantitative model with comprehensive noise analysis is necessary to fully utilize this technique in biomedical applications and clinical trials. In this study, we investigated several major noise sources in LSCI: periodic physiology noise, shot noise and statistical noise. (1) We observed periodic physiology noise in our experiments and found that its sources consist principally of motions induced by heart beats and/or ventilation. (2) We found that shot noise caused an offset of speckle contrast (SC) values, and this offset is directly related to the incident light intensity. (3) A mathematical model of statistical noise was also developed. The model indicated that statistical noise in speckle contrast imaging is related to the SC values and the total number of pixels used in the SC calculation. Our experimental results are consistent with theoretical predications, as well as with other published works.

  7. Difference Image Analysis of Galactic Microlensing. I. Data Analysis

    SciTech Connect

    Alcock, C.; Allsman, R. A.; Alves, D.; Axelrod, T. S.; Becker, A. C.; Bennett, D. P.; Cook, K. H.; Drake, A. J.; Freeman, K. C.; Griest, K.

    1999-08-20

    This is a preliminary report on the application of Difference Image Analysis (DIA) to Galactic bulge images. The aim of this analysis is to increase the sensitivity to the detection of gravitational microlensing. We discuss how the DIA technique simplifies the process of discovering microlensing events by detecting only objects that have variable flux. We illustrate how the DIA technique is not limited to detection of so-called ''pixel lensing'' events but can also be used to improve photometry for classical microlensing events by removing the effects of blending. We will present a method whereby DIA can be used to reveal the true unblended colors, positions, and light curves of microlensing events. We discuss the need for a technique to obtain the accurate microlensing timescales from blended sources and present a possible solution to this problem using the existing Hubble Space Telescope color-magnitude diagrams of the Galactic bulge and LMC. The use of such a solution with both classical and pixel microlensing searches is discussed. We show that one of the major causes of systematic noise in DIA is differential refraction. A technique for removing this systematic by effectively registering images to a common air mass is presented. Improvements to commonly used image differencing techniques are discussed. (c) 1999 The American Astronomical Society.

  8. Analysis of image quality based on perceptual preference

    NASA Astrophysics Data System (ADS)

    Xue, Liqin; Hua, Yuning; Zhao, Guangzhou; Qi, Yaping

    2007-11-01

    This paper deals with image quality analysis considering the impact of psychological factors involved in assessment. The attributes of image quality requirement were partitioned according to the visual perception characteristics and the preference of image quality were obtained by the factor analysis method. The features of image quality which support the subjective preference were identified, The adequacy of image is evidenced to be the top requirement issues to the display image quality improvement. The approach will be beneficial to the research of the image quality subjective quantitative assessment method.

  9. Wavelet Analysis of Space Solar Telescope Images

    NASA Astrophysics Data System (ADS)

    Zhu, Xi-An; Jin, Sheng-Zhen; Wang, Jing-Yu; Ning, Shu-Nian

    2003-12-01

    The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

  10. The Scientific Image in Behavior Analysis.

    PubMed

    Keenan, Mickey

    2016-05-01

    Throughout the history of science, the scientific image has played a significant role in communication. With recent developments in computing technology, there has been an increase in the kinds of opportunities now available for scientists to communicate in more sophisticated ways. Within behavior analysis, though, we are only just beginning to appreciate the importance of going beyond the printing press to elucidate basic principles of behavior. The aim of this manuscript is to stimulate appreciation of both the role of the scientific image and the opportunities provided by a quick response code (QR code) for enhancing the functionality of the printed page. I discuss the limitations of imagery in behavior analysis ("Introduction"), and I show examples of what can be done with animations and multimedia for teaching philosophical issues that arise when teaching about private events ("Private Events 1 and 2"). Animations are also useful for bypassing ethical issues when showing examples of challenging behavior ("Challenging Behavior"). Each of these topics can be accessed only by scanning the QR code provided. This contingency has been arranged to help the reader embrace this new technology. In so doing, I hope to show its potential for going beyond the limitations of the printing press. PMID:27606187

  11. Monotonic correlation analysis of image quality measures for image fusion

    NASA Astrophysics Data System (ADS)

    Kaplan, Lance M.; Burks, Stephen D.; Moore, Richard K.; Nguyen, Quang

    2008-04-01

    The next generation of night vision goggles will fuse image intensified and long wave infra-red to create a hybrid image that will enable soldiers to better interpret their surroundings during nighttime missions. Paramount to the development of such goggles is the exploitation of image quality (IQ) measures to automatically determine the best image fusion algorithm for a particular task. This work introduces a novel monotonic correlation coefficient to investigate how well possible IQ features correlate to actual human performance, which is measured by a perception study. The paper will demonstrate how monotonic correlation can identify worthy features that could be overlooked by traditional correlation values.

  12. Vector processing enhancements for real-time image analysis.

    SciTech Connect

    Shoaf, S.; APS Engineering Support Division

    2008-01-01

    A real-time image analysis system was developed for beam imaging diagnostics. An Apple Power Mac G5 with an Active Silicon LFG frame grabber was used to capture video images that were processed and analyzed. Software routines were created to utilize vector-processing hardware to reduce the time to process images as compared to conventional methods. These improvements allow for more advanced image processing diagnostics to be performed in real time.

  13. 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. PMID:27534470

  14. MaZda--a software package for image texture analysis.

    PubMed

    Szczypiński, Piotr M; Strzelecki, Michał; Materka, Andrzej; Klepaczko, Artur

    2009-04-01

    MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage. PMID:18922598

  15. Thermal image analysis for detecting facemask leakage

    NASA Astrophysics Data System (ADS)

    Dowdall, Jonathan B.; Pavlidis, Ioannis T.; Levine, James

    2005-03-01

    Due to the modern advent of near ubiquitous accessibility to rapid international transportation the epidemiologic trends of highly communicable diseases can be devastating. With the recent emergence of diseases matching this pattern, such as Severe Acute Respiratory Syndrome (SARS), an area of overt concern has been the transmission of infection through respiratory droplets. Approved facemasks are typically effective physical barriers for preventing the spread of viruses through droplets, but breaches in a mask"s integrity can lead to an elevated risk of exposure and subsequent infection. Quality control mechanisms in place during the manufacturing process insure that masks are defect free when leaving the factory, but there remains little to detect damage caused by transportation or during usage. A system that could monitor masks in real-time while they were in use would facilitate a more secure environment for treatment and screening. To fulfill this necessity, we have devised a touchless method to detect mask breaches in real-time by utilizing the emissive properties of the mask in the thermal infrared spectrum. Specifically, we use a specialized thermal imaging system to detect minute air leakage in masks based on the principles of heat transfer and thermodynamics. The advantage of this passive modality is that thermal imaging does not require contact with the subject and can provide instant visualization and analysis. These capabilities can prove invaluable for protecting personnel in scenarios with elevated levels of transmission risk such as hospital clinics, border check points, and airports.

  16. Vision-sensing image analysis for GTAW process control

    SciTech Connect

    Long, D.D.

    1994-11-01

    Image analysis of a gas tungsten arc welding (GTAW) process was completed using video images from a charge coupled device (CCD) camera inside a specially designed coaxial (GTAW) electrode holder. Video data was obtained from filtered and unfiltered images, with and without the GTAW arc present, showing weld joint features and locations. Data Translation image processing boards, installed in an IBM PC AT 386 compatible computer, and Media Cybernetics image processing software were used to investigate edge flange weld joint geometry for image analysis.

  17. Image analysis by integration of disparate information

    NASA Technical Reports Server (NTRS)

    Lemoigne, Jacqueline

    1993-01-01

    Image analysis often starts with some preliminary segmentation which provides a representation of the scene needed for further interpretation. Segmentation can be performed in several ways, which are categorized as pixel based, edge-based, and region-based. Each of these approaches are affected differently by various factors, and the final result may be improved by integrating several or all of these methods, thus taking advantage of their complementary nature. In this paper, we propose an approach that integrates pixel-based and edge-based results by utilizing an iterative relaxation technique. This approach has been implemented on a massively parallel computer and tested on some remotely sensed imagery from the Landsat-Thematic Mapper (TM) sensor.

  18. APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

    SciTech Connect

    STOYANOVA,R.S.; OCHS,M.F.; BROWN,T.R.; ROONEY,W.D.; LI,X.; LEE,J.H.; SPRINGER,C.S.

    1999-05-22

    Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content.

  19. Some selected quantitative methods of thermal image analysis in Matlab.

    PubMed

    Koprowski, Robert

    2016-05-01

    The paper presents a new algorithm based on some selected automatic quantitative methods for analysing thermal images. It shows the practical implementation of these image analysis methods in Matlab. It enables to perform fully automated and reproducible measurements of selected parameters in thermal images. The paper also shows two examples of the use of the proposed image analysis methods for the area of ​​the skin of a human foot and face. The full source code of the developed application is also provided as an attachment. The main window of the program during dynamic analysis of the foot thermal image. PMID:26556680

  20. Dynamic Chest Image Analysis: Model-Based Perfusion Analysis in Dynamic Pulmonary Imaging

    NASA Astrophysics Data System (ADS)

    Liang, Jianming; Järvi, Timo; Kiuru, Aaro; Kormano, Martti; Svedström, Erkki

    2003-12-01

    The "Dynamic Chest Image Analysis" project aims to develop model-based computer analysis and visualization methods for showing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected with the dynamic pulmonary imaging technique. We have proposed and evaluated a multiresolutional method with an explicit ventilation model for ventilation analysis. This paper presents a new model-based method for pulmonary perfusion analysis. According to perfusion properties, we first devise a novel mathematical function to form a perfusion model. A simple yet accurate approach is further introduced to extract cardiac systolic and diastolic phases from the heart, so that this cardiac information may be utilized to accelerate the perfusion analysis and improve its sensitivity in detecting pulmonary perfusion abnormalities. This makes perfusion analysis not only fast but also robust in computation; consequently, perfusion analysis becomes computationally feasible without using contrast media. Our clinical case studies with 52 patients show that this technique is effective for pulmonary embolism even without using contrast media, demonstrating consistent correlations with computed tomography (CT) and nuclear medicine (NM) studies. This fluoroscopical examination takes only about 2 seconds for perfusion study with only low radiation dose to patient, involving no preparation, no radioactive isotopes, and no contrast media.

  1. A framework for joint image-and-shape analysis

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Tannenbaum, Allen; Bouix, Sylvain

    2014-03-01

    Techniques in medical image analysis are many times used for the comparison or regression on the intensities of images. In general, the domain of the image is a given Cartesian grids. Shape analysis, on the other hand, studies the similarities and differences among spatial objects of arbitrary geometry and topology. Usually, there is no function defined on the domain of shapes. Recently, there has been a growing needs for defining and analyzing functions defined on the shape space, and a coupled analysis on both the shapes and the functions defined on them. Following this direction, in this work we present a coupled analysis for both images and shapes. As a result, the statistically significant discrepancies in both the image intensities as well as on the underlying shapes are detected. The method is applied on both brain images for the schizophrenia and heart images for atrial fibrillation patients.

  2. High resolution ultraviolet imaging spectrometer for latent image analysis.

    PubMed

    Lyu, Hang; Liao, Ningfang; Li, Hongsong; Wu, Wenmin

    2016-03-21

    In this work, we present a close-range ultraviolet imaging spectrometer with high spatial resolution, and reasonably high spectral resolution. As the transmissive optical components cause chromatic aberration in the ultraviolet (UV) spectral range, an all-reflective imaging scheme is introduced to promote the image quality. The proposed instrument consists of an oscillating mirror, a Cassegrain objective, a Michelson structure, an Offner relay, and a UV enhanced CCD. The finished spectrometer has a spatial resolution of 29.30μm on the target plane; the spectral scope covers both near and middle UV band; and can obtain approximately 100 wavelength samples over the range of 240~370nm. The control computer coordinates all the components of the instrument and enables capturing a series of images, which can be reconstructed into an interferogram datacube. The datacube can be converted into a spectrum datacube, which contains spectral information of each pixel with many wavelength samples. A spectral calibration is carried out by using a high pressure mercury discharge lamp. A test run demonstrated that this interferometric configuration can obtain high resolution spectrum datacube. The pattern recognition algorithm is introduced to analyze the datacube and distinguish the latent traces from the base materials. This design is particularly good at identifying the latent traces in the application field of forensic imaging. PMID:27136837

  3. LANDSAT-4 image data quality analysis

    NASA Technical Reports Server (NTRS)

    Anuta, P. E. (Principal Investigator)

    1982-01-01

    Work done on evaluating the geometric and radiometric quality of early LANDSAT-4 sensor data is described. Band to band and channel to channel registration evaluations were carried out using a line correlator. Visual blink comparisons were run on an image display to observe band to band registration over 512 x 512 pixel blocks. The results indicate a .5 pixel line misregistration between the 1.55 to 1.75, 2.08 to 2.35 micrometer bands and the first four bands. Also a four 30M line and column misregistration of the thermal IR band was observed. Radiometric evaluation included mean and variance analysis of individual detectors and principal components analysis. Results indicate that detector bias for all bands is very close or within tolerance. Bright spots were observed in the thermal IR band on an 18 line by 128 pixel grid. No explanation for this was pursued. The general overall quality of the TM was judged to be very high.

  4. Analysis of physical processes via imaging vectors

    NASA Astrophysics Data System (ADS)

    Volovodenko, V.; Efremova, N.; Efremov, V.

    2016-06-01

    Practically, all modeling processes in one way or another are random. The foremost formulated theoretical foundation embraces Markov processes, being represented in different forms. Markov processes are characterized as a random process that undergoes transitions from one state to another on a state space, whereas the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. In the Markov processes the proposition (model) of the future by no means changes in the event of the expansion and/or strong information progression relative to preceding time. Basically, modeling physical fields involves process changing in time, i.e. non-stationay processes. In this case, the application of Laplace transformation provides unjustified description complications. Transition to other possibilities results in explicit simplification. The method of imaging vectors renders constructive mathematical models and necessary transition in the modeling process and analysis itself. The flexibility of the model itself using polynomial basis leads to the possible rapid transition of the mathematical model and further analysis acceleration. It should be noted that the mathematical description permits operator representation. Conversely, operator representation of the structures, algorithms and data processing procedures significantly improve the flexibility of the modeling process.

  5. Dynamic chest image analysis: model-based pulmonary perfusion analysis with pyramid images

    NASA Astrophysics Data System (ADS)

    Liang, Jianming; Haapanen, Arto; Jaervi, Timo; Kiuru, Aaro J.; Kormano, Martti; Svedstrom, Erkki; Virkki, Raimo

    1998-07-01

    The aim of the study 'Dynamic Chest Image Analysis' is to develop computer analysis and visualization methods for showing focal and general abnormalities of lung ventilation and perfusion based on a sequence of digital chest fluoroscopy frames collected at different phases of the respiratory/cardiac cycles in a short period of time. We have proposed a framework for ventilation study with an explicit ventilation model based on pyramid images. In this paper, we extend the framework to pulmonary perfusion study. A perfusion model and the truncated pyramid are introduced. The perfusion model aims at extracting accurate, geographic perfusion parameters, and the truncated pyramid helps in understanding perfusion at multiple resolutions and speeding up the convergence process in optimization. Three cases are included to illustrate the experimental results.

  6. Medical Image Analysis by Cognitive Information Systems - a Review.

    PubMed

    Ogiela, Lidia; Takizawa, Makoto

    2016-10-01

    This publication presents a review of medical image analysis systems. The paradigms of cognitive information systems will be presented by examples of medical image analysis systems. The semantic processes present as it is applied to different types of medical images. Cognitive information systems were defined on the basis of methods for the semantic analysis and interpretation of information - medical images - applied to cognitive meaning of medical images contained in analyzed data sets. Semantic analysis was proposed to analyzed the meaning of data. Meaning is included in information, for example in medical images. Medical image analysis will be presented and discussed as they are applied to various types of medical images, presented selected human organs, with different pathologies. Those images were analyzed using different classes of cognitive information systems. Cognitive information systems dedicated to medical image analysis was also defined for the decision supporting tasks. This process is very important for example in diagnostic and therapy processes, in the selection of semantic aspects/features, from analyzed data sets. Those features allow to create a new way of analysis. PMID:27526188

  7. Image based SAR product simulation for analysis

    NASA Technical Reports Server (NTRS)

    Domik, G.; Leberl, F.

    1987-01-01

    SAR product simulation serves to predict SAR image gray values for various flight paths. Input typically consists of a digital elevation model and backscatter curves. A new method is described of product simulation that employs also a real SAR input image for image simulation. This can be denoted as 'image-based simulation'. Different methods to perform this SAR prediction are presented and advantages and disadvantages discussed. Ascending and descending orbit images from NASA's SIR-B experiment were used for verification of the concept: input images from ascending orbits were converted into images from a descending orbit; the results are compared to the available real imagery to verify that the prediction technique produces meaningful image data.

  8. Imaging biomarkers in multiple Sclerosis: From image analysis to population imaging.

    PubMed

    Barillot, Christian; Edan, Gilles; Commowick, Olivier

    2016-10-01

    The production of imaging data in medicine increases more rapidly than the capacity of computing models to extract information from it. The grand challenges of better understanding the brain, offering better care for neurological disorders, and stimulating new drug design will not be achieved without significant advances in computational neuroscience. The road to success is to develop a new, generic, computational methodology and to confront and validate this methodology on relevant diseases with adapted computational infrastructures. This new concept sustains the need to build new research paradigms to better understand the natural history of the pathology at the early phase; to better aggregate data that will provide the most complete representation of the pathology in order to better correlate imaging with other relevant features such as clinical, biological or genetic data. In this context, one of the major challenges of neuroimaging in clinical neurosciences is to detect quantitative signs of pathological evolution as early as possible to prevent disease progression, evaluate therapeutic protocols or even better understand and model the natural history of a given neurological pathology. Many diseases encompass brain alterations often not visible on conventional MRI sequences, especially in normal appearing brain tissues (NABT). MRI has often a low specificity for differentiating between possible pathological changes which could help in discriminating between the different pathological stages or grades. The objective of medical image analysis procedures is to define new quantitative neuroimaging biomarkers to track the evolution of the pathology at different levels. This paper illustrates this issue in one acute neuro-inflammatory pathology: Multiple Sclerosis (MS). It exhibits the current medical image analysis approaches and explains how this field of research will evolve in the next decade to integrate larger scale of information at the temporal, cellular

  9. Image pattern recognition supporting interactive analysis and graphical visualization

    NASA Technical Reports Server (NTRS)

    Coggins, James M.

    1992-01-01

    Image Pattern Recognition attempts to infer properties of the world from image data. Such capabilities are crucial for making measurements from satellite or telescope images related to Earth and space science problems. Such measurements can be the required product itself, or the measurements can be used as input to a computer graphics system for visualization purposes. At present, the field of image pattern recognition lacks a unified scientific structure for developing and evaluating image pattern recognition applications. The overall goal of this project is to begin developing such a structure. This report summarizes results of a 3-year research effort in image pattern recognition addressing the following three principal aims: (1) to create a software foundation for the research and identify image pattern recognition problems in Earth and space science; (2) to develop image measurement operations based on Artificial Visual Systems; and (3) to develop multiscale image descriptions for use in interactive image analysis.

  10. Atomic force microscope, molecular imaging, and analysis.

    PubMed

    Chen, Shu-wen W; Teulon, Jean-Marie; Godon, Christian; Pellequer, Jean-Luc

    2016-01-01

    Image visibility is a central issue in analyzing all kinds of microscopic images. An increase of intensity contrast helps to raise the image visibility, thereby to reveal fine image features. Accordingly, a proper evaluation of results with current imaging parameters can be used for feedback on future imaging experiments. In this work, we have applied the Laplacian function of image intensity as either an additive component (Laplacian mask) or a multiplying factor (Laplacian weight) for enhancing image contrast of high-resolution AFM images of two molecular systems, an unknown protein imaged in air, provided by AFM COST Action TD1002 (http://www.afm4nanomedbio.eu/), and tobacco mosaic virus (TMV) particles imaged in liquid. Based on both visual inspection and quantitative representation of contrast measurements, we found that the Laplacian weight is more effective than the Laplacian mask for the unknown protein, whereas for the TMV system the strengthened Laplacian mask is superior to the Laplacian weight. The present results indicate that a mathematical function, as exemplified by the Laplacian function, may yield varied processing effects with different operations. To interpret the diversity of molecular structure and topology in images, an explicit expression for processing procedures should be included in scientific reports alongside instrumental setups. PMID:26224520

  11. Wndchrm – an open source utility for biological image analysis

    PubMed Central

    Shamir, Lior; Orlov, Nikita; Eckley, D Mark; Macura, Tomasz; Johnston, Josiah; Goldberg, Ilya G

    2008-01-01

    Background Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening. Methods Wndchrm is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement. Results Wndchrm has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data. Conclusion We suggest that wndchrm can be effectively used for a wide range of biological image analysis tasks. Using wndchrm can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms. PMID:18611266

  12. Wave-Optics Analysis of Pupil Imaging

    NASA Technical Reports Server (NTRS)

    Dean, Bruce H.; Bos, Brent J.

    2006-01-01

    Pupil imaging performance is analyzed from the perspective of physical optics. A multi-plane diffraction model is constructed by propagating the scalar electromagnetic field, surface by surface, along the optical path comprising the pupil imaging optical system. Modeling results are compared with pupil images collected in the laboratory. The experimental setup, although generic for pupil imaging systems in general, has application to the James Webb Space Telescope (JWST) optical system characterization where the pupil images are used as a constraint to the wavefront sensing and control process. Practical design considerations follow from the diffraction modeling which are discussed in the context of the JWST Observatory.

  13. Advanced image analysis for the preservation of cultural heritage

    NASA Astrophysics Data System (ADS)

    France, Fenella G.; Christens-Barry, William; Toth, Michael B.; Boydston, Kenneth

    2010-02-01

    The Library of Congress' Preservation Research and Testing Division has established an advanced preservation studies scientific program for research and analysis of the diverse range of cultural heritage objects in its collection. Using this system, the Library is currently developing specialized integrated research methodologies for extending preservation analytical capacities through non-destructive hyperspectral imaging of cultural objects. The research program has revealed key information to support preservation specialists, scholars and other institutions. The approach requires close and ongoing collaboration between a range of scientific and cultural heritage personnel - imaging and preservation scientists, art historians, curators, conservators and technology analysts. A research project of the Pierre L'Enfant Plan of Washington DC, 1791 had been undertaken to implement and advance the image analysis capabilities of the imaging system. Innovative imaging options and analysis techniques allow greater processing and analysis capacities to establish the imaging technique as the first initial non-invasive analysis and documentation step in all cultural heritage analyses. Mapping spectral responses, organic and inorganic data, topography semi-microscopic imaging, and creating full spectrum images have greatly extended this capacity from a simple image capture technique. Linking hyperspectral data with other non-destructive analyses has further enhanced the research potential of this image analysis technique.

  14. Three modality image registration of brain SPECT/CT and MR images for quantitative analysis of dopamine transporter imaging

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yuzuho; Takeda, Yuta; Hara, Takeshi; Zhou, Xiangrong; Matsusako, Masaki; Tanaka, Yuki; Hosoya, Kazuhiko; Nihei, Tsutomu; Katafuchi, Tetsuro; Fujita, Hiroshi

    2016-03-01

    Important features in Parkinson's disease (PD) are degenerations and losses of dopamine neurons in corpus striatum. 123I-FP-CIT can visualize activities of the dopamine neurons. The activity radio of background to corpus striatum is used for diagnosis of PD and Dementia with Lewy Bodies (DLB). The specific activity can be observed in the corpus striatum on SPECT images, but the location and the shape of the corpus striatum on SPECT images only are often lost because of the low uptake. In contrast, MR images can visualize the locations of the corpus striatum. The purpose of this study was to realize a quantitative image analysis for the SPECT images by using image registration technique with brain MR images that can determine the region of corpus striatum. In this study, the image fusion technique was used to fuse SPECT and MR images by intervening CT image taken by SPECT/CT. The mutual information (MI) for image registration between CT and MR images was used for the registration. Six SPECT/CT and four MR scans of phantom materials are taken by changing the direction. As the results of the image registrations, 16 of 24 combinations were registered within 1.3mm. By applying the approach to 32 clinical SPECT/CT and MR cases, all of the cases were registered within 0.86mm. In conclusions, our registration method has a potential in superimposing MR images on SPECT images.

  15. Characterization and analysis of infrared images

    NASA Astrophysics Data System (ADS)

    Raglin, Adrienne; Wetmore, Alan; Ligon, David

    2006-05-01

    Stokes images in the long-wave infrared (LWIR) and methods for processing polarimetric data continue to be areas of interest. Stokes images which are sensitive to geometry and material differences are acquired by measuring the polarization state of the received electromagnetic radiation. The polarimetric data from Stokes images may provide enhancements to conventional IR imagery data. It is generally agreed that polarimetric images can reveal information about objects or features within a scene that are not available through other imaging techniques. This additional information may generate different approaches to segmentation, detection, and recognition of objects or features. Previous research where horizontal and vertical polarization data is used supports the use of this type of data for image processing tasks. In this work we analyze a sample polarimetric image to show both improved segmentation of objects and derivation of their inherent 3-D geometry.

  16. Image segmentation by iterative parallel region growing with application to data compression and image analysis

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    1988-01-01

    Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

  17. Analysis of scanning probe microscope images using wavelets.

    PubMed

    Gackenheimer, C; Cayon, L; Reifenberger, R

    2006-03-01

    The utility of wavelet transforms for analysis of scanning probe images is investigated. Simulated scanning probe images are analyzed using wavelet transforms and compared to a parallel analysis using more conventional Fourier transform techniques. The wavelet method introduced in this paper is particularly useful as an image recognition algorithm to enhance nanoscale objects of a specific scale that may be present in scanning probe images. In its present form, the applied wavelet is optimal for detecting objects with rotational symmetry. The wavelet scheme is applied to the analysis of scanning probe data to better illustrate the advantages that this new analysis tool offers. The wavelet algorithm developed for analysis of scanning probe microscope (SPM) images has been incorporated into the WSxM software which is a versatile freeware SPM analysis package. PMID:16439061

  18. Image analysis for dental bone quality assessment using CBCT imaging

    NASA Astrophysics Data System (ADS)

    Suprijanto; Epsilawati, L.; Hajarini, M. S.; Juliastuti, E.; Susanti, H.

    2016-03-01

    Cone beam computerized tomography (CBCT) is one of X-ray imaging modalities that are applied in dentistry. Its modality can visualize the oral region in 3D and in a high resolution. CBCT jaw image has potential information for the assessment of bone quality that often used for pre-operative implant planning. We propose comparison method based on normalized histogram (NH) on the region of inter-dental septum and premolar teeth. Furthermore, the NH characteristic from normal and abnormal bone condition are compared and analyzed. Four test parameters are proposed, i.e. the difference between teeth and bone average intensity (s), the ratio between bone and teeth average intensity (n) of NH, the difference between teeth and bone peak value (Δp) of NH, and the ratio between teeth and bone of NH range (r). The results showed that n, s, and Δp have potential to be the classification parameters of dental calcium density.

  19. Analysis of Anechoic Chamber Testing of the Hurricane Imaging Radiometer

    NASA Technical Reports Server (NTRS)

    Fenigstein, David; Ruf, Chris; James, Mark; Simmons, David; Miller, Timothy; Buckley, Courtney

    2010-01-01

    The Hurricane Imaging Radiometer System (HIRAD) is a new airborne passive microwave remote sensor developed to observe hurricanes. HIRAD incorporates synthetic thinned array radiometry technology, which use Fourier synthesis to reconstruct images from an array of correlated antenna elements. The HIRAD system response to a point emitter has been measured in an anechoic chamber. With this data, a Fourier inversion image reconstruction algorithm has been developed. Performance analysis of the apparatus is presented, along with an overview of the image reconstruction algorithm

  20. Image and Data-analysis Tools For Paleoclimatic Reconstructions

    NASA Astrophysics Data System (ADS)

    Pozzi, M.

    It comes here proposed a directory of instruments and computer science resources chosen in order to resolve the problematic ones that regard the paleoclimatic recon- structions. They will come discussed in particular the following points: 1) Numerical analysis of paleo-data (fossils abundances, species analyses, isotopic signals, chemical-physical parameters, biological data): a) statistical analyses (uni- variate, diversity, rarefaction, correlation, ANOVA, F and T tests, Chi^2) b) multidi- mensional analyses (principal components, corrispondence, cluster analysis, seriation, discriminant, autocorrelation, spectral analysis) neural analyses (backpropagation net, kohonen feature map, hopfield net genetic algorithms) 2) Graphical analysis (visu- alization tools) of paleo-data (quantitative and qualitative fossils abundances, species analyses, isotopic signals, chemical-physical parameters): a) 2-D data analyses (graph, histogram, ternary, survivorship) b) 3-D data analyses (direct volume rendering, iso- surfaces, segmentation, surface reconstruction, surface simplification,generation of tetrahedral grids). 3) Quantitative and qualitative digital image analysis (macro and microfossils image analysis, Scanning Electron Microscope. and Optical Polarized Microscope images capture and analysis, morphometric data analysis, 3-D reconstruc- tions): a) 2D image analysis (correction of image defects, enhancement of image de- tail, converting texture and directionality to grey scale or colour differences, visual enhancement using pseudo-colour, pseudo-3D, thresholding of image features, binary image processing, measurements, stereological measurements, measuring features on a white background) b) 3D image analysis (basic stereological procedures, two dimen- sional structures; area fraction from the point count, volume fraction from the point count, three dimensional structures: surface area and the line intercept count, three dimensional microstructures; line length and the

  1. Image analysis of neuropsychological test responses

    NASA Astrophysics Data System (ADS)

    Smith, Stephen L.; Hiller, Darren L.

    1996-04-01

    This paper reports recent advances in the development of an automated approach to neuropsychological testing. High performance image analysis algorithms have been developed as part of a convenient and non-invasive computer-based system to provide an objective assessment of patient responses to figure-copying tests. Tests of this type are important in determining the neurological function of patients following stroke through evaluation of their visuo-spatial performance. Many conventional neuropsychological tests suffer from the serious drawback that subjective judgement on the part of the tester is required in the measurement of the patient's response which leads to a qualitative neuropsychological assessment that can be both inconsistent and inaccurate. Results for this automated approach are presented for three clinical populations: patients suffering right hemisphere stroke are compared with adults with no known neurological disorder and a population comprising normal school children of 11 years is presented to demonstrate the sensitivity of the technique. As well as providing a more reliable and consistent diagnosis this technique is sufficiently sensitive to monitor a patient's progress over a period of time and will provide the neuropsychologist with a practical means of evaluating the effectiveness of therapy or medication administered as part of a rehabilitation program.

  2. EVALUATION OF COLOR ALTERATION ON FABRICS BY IMAGE ANALYSIS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Evaluation of color changes is usually done manually and is often inconsistent. Image analysis provides a method in which to evaluate color-related testing that is not only simple, but also consistent. Image analysis can also be used to measure areas that were considered too large for the colorimet...

  3. Slide Set: Reproducible image analysis and batch processing with ImageJ.

    PubMed

    Nanes, Benjamin A

    2015-11-01

    Most imaging studies in the biological sciences rely on analyses that are relatively simple. However, manual repetition of analysis tasks across multiple regions in many images can complicate even the simplest analysis, making record keeping difficult, increasing the potential for error, and limiting reproducibility. While fully automated solutions are necessary for very large data sets, they are sometimes impractical for the small- and medium-sized data sets common in biology. Here we present the Slide Set plugin for ImageJ, which provides a framework for reproducible image analysis and batch processing. Slide Set organizes data into tables, associating image files with regions of interest and other relevant information. Analysis commands are automatically repeated over each image in the data set, and multiple commands can be chained together for more complex analysis tasks. All analysis parameters are saved, ensuring transparency and reproducibility. Slide Set includes a variety of built-in analysis commands and can be easily extended to automate other ImageJ plugins, reducing the manual repetition of image analysis without the set-up effort or programming expertise required for a fully automated solution. PMID:26554504

  4. Slide Set: reproducible image analysis and batch processing with ImageJ

    PubMed Central

    Nanes, Benjamin A.

    2015-01-01

    Most imaging studies in the biological sciences rely on analyses that are relatively simple. However, manual repetition of analysis tasks across multiple regions in many images can complicate even the simplest analysis, making record keeping difficult, increasing the potential for error, and limiting reproducibility. While fully automated solutions are necessary for very large data sets, they are sometimes impractical for the small- and medium-sized data sets that are common in biology. This paper introduces Slide Set, a framework for reproducible image analysis and batch processing with ImageJ. Slide Set organizes data into tables, associating image files with regions of interest and other relevant information. Analysis commands are automatically repeated over each image in the data set, and multiple commands can be chained together for more complex analysis tasks. All analysis parameters are saved, ensuring transparency and reproducibility. Slide Set includes a variety of built-in analysis commands and can be easily extended to automate other ImageJ plugins, reducing the manual repetition of image analysis without the set-up effort or programming expertise required for a fully automated solution. PMID:26554504

  5. Image analysis for discrimination of cervical neoplasia

    NASA Astrophysics Data System (ADS)

    Pogue, Brian W.; Mycek, Mary-Ann; Harper, Diane

    2000-01-01

    Colposcopy involves visual imaging of the cervix for patients who have exhibited some prior indication of abnormality, and the major goals are to visually inspect for any malignancies and to guide biopsy sampling. Currently colposcopy equipment is being upgraded in many health care centers to incorporate digital image acquisition and archiving. These permanent images can be analyzed for characteristic features and color patterns which may enhance the specificity and objectivity of the routine exam. In this study a series of images from patients with biopsy confirmed cervical intraepithelia neoplasia stage 2/3 are compared with images from patients with biopsy confirmed immature squamous metaplasia, with the goal of determining optimal criteria for automated discrimination between them. All images were separated into their red, green, and blue channels, and comparisons were made between relative intensity, intensity variation, spatial frequencies, fractal dimension, and Euler number. This study indicates that computer-based processing of cervical images can provide some discrimination of the type of tissue features which are important for clinical evaluation, with the Euler number being the most clinically useful feature to discriminate metaplasia from neoplasia. Also there was a strong indication that morphology observed in the blue channel of the image provided more information about epithelial cell changes. Further research in this field can lead to advances in computer-aided diagnosis as well as the potential for online image enhancement in digital colposcopy.

  6. A linear mixture analysis-based compression for hyperspectral image analysis

    SciTech Connect

    C. I. Chang; I. W. Ginsberg

    2000-06-30

    In this paper, the authors present a fully constrained least squares linear spectral mixture analysis-based compression technique for hyperspectral image analysis, particularly, target detection and classification. Unlike most compression techniques that directly deal with image gray levels, the proposed compression approach generates the abundance fractional images of potential targets present in an image scene and then encodes these fractional images so as to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in the abundance fractional images, the loss of information may have very little impact on image analysis. In some occasions, it even improves analysis performance. Airborne visible infrared imaging spectrometer (AVIRIS) data experiments demonstrate that it can effectively detect and classify targets while achieving very high compression ratios.

  7. Analysis of airborne MAIS imaging spectrometric data for mineral exploration

    SciTech Connect

    Wang Jinnian; Zheng Lanfen; Tong Qingxi

    1996-11-01

    The high spectral resolution imaging spectrometric system made quantitative analysis and mapping of surface composition possible. The key issue will be the quantitative approach for analysis of surface parameters for imaging spectrometer data. This paper describes the methods and the stages of quantitative analysis. (1) Extracting surface reflectance from imaging spectrometer image. Lab. and inflight field measurements are conducted for calibration of imaging spectrometer data, and the atmospheric correction has also been used to obtain ground reflectance by using empirical line method and radiation transfer modeling. (2) Determining quantitative relationship between absorption band parameters from the imaging spectrometer data and chemical composition of minerals. (3) Spectral comparison between the spectra of spectral library and the spectra derived from the imagery. The wavelet analysis-based spectrum-matching techniques for quantitative analysis of imaging spectrometer data has beer, developed. Airborne MAIS imaging spectrometer data were used for analysis and the analysis results have been applied to the mineral and petroleum exploration in Tarim Basin area china. 8 refs., 8 figs.

  8. Low-cost image analysis system

    SciTech Connect

    Lassahn, G.D.

    1995-01-01

    The author has developed an Automatic Target Recognition system based on parallel processing using transputers. This approach gives a powerful, fast image processing system at relatively low cost. This system scans multi-sensor (e.g., several infrared bands) image data to find any identifiable target, such as physical object or a type of vegetation.

  9. Analysis of Images from Experiments Investigating Fragmentation of Materials

    SciTech Connect

    Kamath, C; Hurricane, O

    2007-09-10

    Image processing techniques have been used extensively to identify objects of interest in image data and extract representative characteristics for these objects. However, this can be a challenge due to the presence of noise in the images and the variation across images in a dataset. When the number of images to be analyzed is large, the algorithms used must also be relatively insensitive to the choice of parameters and lend themselves to partial or full automation. This not only avoids manual analysis which can be time consuming and error-prone, but also makes the analysis reproducible, thus enabling comparisons between images which have been processed in an identical manner. In this paper, we describe our approach to extracting features for objects of interest in experimental images. Focusing on the specific problem of fragmentation of materials, we show how we can extract statistics for the fragments and the gaps between them.

  10. Multimodal digital color imaging system for facial skin lesion analysis

    NASA Astrophysics Data System (ADS)

    Bae, Youngwoo; Lee, Youn-Heum; Jung, Byungjo

    2008-02-01

    In dermatology, various digital imaging modalities have been used as an important tool to quantitatively evaluate the treatment effect of skin lesions. Cross-polarization color image was used to evaluate skin chromophores (melanin and hemoglobin) information and parallel-polarization image to evaluate skin texture information. In addition, UV-A induced fluorescent image has been widely used to evaluate various skin conditions such as sebum, keratosis, sun damages, and vitiligo. In order to maximize the evaluation efficacy of various skin lesions, it is necessary to integrate various imaging modalities into an imaging system. In this study, we propose a multimodal digital color imaging system, which provides four different digital color images of standard color image, parallel and cross-polarization color image, and UV-A induced fluorescent color image. Herein, we describe the imaging system and present the examples of image analysis. By analyzing the color information and morphological features of facial skin lesions, we are able to comparably and simultaneously evaluate various skin lesions. In conclusion, we are sure that the multimodal color imaging system can be utilized as an important assistant tool in dermatology.

  11. PIZZARO: Forensic analysis and restoration of image and video data.

    PubMed

    Kamenicky, Jan; Bartos, Michal; Flusser, Jan; Mahdian, Babak; Kotera, Jan; Novozamsky, Adam; Saic, Stanislav; Sroubek, Filip; Sorel, Michal; Zita, Ales; Zitova, Barbara; Sima, Zdenek; Svarc, Petr; Horinek, Jan

    2016-07-01

    This paper introduces a set of methods for image and video forensic analysis. They were designed to help to assess image and video credibility and origin and to restore and increase image quality by diminishing unwanted blur, noise, and other possible artifacts. The motivation came from the best practices used in the criminal investigation utilizing images and/or videos. The determination of the image source, the verification of the image content, and image restoration were identified as the most important issues of which automation can facilitate criminalists work. Novel theoretical results complemented with existing approaches (LCD re-capture detection and denoising) were implemented in the PIZZARO software tool, which consists of the image processing functionality as well as of reporting and archiving functions to ensure the repeatability of image analysis procedures and thus fulfills formal aspects of the image/video analysis work. Comparison of new proposed methods with the state of the art approaches is shown. Real use cases are presented, which illustrate the functionality of the developed methods and demonstrate their applicability in different situations. The use cases as well as the method design were solved in tight cooperation of scientists from the Institute of Criminalistics, National Drug Headquarters of the Criminal Police and Investigation Service of the Police of the Czech Republic, and image processing experts from the Czech Academy of Sciences. PMID:27182830

  12. Dehazing method through polarimetric imaging and multi-scale analysis

    NASA Astrophysics Data System (ADS)

    Cao, Lei; Shao, Xiaopeng; Liu, Fei; Wang, Lin

    2015-05-01

    An approach for haze removal utilizing polarimetric imaging and multi-scale analysis has been developed to solve one problem that haze weather weakens the interpretation of remote sensing because of the poor visibility and short detection distance of haze images. On the one hand, the polarization effects of the airlight and the object radiance in the imaging procedure has been considered. On the other hand, one fact that objects and haze possess different frequency distribution properties has been emphasized. So multi-scale analysis through wavelet transform has been employed to make it possible for low frequency components that haze presents and high frequency coefficients that image details or edges occupy are processed separately. According to the measure of the polarization feather by Stokes parameters, three linear polarized images (0°, 45°, and 90°) have been taken on haze weather, then the best polarized image min I and the worst one max I can be synthesized. Afterwards, those two polarized images contaminated by haze have been decomposed into different spatial layers with wavelet analysis, and the low frequency images have been processed via a polarization dehazing algorithm while high frequency components manipulated with a nonlinear transform. Then the ultimate haze-free image can be reconstructed by inverse wavelet reconstruction. Experimental results verify that the dehazing method proposed in this study can strongly promote image visibility and increase detection distance through haze for imaging warning and remote sensing systems.

  13. Spatio-spectral image analysis using classical and neural algorithms

    SciTech Connect

    Roberts, S.; Gisler, G.R.; Theiler, J.

    1996-12-31

    Remote imaging at high spatial resolution has a number of environmental, industrial, and military applications. Analysis of high-resolution multi-spectral images usually involves either spectral analysis of single pixels in a multi- or hyper-spectral image or spatial analysis of multi-pixels in a panchromatic or monochromatic image. Although insufficient for some pattern recognition applications individually, the combination of spatial and spectral analytical techniques may allow the identification of more complex signatures that might not otherwise be manifested in the individual spatial or spectral domains. We report on some preliminary investigation of unsupervised classification methodologies (using both ``classical`` and ``neural`` algorithms) to identify potentially revealing features in these images. We apply dimension-reduction preprocessing to the images, duster, and compare the clusterings obtained by different algorithms. Our classification results are analyzed both visually and with a suite of objective, quantitative measures.

  14. Dynamic infrared imaging in identification of breast cancer tissue with combined image processing and frequency analysis.

    PubMed

    Joro, R; Lääperi, A-L; Soimakallio, S; Järvenpää, R; Kuukasjärvi, T; Toivonen, T; Saaristo, R; Dastidar, P

    2008-01-01

    Five combinations of image-processing algorithms were applied to dynamic infrared (IR) images of six breast cancer patients preoperatively to establish optimal enhancement of cancer tissue before frequency analysis. mid-wave photovoltaic (PV) IR cameras with 320x254 and 640x512 pixels were used. The signal-to-noise ratio and the specificity for breast cancer were evaluated with the image-processing combinations from the image series of each patient. Before image processing and frequency analysis the effect of patient movement was minimized with a stabilization program developed and tested in the study by stabilizing image slices using surface markers set as measurement points on the skin of the imaged breast. A mathematical equation for superiority value was developed for comparison of the key ratios of the image-processing combinations. The ability of each combination to locate the mammography finding of breast cancer in each patient was compared. Our results show that data collected with a 640x512-pixel mid-wave PV camera applying image-processing methods optimizing signal-to-noise ratio, morphological image processing and linear image restoration before frequency analysis possess the greatest superiority value, showing the cancer area most clearly also in the match centre of the mammography estimation. PMID:18666012

  15. Vector sparse representation of color image using quaternion matrix analysis.

    PubMed

    Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong

    2015-04-01

    Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain. PMID:25643407

  16. An approach to multi-temporal MODIS image analysis using image classification and segmentation

    NASA Astrophysics Data System (ADS)

    Senthilnath, J.; Bajpai, Shivesh; Omkar, S. N.; Diwakar, P. G.; Mani, V.

    2012-11-01

    This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from "normal" (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions.

  17. Multiple sclerosis medical image analysis and information management.

    PubMed

    Liu, Lifeng; Meier, Dominik; Polgar-Turcsanyi, Mariann; Karkocha, Pawel; Bakshi, Rohit; Guttmann, Charles R G

    2005-01-01

    Magnetic resonance imaging (MRI) has become a central tool for patient management, as well as research, in multiple sclerosis (MS). Measurements of disease burden and activity derived from MRI through quantitative image analysis techniques are increasingly being used. There are many complexities and challenges in building computerized processing pipelines to ensure efficiency, reproducibility, and quality control for MRI scans from MS patients. Such paradigms require advanced image processing and analysis technologies, as well as integrated database management systems to ensure the most utility for clinical and research purposes. This article reviews pipelines available for quantitative clinical MRI research in MS, including image segmentation, registration, time-series analysis, performance validation, visualization techniques, and advanced medical imaging software packages. To address the complex demands of the sequential processes, the authors developed a workflow management system that uses a centralized database and distributed computing system for image processing and analysis. The implementation of their system includes a web-form-based Oracle database application for information management and event dispatching, and multiple modules for image processing and analysis. The seamless integration of processing pipelines with the database makes it more efficient for users to navigate complex, multistep analysis protocols, reduces the user's learning curve, reduces the time needed for combining and activating different computing modules, and allows for close monitoring for quality-control purposes. The authors' system can be extended to general applications in clinical trials and to routine processing for image-based clinical research. PMID:16385023

  18. Development of a quantitative autoradiography image analysis system

    SciTech Connect

    Hoffman, T.J.; Volkert, W.A.; Holmes R.A.

    1986-03-01

    A low cost image analysis system suitable for quantitative autoradiography (QAR) analysis has been developed. Autoradiographs can be digitized using a conventional Newvicon television camera interfaced to an IBM-XT microcomputer. Software routines for image digitization and capture permit the acquisition of thresholded or windowed images with graphic overlays that can be stored on storage devices. Image analysis software performs all background and non-linearity corrections prior to display as black/white or pseudocolor images. The relationship of pixel intensity to a standard radionuclide concentration allows the production of quantitative maps of tissue radiotracer concentrations. An easily modified subroutine is provided for adaptation to use appropriate operational equations when parameters such as regional cerebral blood flow or regional cerebral glucose metabolism are under investigation. This system could provide smaller research laboratories with the capability of QAR analysis at relatively low cost.

  19. 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.

  20. Automated thermal mapping techniques using chromatic image analysis

    NASA Technical Reports Server (NTRS)

    Buck, Gregory M.

    1989-01-01

    Thermal imaging techniques are introduced using a chromatic image analysis system and temperature sensitive coatings. These techniques are used for thermal mapping and surface heat transfer measurements on aerothermodynamic test models in hypersonic wind tunnels. Measurements are made on complex vehicle configurations in a timely manner and at minimal expense. The image analysis system uses separate wavelength filtered images to analyze surface spectral intensity data. The system was initially developed for quantitative surface temperature mapping using two-color thermographic phosphors but was found useful in interpreting phase change paint and liquid crystal data as well.

  1. Rapid analysis and exploration of fluorescence microscopy images.

    PubMed

    Pavie, Benjamin; Rajaram, Satwik; Ouyang, Austin; Altschuler, Jason M; Steininger, Robert J; Wu, Lani F; Altschuler, Steven J

    2014-01-01

    Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens. PMID:24686220

  2. Research of second harmonic generation images based on texture analysis

    NASA Astrophysics Data System (ADS)

    Liu, Yao; Li, Yan; Gong, Haiming; Zhu, Xiaoqin; Huang, Zufang; Chen, Guannan

    2014-09-01

    Texture analysis plays a crucial role in identifying objects or regions of interest in an image. It has been applied to a variety of medical image processing, ranging from the detection of disease and the segmentation of specific anatomical structures, to differentiation between healthy and pathological tissues. Second harmonic generation (SHG) microscopy as a potential noninvasive tool for imaging biological tissues has been widely used in medicine, with reduced phototoxicity and photobleaching. In this paper, we clarified the principles of texture analysis including statistical, transform, structural and model-based methods and gave examples of its applications, reviewing studies of the technique. Moreover, we tried to apply texture analysis to the SHG images for the differentiation of human skin scar tissues. Texture analysis method based on local binary pattern (LBP) and wavelet transform was used to extract texture features of SHG images from collagen in normal and abnormal scars, and then the scar SHG images were classified into normal or abnormal ones. Compared with other texture analysis methods with respect to the receiver operating characteristic analysis, LBP combined with wavelet transform was demonstrated to achieve higher accuracy. It can provide a new way for clinical diagnosis of scar types. At last, future development of texture analysis in SHG images were discussed.

  3. Multistage hierarchy for fast image analysis

    NASA Astrophysics Data System (ADS)

    Grudin, Maxim A.; Harvey, David M.; Timchenko, Leonid I.

    1996-12-01

    In this paper, a novel approach is proposed, which allows for an efficient reduction of the amount of visual data required for representing structural information in the image. This is a multistage architecture which investigates partial correlations between structural image components. Mathematical description of the multistage hierarchical processing is provided, together with the network architecture. Initially the image is partitioned to be processed in parallel channels. In each channel, the structural components are transformed and subsequently separated, depending on their structural significance, to be then combined with the components from other channels for further processing. The output result is represented as a pattern vector, whose components are computed one at a time to allow the quickest possible response. The input gray- scale image is transformed before the processing begins, so that each pixel contains information about the spatial structure of its neighborhood. The most correlated information is extracted first, making the algorithm tolerant to minor structural changes.

  4. Introducing PLIA: Planetary Laboratory for Image Analysis

    NASA Astrophysics Data System (ADS)

    Peralta, J.; Hueso, R.; Barrado, N.; Sánchez-Lavega, A.

    2005-08-01

    We present a graphical software tool developed under IDL software to navigate, process and analyze planetary images. The software has a complete Graphical User Interface and is cross-platform. It can also run under the IDL Virtual Machine without the need to own an IDL license. The set of tools included allow image navigation (orientation, centring and automatic limb determination), dynamical and photometric atmospheric measurements (winds and cloud albedos), cylindrical and polar projections, as well as image treatment under several procedures. Being written in IDL, it is modular and easy to modify and grow for adding new capabilities. We show several examples of the software capabilities with Galileo-Venus observations: Image navigation, photometrical corrections, wind profiles obtained by cloud tracking, cylindrical projections and cloud photometric measurements. Acknowledgements: This work has been funded by Spanish MCYT PNAYA2003-03216, fondos FEDER and Grupos UPV 15946/2004. R. Hueso acknowledges a post-doc fellowship from Gobierno Vasco.

  5. 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

  6. MR brain image analysis in dementia: From quantitative imaging biomarkers to ageing brain models and imaging genetics.

    PubMed

    Niessen, Wiro J

    2016-10-01

    MR brain image analysis has constantly been a hot topic research area in medical image analysis over the past two decades. In this article, it is discussed how the field developed from the construction of tools for automatic quantification of brain morphology, function, connectivity and pathology, to creating models of the ageing brain in normal ageing and disease, and tools for integrated analysis of imaging and genetic data. The current and future role of the field in improved understanding of the development of neurodegenerative disease is discussed, and its potential for aiding in early and differential diagnosis and prognosis of different types of dementia. For the latter, the use of reference imaging data and reference models derived from large clinical and population imaging studies, and the application of machine learning techniques on these reference data, are expected to play a key role. PMID:27344937

  7. Radar images analysis for scattering surfaces characterization

    NASA Astrophysics Data System (ADS)

    Piazza, Enrico

    1998-10-01

    According to the different problems and techniques related to the detection and recognition of airplanes and vehicles moving on the Airport surface, the present work mainly deals with the processing of images gathered by a high-resolution radar sensor. The radar images used to test the investigated algorithms are relative to sequence of images obtained in some field experiments carried out by the Electronic Engineering Department of the University of Florence. The radar is the Ka band radar operating in the'Leonardo da Vinci' Airport in Fiumicino (Rome). The images obtained from the radar scan converter are digitized and putted in x, y, (pixel) co- ordinates. For a correct matching of the images, these are corrected in true geometrical co-ordinates (meters) on the basis of fixed points on an airport map. Correlating the airplane 2-D multipoint template with actual radar images, the value of the signal in the points involved in the template can be extracted. Results for a lot of observation show a typical response for the main section of the fuselage and the wings. For the fuselage, the back-scattered echo is low at the prow, became larger near the center on the aircraft and than it decrease again toward the tail. For the wings the signal is growing with a pretty regular slope from the fuselage to the tips, where the signal is the strongest.

  8. Localised manifold learning for cardiac image analysis

    NASA Astrophysics Data System (ADS)

    Bhatia, Kanwal K.; Price, Anthony N.; Hajnal, Jo V.; Rueckert, Daniel

    2012-02-01

    Manifold learning is increasingly being used to discover the underlying structure of medical image data. Traditional approaches operate on whole images with a single measure of similarity used to compare entire images. In this way, information on the locality of differences is lost and smaller trends may be masked by dominant global differences. In this paper, we propose the use of multiple local manifolds to analyse regions of images without any prior knowledge of which regions are important. Localised manifolds are created by partitioning images into regular subsections with a manifold constructed for each patch. We propose a framework for incorporating information from the neighbours of each patch to calculate a coherent embedding. This generates a simultaneous dimensionality reduction of all patches and results in the creation of embeddings which are spatially-varying. Additionally, a hierarchical method is presented to enable a multi-scale embedding solution. We use this to extract spatially-varying respiratory and cardiac motions from cardiac MRI. Although there is a complex interplay between these motions, we show how they can be separated on a regional basis. We demonstrate the utility of the localised joint embedding over a global embedding of whole images and over embedding individual patches independently.

  9. 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

  10. 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. PMID:27141917

  11. Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging

    NASA Astrophysics Data System (ADS)

    Spinelli, Antonello E.; Boschi, Federico

    2011-12-01

    Clustering analysis (CA) and principal component analysis (PCA) were applied to dynamic Cerenkov luminescence images (dCLI). In order to investigate the performances of the proposed approaches, two distinct dynamic data sets obtained by injecting mice with 32P-ATP and 18F-FDG were acquired using the IVIS 200 optical imager. The k-means clustering algorithm has been applied to dCLI and was implemented using interactive data language 8.1. We show that cluster analysis allows us to obtain good agreement between the clustered and the corresponding emission regions like the bladder, the liver, and the tumor. We also show a good correspondence between the time activity curves of the different regions obtained by using CA and manual region of interest analysis on dCLIT and PCA images. We conclude that CA provides an automatic unsupervised method for the analysis of preclinical dynamic Cerenkov luminescence image data.

  12. Digital Image Analysis for DETCHIP(®) Code Determination.

    PubMed

    Lyon, Marcus; Wilson, Mark V; Rouhier, Kerry A; Symonsbergen, David J; Bastola, Kiran; Thapa, Ishwor; Holmes, Andrea E; Sikich, Sharmin M; Jackson, Abby

    2012-08-01

    DETECHIP(®) is a molecular sensing array used for identification of a large variety of substances. Previous methodology for the analysis of DETECHIP(®) used human vision to distinguish color changes induced by the presence of the analyte of interest. This paper describes several analysis techniques using digital images of DETECHIP(®). Both a digital camera and flatbed desktop photo scanner were used to obtain Jpeg images. Color information within these digital images was obtained through the measurement of red-green-blue (RGB) values using software such as GIMP, Photoshop and ImageJ. Several different techniques were used to evaluate these color changes. It was determined that the flatbed scanner produced in the clearest and more reproducible images. Furthermore, codes obtained using a macro written for use within ImageJ showed improved consistency versus pervious methods. PMID:25267940

  13. Anima: modular workflow system for comprehensive image data analysis.

    PubMed

    Rantanen, Ville; Valori, Miko; Hautaniemi, Sampsa

    2014-01-01

    Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima. PMID:25126541

  14. Anima: Modular Workflow System for Comprehensive Image Data Analysis

    PubMed Central

    Rantanen, Ville; Valori, Miko; Hautaniemi, Sampsa

    2014-01-01

    Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima. PMID:25126541

  15. Basic research planning in mathematical pattern recognition and image analysis

    NASA Technical Reports Server (NTRS)

    Bryant, J.; Guseman, L. F., Jr.

    1981-01-01

    Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis.

  16. An Analysis of the Magneto-Optic Imaging System

    NASA Technical Reports Server (NTRS)

    Nath, Shridhar

    1996-01-01

    The Magneto-Optic Imaging system is being used for the detection of defects in airframes and other aircraft structures. The system has been successfully applied to detecting surface cracks, but has difficulty in the detection of sub-surface defects such as corrosion. The intent of the grant was to understand the physics of the MOI better, in order to use it effectively for detecting corrosion and for classifying surface defects. Finite element analysis, image classification, and image processing are addressed.

  17. Uncooled LWIR imaging: applications and market analysis

    NASA Astrophysics Data System (ADS)

    Takasawa, Satomi

    2015-05-01

    The evolution of infrared (IR) imaging sensor technology for defense market has played an important role in developing commercial market, as dual use of the technology has expanded. In particular, technologies of both reduction in pixel pitch and vacuum package have drastically evolved in the area of uncooled Long-Wave IR (LWIR; 8-14 μm wavelength region) imaging sensor, increasing opportunity to create new applications. From the macroscopic point of view, the uncooled LWIR imaging market is divided into two areas. One is a high-end market where uncooled LWIR imaging sensor with sensitivity as close to that of cooled one as possible is required, while the other is a low-end market which is promoted by miniaturization and reduction in price. Especially, in the latter case, approaches towards consumer market have recently appeared, such as applications of uncooled LWIR imaging sensors to night visions for automobiles and smart phones. The appearance of such a kind of commodity surely changes existing business models. Further technological innovation is necessary for creating consumer market, and there will be a room for other companies treating components and materials such as lens materials and getter materials and so on to enter into the consumer market.

  18. Continuous-wave terahertz scanning image resolution analysis and restoration

    NASA Astrophysics Data System (ADS)

    Li, Qi; Yin, Qiguo; Yao, Rui; Ding, Shenghui; Wang, Qi

    2010-03-01

    Resolution of continuous-wave (CW) terahertz scanning image is limited by many factors among which the aperture effect of finite focus diameter is very important. We have investigated the factors that affect terahertz (THz) image resolution in details through theory analysis and simulation. On the other hand, in order to enhance THz image resolution, Richardson-Lucy algorithm has been introduced as a promising approach to improve image details. By analyzing the imaging theory, it is proposed that intensity distribution function of actual THz laser focal spot can be approximatively used as point spread function (PSF) in the restoration algorithm. The focal spot image could be obtained by applying the pyroelectric camera, and mean filtering result of the focal spot image is used as the PSF. Simulation and experiment show that the algorithm implemented is comparatively effective.

  19. Multi-Scale Fractal Analysis of Image Texture and Pattern

    NASA Technical Reports Server (NTRS)

    Emerson, Charles W.; Lam, Nina Siu-Ngan; Quattrochi, Dale A.

    1999-01-01

    Analyses of the fractal dimension of Normalized Difference Vegetation Index (NDVI) images of homogeneous land covers near Huntsville, Alabama revealed that the fractal dimension of an image of an agricultural land cover indicates greater complexity as pixel size increases, a forested land cover gradually grows smoother, and an urban image remains roughly self-similar over the range of pixel sizes analyzed (10 to 80 meters). A similar analysis of Landsat Thematic Mapper images of the East Humboldt Range in Nevada taken four months apart show a more complex relation between pixel size and fractal dimension. The major visible difference between the spring and late summer NDVI images is the absence of high elevation snow cover in the summer image. This change significantly alters the relation between fractal dimension and pixel size. The slope of the fractal dimension-resolution relation provides indications of how image classification or feature identification will be affected by changes in sensor spatial resolution.

  20. Multi-Scale Fractal Analysis of Image Texture and Pattern

    NASA Technical Reports Server (NTRS)

    Emerson, Charles W.; Lam, Nina Siu-Ngan; Quattrochi, Dale A.

    1999-01-01

    Analyses of the fractal dimension of Normalized Difference Vegetation Index (NDVI) images of homogeneous land covers near Huntsville, Alabama revealed that the fractal dimension of an image of an agricultural land cover indicates greater complexity as pixel size increases, a forested land cover gradually grows smoother, and an urban image remains roughly self-similar over the range of pixel sizes analyzed (10 to 80 meters). A similar analysis of Landsat Thematic Mapper images of the East Humboldt Range in Nevada taken four months apart show a more complex relation between pixel size and fractal dimension. The major visible difference between the spring and late summer NDVI images of the absence of high elevation snow cover in the summer image. This change significantly alters the relation between fractal dimension and pixel size. The slope of the fractal dimensional-resolution relation provides indications of how image classification or feature identification will be affected by changes in sensor spatial resolution.

  1. Optical image acquisition system for colony analysis

    NASA Astrophysics Data System (ADS)

    Wang, Weixing; Jin, Wenbiao

    2006-02-01

    For counting of both colonies and plaques, there is a large number of applications including food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing, AMES testing, pharmaceuticals, paints, sterile fluids and fungal contamination. Recently, many researchers and developers have made efforts for this kind of systems. By investigation, some existing systems have some problems since they belong to a new technology product. One of the main problems is image acquisition. In order to acquire colony images with good quality, an illumination box was constructed as: the box includes front lightning and back lightning, which can be selected by users based on properties of colony dishes. With the illumination box, lightning can be uniform; colony dish can be put in the same place every time, which make image processing easy. A digital camera in the top of the box connected to a PC computer with a USB cable, all the camera functions are controlled by the computer.

  2. System Matrix Analysis for Computed Tomography Imaging

    PubMed Central

    Flores, Liubov; Vidal, Vicent; Verdú, Gumersindo

    2015-01-01

    In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data. PMID:26575482

  3. System Matrix Analysis for Computed Tomography Imaging.

    PubMed

    Flores, Liubov; Vidal, Vicent; Verdú, Gumersindo

    2015-01-01

    In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data. PMID:26575482

  4. [Analysis of bone tissues by intravital imaging].

    PubMed

    Mizuno, Hiroki; Yamashita, Erika; Ishii, Masaru

    2016-05-01

    In recent years,"the fluorescent imaging techniques"has made rapid advances, it has become possible to observe the dynamics of living cells in individuals or tissues. It has been considered that it is extremely difficult to observe the living bone marrow directly because bone marrow is surrounded by a hard calcareous. But now, we established a method for observing the cells constituting the bone marrow of living mice in real time by the use of the intravital two-photon imaging system. In this article, we show the latest data and the reports about the hematopoietic stem cells and the leukemia cells by using the intravital imaging techniques, and also discuss its further application. PMID:27117619

  5. Texture Analysis for Classification of Risat-Ii Images

    NASA Astrophysics Data System (ADS)

    Chakraborty, D.; Thakur, S.; Jeyaram, A.; Krishna Murthy, Y. V. N.; Dadhwal, V. K.

    2012-08-01

    RISAT-II or Radar Imaging satellite - II is a microwave-imaging satellite lunched by ISRO to take images of the earth during day and night as well as all weather condition. This satellite enhances the ISRO's capability for disaster management application together with forestry, agricultural, urban and oceanographic applications. The conventional pixel based classification technique cannot classify these type of images since it do not take into account the texture information of the image. This paper presents a method to classify the high-resolution RISAT-II microwave images based on texture analysis. It suppress the speckle noise from the microwave image before analysis the texture of the image since speckle is essentially a form of noise, which degrades the quality of an image; make interpretation (visual or digital) more difficult. A local adaptive median filter is developed that uses local statistics to detect the speckle noise of microwave image and to replace it with a local median value. Local Binary Pattern (LBP) operator is proposed to measure the texture around each pixel of the speckle suppressed microwave image. It considers a series of circles (2D) centered on the pixel with incremental radius values and the intersected pixels on the perimeter of the circles of radius r (where r = 1, 3 and 5) are used for measuring the LBP of the center pixel. The significance of LBP is that it measure the texture around each pixel of the image and computationally simple. ISODATA method is used to cluster the transformed LBP image. The proposed method adequately classifies RISAT-II X band microwave images without human intervention.

  6. Disability in Physical Education Textbooks: An Analysis of Image Content

    ERIC Educational Resources Information Center

    Taboas-Pais, Maria Ines; Rey-Cao, Ana

    2012-01-01

    The aim of this paper is to show how images of disability are portrayed in physical education textbooks for secondary schools in Spain. The sample was composed of 3,316 images published in 36 textbooks by 10 publishing houses. A content analysis was carried out using a coding scheme based on categories employed in other similar studies and adapted…

  7. An Online Image Analysis Tool for Science Education

    ERIC Educational Resources Information Center

    Raeside, L.; Busschots, B.; Waddington, S.; Keating, J. G.

    2008-01-01

    This paper describes an online image analysis tool developed as part of an iterative, user-centered development of an online Virtual Learning Environment (VLE) called the Education through Virtual Experience (EVE) Portal. The VLE provides a Web portal through which schoolchildren and their teachers create scientific proposals, retrieve images and…

  8. Higher Education Institution Image: A Correspondence Analysis Approach.

    ERIC Educational Resources Information Center

    Ivy, Jonathan

    2001-01-01

    Investigated how marketing is used to convey higher education institution type image in the United Kingdom and South Africa. Using correspondence analysis, revealed the unique positionings created by old and new universities and technikons in these countries. Also identified which marketing tools they use in conveying their image. (EV)

  9. Four challenges in medical image analysis from an industrial perspective.

    PubMed

    Weese, Jürgen; Lorenz, Cristian

    2016-10-01

    Today's medical imaging systems produce a huge amount of images containing a wealth of information. However, the information is hidden in the data and image analysis algorithms are needed to extract it, to make it readily available for medical decisions and to enable an efficient work flow. Advances in medical image analysis over the past 20 years mean there are now many algorithms and ideas available that allow to address medical image analysis tasks in commercial solutions with sufficient performance in terms of accuracy, reliability and speed. At the same time new challenges have arisen. Firstly, there is a need for more generic image analysis technologies that can be efficiently adapted for a specific clinical task. Secondly, efficient approaches for ground truth generation are needed to match the increasing demands regarding validation and machine learning. Thirdly, algorithms for analyzing heterogeneous image data are needed. Finally, anatomical and organ models play a crucial role in many applications, and algorithms to construct patient-specific models from medical images with a minimum of user interaction are needed. These challenges are complementary to the on-going need for more accurate, more reliable and faster algorithms, and dedicated algorithmic solutions for specific applications. PMID:27344939

  10. Ringed impact craters on Venus: An analysis from Magellan images

    NASA Technical Reports Server (NTRS)

    Alexopoulos, Jim S.; Mckinnon, William B.

    1992-01-01

    We have analyzed cycle 1 Magellan images covering approximately 90 percent of the venusian surface and have identified 55 unequivocal peak-ring craters and multiringed impact basins. This comprehensive study (52 peak-ring craters and at least 3 multiringed impact basins) complements our earlier independent analysis of Arecibo and Venera images and initial Magellan data and that of the Magellan team.

  11. Geopositioning Precision Analysis of Multiple Image Triangulation Using Lro Nac Lunar Images

    NASA Astrophysics Data System (ADS)

    Di, K.; Xu, B.; Liu, B.; Jia, M.; Liu, Z.

    2016-06-01

    This paper presents an empirical analysis of the geopositioning precision of multiple image triangulation using Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images at the Chang'e-3(CE-3) landing site. Nine LROC NAC images are selected for comparative analysis of geopositioning precision. Rigorous sensor models of the images are established based on collinearity equations with interior and exterior orientation elements retrieved from the corresponding SPICE kernels. Rational polynomial coefficients (RPCs) of each image are derived by least squares fitting using vast number of virtual control points generated according to rigorous sensor models. Experiments of different combinations of images are performed for comparisons. The results demonstrate that the plane coordinates can achieve a precision of 0.54 m to 2.54 m, with a height precision of 0.71 m to 8.16 m when only two images are used for three-dimensional triangulation. There is a general trend that the geopositioning precision, especially the height precision, is improved with the convergent angle of the two images increasing from several degrees to about 50°. However, the image matching precision should also be taken into consideration when choosing image pairs for triangulation. The precisions of using all the 9 images are 0.60 m, 0.50 m, 1.23 m in along-track, cross-track, and height directions, which are better than most combinations of two or more images. However, triangulation with selected fewer images could produce better precision than that using all the images.

  12. Analysis of PETT images in psychiatric disorders

    SciTech Connect

    Brodie, J.D.; Gomez-Mont, F.; Volkow, N.D.; Corona, J.F.; Wolf, A.P.; Wolkin, A.; Russell, J.A.G.; Christman, D.; Jaeger, J.

    1983-01-01

    A quantitative method is presented for studying the pattern of metabolic activity in a set of Positron Emission Transaxial Tomography (PETT) images. Using complex Fourier coefficients as a feature vector for each image, cluster, principal components, and discriminant function analyses are used to empirically describe metabolic differences between control subjects and patients with DSM III diagnosis for schizophrenia or endogenous depression. We also present data on the effects of neuroleptic treatment on the local cerebral metabolic rate of glucose utilization (LCMRGI) in a group of chronic schizophrenics using the region of interest approach. 15 references, 4 figures, 3 tables.

  13. SLAR image interpretation keys for geographic analysis

    NASA Technical Reports Server (NTRS)

    Coiner, J. C.

    1972-01-01

    A means for side-looking airborne radar (SLAR) imagery to become a more widely used data source in geoscience and agriculture is suggested by providing interpretation keys as an easily implemented interpretation model. Interpretation problems faced by the researcher wishing to employ SLAR are specifically described, and the use of various types of image interpretation keys to overcome these problems is suggested. With examples drawn from agriculture and vegetation mapping, direct and associate dichotomous image interpretation keys are discussed and methods of constructing keys are outlined. Initial testing of the keys, key-based automated decision rules, and the role of the keys in an information system for agriculture are developed.

  14. Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis.

    PubMed

    Downie, H F; Adu, M O; Schmidt, S; Otten, W; Dupuy, L X; White, P J; Valentine, T A

    2015-07-01

    The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions. PMID:25211059

  15. Spatially Weighted Principal Component Analysis for Imaging Classification

    PubMed Central

    Guo, Ruixin; Ahn, Mihye; Zhu, Hongtu

    2014-01-01

    The aim of this paper is to develop a supervised dimension reduction framework, called Spatially Weighted Principal Component Analysis (SWPCA), for high dimensional imaging classification. Two main challenges in imaging classification are the high dimensionality of the feature space and the complex spatial structure of imaging data. In SWPCA, we introduce two sets of novel weights including global and local spatial weights, which enable a selective treatment of individual features and incorporation of the spatial structure of imaging data and class label information. We develop an e cient two-stage iterative SWPCA algorithm and its penalized version along with the associated weight determination. We use both simulation studies and real data analysis to evaluate the finite-sample performance of our SWPCA. The results show that SWPCA outperforms several competing principal component analysis (PCA) methods, such as supervised PCA (SPCA), and other competing methods, such as sparse discriminant analysis (SDA). PMID:26089629

  16. Electron Microscopy and Image Analysis for Selected Materials

    NASA Technical Reports Server (NTRS)

    Williams, George

    1999-01-01

    This particular project was completed in collaboration with the metallurgical diagnostics facility. The objective of this research had four major components. First, we required training in the operation of the environmental scanning electron microscope (ESEM) for imaging of selected materials including biological specimens. The types of materials range from cyanobacteria and diatoms to cloth, metals, sand, composites and other materials. Second, to obtain training in surface elemental analysis technology using energy dispersive x-ray (EDX) analysis, and in the preparation of x-ray maps of these same materials. Third, to provide training for the staff of the metallurgical diagnostics and failure analysis team in the area of image processing and image analysis technology using NIH Image software. Finally, we were to assist in the sample preparation, observing, imaging, and elemental analysis for Mr. Richard Hoover, one of NASA MSFC's solar physicists and Marshall's principal scientist for the agency-wide virtual Astrobiology Institute. These materials have been collected from various places around the world including the Fox Tunnel in Alaska, Siberia, Antarctica, ice core samples from near Lake Vostoc, thermal vents in the ocean floor, hot springs and many others. We were successful in our efforts to obtain high quality, high resolution images of various materials including selected biological ones. Surface analyses (EDX) and x-ray maps were easily prepared with this technology. We also discovered and used some applications for NIH Image software in the metallurgical diagnostics facility.

  17. Automated Analysis of Mammography Phantom Images

    NASA Astrophysics Data System (ADS)

    Brooks, Kenneth Wesley

    The present work stems from the hypothesis that humans are inconsistent when making subjective analyses of images and that human decisions for moderately complex images may be performed by a computer with complete objectivity, once a human acceptance level has been established. The following goals were established to test the hypothesis: (1) investigate observer variability within the standard mammographic phantom evaluation process; (2) evaluate options for high-resolution image digitization and utilize the most appropriate technology for standard mammographic phantom film digitization; (3) develop a machine-based vision system for evaluating standard mammographic phantom images to eliminate effects of human variabilities; and (4) demonstrate the completed system's performance against human observers for accreditation and for manufacturing quality control of standard mammographic phantom images. The following methods and procedures were followed to achieve the goals of the research: (1) human variabilities in the American College of Radiology accreditation process were simulated by observer studies involving 30 medical physicists and these were compared to the same number of diagnostic radiologists and untrained control group of observers; (2) current digitization technologies were presented and performance test procedures were developed; three devices were tested which represented commercially available high, intermediate and low-end contrast and spatial resolution capabilities; (3) optimal image processing schemes were applied and tested which performed low, intermediate and high-level computer vision tasks; and (4) the completed system's performance was tested against human observers for accreditation and for manufacturing quality control of standard mammographic phantom images. The results from application of the procedures were as follows: (1) the simulated American College of Radiology mammography accreditation program phantom evaluation process demonstrated

  18. Non-Imaging Software/Data Analysis Requirements

    NASA Technical Reports Server (NTRS)

    1984-01-01

    The analysis software needs of the non-imaging planetary data user are discussed. Assumptions as to the nature of the planetary science data centers where the data are physically stored are advanced, the scope of the non-imaging data is outlined, and facilities that users are likely to need to define and access data are identified. Data manipulation and analysis needs and display graphics are discussed.

  19. Image analysis in dual modality tomography for material classification

    NASA Astrophysics Data System (ADS)

    Basarab-Horwath, I.; Daniels, A. T.; Green, R. G.

    2001-08-01

    A dual modality tomographic system is described for material classification in a simulated multi-component flow regime. It combines two tomographic modalities, electrical current and light, to image the interrogated area. Derived image parameters did not allow material classification. PCA analysis was performed on this data set producing a new parameter set, which allowed material classification. This procedure reduces the dimensionality of the data set and also offers a pre-processing technique prior to analysis by another classifier.

  20. Analysis on correlation imaging based on fractal interpolation

    NASA Astrophysics Data System (ADS)

    Li, Bailing; Zhang, Wenwen; Chen, Qian; Gu, Guohua

    2015-10-01

    One fractal interpolation algorithm has been discussed in detail and the statistical self-similarity characteristics of light field have been analized in correlated experiment. For the correlation imaging experiment in condition of low sampling frequent, an image analysis approach based on fractal interpolation algorithm is proposed. This approach aims to improve the resolution of original image which contains a fewer number of pixels and highlight the image contour feature which is fuzzy. By using this method, a new model for the light field has been established. For the case of different moments of the intensity in the receiving plane, the local field division also has been established and then the iterated function system based on the experimental data set can be obtained by choosing the appropriate compression ratio under a scientific error estimate. On the basis of the iterative function, an explicit fractal interpolation function expression is given out in this paper. The simulation results show that the correlation image reconstructed by fractal interpolation has good approximations to the original image. The number of pixels of image after interpolation is significantly increased. This method will effectively solve the difficulty of image pixel deficiency and significantly improved the outline of objects in the image. The rate of deviation as the parameter has been adopted in the paper in order to evaluate objectively the effect of the algorithm. To sum up, fractal interpolation method proposed in this paper not only keeps the overall image but also increases the local information of the original image.

  1. Image analysis of dye stained patterns in soils

    NASA Astrophysics Data System (ADS)

    Bogner, Christina; Trancón y Widemann, Baltasar; Lange, Holger

    2013-04-01

    Quality of surface water and groundwater is directly affected by flow processes in the unsaturated zone. In general, it is difficult to measure or model water flow. Indeed, parametrization of hydrological models is problematic and often no unique solution exists. To visualise flow patterns in soils directly dye tracer studies can be done. These experiments provide images of stained soil profiles and their evaluation demands knowledge in hydrology as well as in image analysis and statistics. First, these photographs are converted to binary images classifying the pixels in dye stained and non-stained ones. Then, some feature extraction is necessary to discern relevant hydrological information. In our study we propose to use several index functions to extract different (ideally complementary) features. We associate each image row with a feature vector (i.e. a certain number of image function values) and use these features to cluster the image rows to identify similar image areas. Because images of stained profiles might have different reasonable clusterings, we calculate multiple consensus clusterings. An expert can explore these different solutions and base his/her interpretation of predominant flow mechanisms on quantitative (objective) criteria. The complete workflow from reading-in binary images to final clusterings has been implemented in the free R system, a language and environment for statistical computing. The calculation of image indices is part of our own package Indigo, manipulation of binary images, clustering and visualization of results are done using either build-in facilities in R, additional R packages or the LATEX system.

  2. Segmentation and learning in the quantitative analysis of microscopy images

    NASA Astrophysics Data System (ADS)

    Ruggiero, Christy; Ross, Amy; Porter, Reid

    2015-02-01

    In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and there is a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional "objects" within these images. These are challenging problems for image processing because of the variability in object appearance that inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways to address these challenges is interactive image segmentation. These algorithms are designed to incorporate input from a human operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key tool in a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms have tailored segmentation on an image-by-image basis, and information derived from operator input is not transferred between images. But recently there has been increasing interest to use machine learning in segmentation to provide interactive tools that accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce the need for operator input over time, and can potentially provide a more dynamic balance between customization and automation for different applications. This paper reviews the state of the art in this area, provides a unified view of these algorithms, and compares the segmentation performance of various design choices.

  3. The ImageJ ecosystem: An open platform for biomedical image analysis.

    PubMed

    Schindelin, Johannes; Rueden, Curtis T; Hiner, Mark C; Eliceiri, Kevin W

    2015-01-01

    Technology in microscopy advances rapidly, enabling increasingly affordable, faster, and more precise quantitative biomedical imaging, which necessitates correspondingly more-advanced image processing and analysis techniques. A wide range of software is available-from commercial to academic, special-purpose to Swiss army knife, small to large-but a key characteristic of software that is suitable for scientific inquiry is its accessibility. Open-source software is ideal for scientific endeavors because it can be freely inspected, modified, and redistributed; in particular, the open-software platform ImageJ has had a huge impact on the life sciences, and continues to do so. From its inception, ImageJ has grown significantly due largely to being freely available and its vibrant and helpful user community. Scientists as diverse as interested hobbyists, technical assistants, students, scientific staff, and advanced biology researchers use ImageJ on a daily basis, and exchange knowledge via its dedicated mailing list. Uses of ImageJ range from data visualization and teaching to advanced image processing and statistical analysis. The software's extensibility continues to attract biologists at all career stages as well as computer scientists who wish to effectively implement specific image-processing algorithms. In this review, we use the ImageJ project as a case study of how open-source software fosters its suites of software tools, making multitudes of image-analysis technology easily accessible to the scientific community. We specifically explore what makes ImageJ so popular, how it impacts the life sciences, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem. PMID:26153368

  4. Image Segmentation Analysis for NASA Earth Science Applications

    NASA Technical Reports Server (NTRS)

    Tilton, James C.

    2010-01-01

    NASA collects large volumes of imagery data from satellite-based Earth remote sensing sensors. Nearly all of the computerized image analysis of this data is performed pixel-by-pixel, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the full information content from the high spatial resolution image data that s now becoming increasingly available from these sensors. The field of object-based image analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. The Recursive Hierarchical Segmentation (RHSEG) software developed by the author is being used to facilitate moving from pixel-based image analysis to OBIA. The key unique aspect of RHSEG is that it tightly intertwines region growing segmentation, which produces spatially connected region objects, with region object classification, which groups sets of region objects together into region classes. No other practical, operational image segmentation approach has this tight integration of region growing object finding with region classification This integration is made possible by the recursive, divide-and-conquer implementation utilized by RHSEG, in which the input image data is recursively subdivided until the image data sections are small enough to successfully mitigat the combinatorial explosion caused by the need to compute the dissimilarity between each pair of image pixels. RHSEG's tight integration of region growing object finding and region classification is what enables the high spatial fidelity of the image segmentations produced by RHSEG. This presentation will provide an overview of the RHSEG algorithm and describe how it is currently being used to support OBIA or Earth Science applications such as snow/ice mapping and finding archaeological sites from remotely sensed data.

  5. Hyperspectral image analysis using artificial color

    NASA Astrophysics Data System (ADS)

    Fu, Jian; Caulfield, H. John; Wu, Dongsheng; Tadesse, Wubishet

    2010-03-01

    By definition, HSC (HyperSpectral Camera) images are much richer in spectral data than, say, a COTS (Commercial-Off-The-Shelf) color camera. But data are not information. If we do the task right, useful information can be derived from the data in HSC images. Nature faced essentially the identical problem. The incident light is so complex spectrally that measuring it with high resolution would provide far more data than animals can handle in real time. Nature's solution was to do irreversible POCS (Projections Onto Convex Sets) to achieve huge reductions in data with minimal reduction in information. Thus we can arrange for our manmade systems to do what nature did - project the HSC image onto two or more broad, overlapping curves. The task we have undertaken in the last few years is to develop this idea that we call Artificial Color. What we report here is the use of the measured HSC image data projected onto two or three convex, overlapping, broad curves in analogy with the sensitivity curves of human cone cells. Testing two quite different HSC images in that manner produced the desired result: good discrimination or segmentation that can be done very simply and hence are likely to be doable in real time with specialized computers. Using POCS on the HSC data to reduce the processing complexity produced excellent discrimination in those two cases. For technical reasons discussed here, the figures of merit for the kind of pattern recognition we use is incommensurate with the figures of merit of conventional pattern recognition. We used some force fitting to make a comparison nevertheless, because it shows what is also obvious qualitatively. In our tasks our method works better.

  6. Analysis of Multipath Pixels in SAR Images

    NASA Astrophysics Data System (ADS)

    Zhao, J. W.; Wu, J. C.; Ding, X. L.; Zhang, L.; Hu, F. M.

    2016-06-01

    As the received radar signal is the sum of signal contributions overlaid in one single pixel regardless of the travel path, the multipath effect should be seriously tackled as the multiple bounce returns are added to direct scatter echoes which leads to ghost scatters. Most of the existing solution towards the multipath is to recover the signal propagation path. To facilitate the signal propagation simulation process, plenty of aspects such as sensor parameters, the geometry of the objects (shape, location, orientation, mutual position between adjacent buildings) and the physical parameters of the surface (roughness, correlation length, permittivity)which determine the strength of radar signal backscattered to the SAR sensor should be given in previous. However, it's not practical to obtain the highly detailed object model in unfamiliar area by field survey as it's a laborious work and time-consuming. In this paper, SAR imaging simulation based on RaySAR is conducted at first aiming at basic understanding of multipath effects and for further comparison. Besides of the pre-imaging simulation, the product of the after-imaging, which refers to radar images is also taken into consideration. Both Cosmo-SkyMed ascending and descending SAR images of Lupu Bridge in Shanghai are used for the experiment. As a result, the reflectivity map and signal distribution map of different bounce level are simulated and validated by 3D real model. The statistic indexes such as the phase stability, mean amplitude, amplitude dispersion, coherence and mean-sigma ratio in case of layover are analyzed with combination of the RaySAR output.

  7. Synthetic aperture sonar imaging using joint time-frequency analysis

    NASA Astrophysics Data System (ADS)

    Wang, Genyuan; Xia, Xiang-Gen

    1999-03-01

    The non-ideal motion of the hydrophone usually induces the aperture error of the synthetic aperture sonar (SAS), which is one of the most important factors degrading the SAS imaging quality. In the SAS imaging, the return signals are usually nonstationary due to the non-ideal hydrophone motion. In this paper, joint time-frequency analysis (JTFA), as a good technique for analyzing nonstationary signals, is used in the SAS imaging. Based on the JTFA of the sonar return signals, a novel SAS imaging algorithm is proposed. The algorithm is verified by simulation examples.

  8. Independent component analysis applications on THz sensing and imaging

    NASA Astrophysics Data System (ADS)

    Balci, Soner; Maleski, Alexander; Nascimento, Matheus Mello; Philip, Elizabath; Kim, Ju-Hyung; Kung, Patrick; Kim, Seongsin M.

    2016-05-01

    We report Independent Component Analysis (ICA) technique applied to THz spectroscopy and imaging to achieve a blind source separation. A reference water vapor absorption spectrum was extracted via ICA, then ICA was utilized on a THz spectroscopic image in order to clean the absorption of water molecules from each pixel. For this purpose, silica gel was chosen as the material of interest for its strong water absorption. The resulting image clearly showed that ICA effectively removed the water content in the detected signal allowing us to image the silica gel beads distinctively even though it was totally embedded in water before ICA was applied.

  9. Fiji - an Open Source platform for biological image analysis

    PubMed Central

    Schindelin, Johannes; Arganda-Carreras, Ignacio; Frise, Erwin; Kaynig, Verena; Longair, Mark; Pietzsch, Tobias; Preibisch, Stephan; Rueden, Curtis; Saalfeld, Stephan; Schmid, Benjamin; Tinevez, Jean-Yves; White, Daniel James; Hartenstein, Volker; Eliceiri, Kevin; Tomancak, Pavel; Cardona, Albert

    2013-01-01

    Fiji is a distribution of the popular Open Source software ImageJ focused on biological image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image processing algorithms. Fiji facilitates the transformation of novel algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities. PMID:22743772

  10. Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images

    NASA Astrophysics Data System (ADS)

    Kavzoglu, T.; Yildiz, M.

    2014-09-01

    Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.

  11. (Hyper)-graphical models in biomedical image analysis.

    PubMed

    Paragios, Nikos; Ferrante, Enzo; Glocker, Ben; Komodakis, Nikos; Parisot, Sarah; Zacharaki, Evangelia I

    2016-10-01

    Computational vision, visual computing and biomedical image analysis have made tremendous progress over the past two decades. This is mostly due the development of efficient learning and inference algorithms which allow better and richer modeling of image and visual understanding tasks. Hyper-graph representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the importance of such representations, discuss their strength and limitations, provide appropriate strategies for their inference and present their application to address a variety of problems in biomedical image analysis. PMID:27377331

  12. The Land Analysis System (LAS) for multispectral image processing

    USGS Publications Warehouse

    Wharton, S. W.; Lu, Y. C.; Quirk, Bruce K.; Oleson, Lyndon R.; Newcomer, J. A.; Irani, Frederick M.

    1988-01-01

    The Land Analysis System (LAS) is an interactive software system available in the public domain for the analysis, display, and management of multispectral and other digital image data. LAS provides over 240 applications functions and utilities, a flexible user interface, complete online and hard-copy documentation, extensive image-data file management, reformatting, conversion utilities, and high-level device independent access to image display hardware. The authors summarize the capabilities of the current release of LAS (version 4.0) and discuss plans for future development. Particular emphasis is given to the issue of system portability and the importance of removing and/or isolating hardware and software dependencies.

  13. Texture analysis on MRI images of non-Hodgkin lymphoma.

    PubMed

    Harrison, L; Dastidar, P; Eskola, H; Järvenpää, R; Pertovaara, H; Luukkaala, T; Kellokumpu-Lehtinen, P-L; Soimakallio, S

    2008-04-01

    The aim here is to show that texture parameters of magnetic resonance imaging (MRI) data changes in lymphoma tissue during chemotherapy. Ten patients having non-Hodgkin lymphoma masses in the abdomen were imaged for chemotherapy response evaluation three consecutive times. The analysis was performed with MaZda texture analysis (TA) application. The best discrimination in lymphoma MRI texture was obtained within T2-weighted images between the pre-treatment and the second response evaluation stage. TA proved to be a promising quantitative means of representing lymphoma tissue changes during medication follow-up. PMID:18342845

  14. Infrared thermal facial image sequence registration analysis and verification

    NASA Astrophysics Data System (ADS)

    Chen, Chieh-Li; Jian, Bo-Lin

    2015-03-01

    To study the emotional responses of subjects to the International Affective Picture System (IAPS), infrared thermal facial image sequence is preprocessed for registration before further analysis such that the variance caused by minor and irregular subject movements is reduced. Without affecting the comfort level and inducing minimal harm, this study proposes an infrared thermal facial image sequence registration process that will reduce the deviations caused by the unconscious head shaking of the subjects. A fixed image for registration is produced through the localization of the centroid of the eye region as well as image translation and rotation processes. Thermal image sequencing will then be automatically registered using the two-stage genetic algorithm proposed. The deviation before and after image registration will be demonstrated by image quality indices. The results show that the infrared thermal image sequence registration process proposed in this study is effective in localizing facial images accurately, which will be beneficial to the correlation analysis of psychological information related to the facial area.

  15. Pathology imaging informatics for quantitative analysis of whole-slide images

    PubMed Central

    Kothari, Sonal; Phan, John H; Stokes, Todd H; Wang, May D

    2013-01-01

    Objectives With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. Target audience This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. Scope First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality. PMID:23959844

  16. Image analysis and compression: renewed focus on texture

    NASA Astrophysics Data System (ADS)

    Pappas, Thrasyvoulos N.; Zujovic, Jana; Neuhoff, David L.

    2010-01-01

    We argue that a key to further advances in the fields of image analysis and compression is a better understanding of texture. We review a number of applications that critically depend on texture analysis, including image and video compression, content-based retrieval, visual to tactile image conversion, and multimodal interfaces. We introduce the idea of "structurally lossless" compression of visual data that allows significant differences between the original and decoded images, which may be perceptible when they are viewed side-by-side, but do not affect the overall quality of the image. We then discuss the development of objective texture similarity metrics, which allow substantial point-by-point deviations between textures that according to human judgment are essentially identical.

  17. Cloud based toolbox for image analysis, processing and reconstruction tasks.

    PubMed

    Bednarz, Tomasz; Wang, Dadong; Arzhaeva, Yulia; Lagerstrom, Ryan; Vallotton, Pascal; Burdett, Neil; Khassapov, Alex; Szul, Piotr; Chen, Shiping; Sun, Changming; Domanski, Luke; Thompson, Darren; Gureyev, Timur; Taylor, John A

    2015-01-01

    This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au . PMID:25381109

  18. Segmented infrared image analysis for rotating machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Duan, Lixiang; Yao, Mingchao; Wang, Jinjiang; Bai, Tangbo; Zhang, Laibin

    2016-07-01

    As a noncontact and non-intrusive technique, infrared image analysis becomes promising for machinery defect diagnosis. However, the insignificant information and strong noise in infrared image limit its performance. To address this issue, this paper presents an image segmentation approach to enhance the feature extraction in infrared image analysis. A region selection criterion named dispersion degree is also formulated to discriminate fault representative regions from unrelated background information. Feature extraction and fusion methods are then applied to obtain features from selected regions for further diagnosis. Experimental studies on a rotor fault simulator demonstrate that the presented segmented feature enhancement approach outperforms the one from the original image using both Naïve Bayes classifier and support vector machine.

  19. Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images.

    PubMed

    Khansari, Maziyar M; O'Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

    2016-07-01

    The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method's discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692

  20. Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images

    PubMed Central

    Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

    2016-01-01

    The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692

  1. Multispectral image analysis for algal biomass quantification.

    PubMed

    Murphy, Thomas E; Macon, Keith; Berberoglu, Halil

    2013-01-01

    This article reports a novel multispectral image processing technique for rapid, noninvasive quantification of biomass concentration in attached and suspended algae cultures. Monitoring the biomass concentration is critical for efficient production of biofuel feedstocks, food supplements, and bioactive chemicals. Particularly, noninvasive and rapid detection techniques can significantly aid in providing delay-free process control feedback in large-scale cultivation platforms. In this technique, three-band spectral images of Anabaena variabilis cultures were acquired and separated into their red, green, and blue components. A correlation between the magnitude of the green component and the areal biomass concentration was generated. The correlation predicted the biomass concentrations of independently prepared attached and suspended cultures with errors of 7 and 15%, respectively, and the effect of varying lighting conditions and background color were investigated. This method can provide necessary feedback for dilution and harvesting strategies to maximize photosynthetic conversion efficiency in large-scale operation. PMID:23554374

  2. Measurement and analysis of image sensors

    NASA Astrophysics Data System (ADS)

    Vitek, Stanislav

    2005-06-01

    For astronomical applications is necessary to have high precision in sensing and processing the image data. In this time are used the large CCD sensors from the various reasons. For the replacement of CCD sensors with CMOS sensing devices is important to know transfer characteristics of used CCD sensors. In the special applications like the robotic telescopes (fully automatic, without human interactions) seems to be good using of specially designed smart sensors, which have integrated more functions and have more features than CCDs.

  3. Computerized microscopic image analysis of follicular lymphoma

    NASA Astrophysics Data System (ADS)

    Sertel, Olcay; Kong, Jun; Lozanski, Gerard; Catalyurek, Umit; Saltz, Joel H.; Gurcan, Metin N.

    2008-03-01

    Follicular Lymphoma (FL) is a cancer arising from the lymphatic system. Originating from follicle center B cells, FL is mainly comprised of centrocytes (usually middle-to-small sized cells) and centroblasts (relatively large malignant cells). According to the World Health Organization's recommendations, there are three histological grades of FL characterized by the number of centroblasts per high-power field (hpf) of area 0.159 mm2. In current practice, these cells are manually counted from ten representative fields of follicles after visual examination of hematoxylin and eosin (H&E) stained slides by pathologists. Several studies clearly demonstrate the poor reproducibility of this grading system with very low inter-reader agreement. In this study, we are developing a computerized system to assist pathologists with this process. A hybrid approach that combines information from several slides with different stains has been developed. Thus, follicles are first detected from digitized microscopy images with immunohistochemistry (IHC) stains, (i.e., CD10 and CD20). The average sensitivity and specificity of the follicle detection tested on 30 images at 2×, 4× and 8× magnifications are 85.5+/-9.8% and 92.5+/-4.0%, respectively. Since the centroblasts detection is carried out in the H&E-stained slides, the follicles in the IHC-stained images are mapped to H&E-stained counterparts. To evaluate the centroblast differentiation capabilities of the system, 11 hpf images have been marked by an experienced pathologist who identified 41 centroblast cells and 53 non-centroblast cells. A non-supervised clustering process differentiates the centroblast cells from noncentroblast cells, resulting in 92.68% sensitivity and 90.57% specificity.

  4. Multispectral/hyperspectral image enhancement for biological cell analysis

    SciTech Connect

    Nuffer, Lisa L.; Medvick, Patricia A.; Foote, Harlan P.; Solinsky, James C.

    2006-08-01

    The paper shows new techniques for analyzing cell images taken with a microscope using multiple filters to form a datacube of spectral image planes. Because of the many neighboring spectral samples, much of the datacube appears as redundant, similar tissue. The analysis is based on the nonGaussian statistics of the image data, allowing for remapping of the data into image components that are dissimilar, and hence isolate subtle, spatial object regions of interest in the tissues. This individual component image set can be recombined into a single RGB color image useful in real-time location of regions of interest. The algorithms are susceptible to parallelization using Field Programmable Gate Array hardware processing.

  5. Performance analysis for geometrical attack on digital image watermarking

    NASA Astrophysics Data System (ADS)

    Jayanthi, VE.; Rajamani, V.; Karthikayen, P.

    2011-11-01

    We present a technique for irreversible watermarking approach robust to affine transform attacks in camera, biomedical and satellite images stored in the form of monochrome bitmap images. The watermarking approach is based on image normalisation in which both watermark embedding and extraction are carried out with respect to an image normalised to meet a set of predefined moment criteria. The normalisation procedure is invariant to affine transform attacks. The result of watermarking scheme is suitable for public watermarking applications, where the original image is not available for watermark extraction. Here, direct-sequence code division multiple access approach is used to embed multibit text information in DCT and DWT transform domains. The proposed watermarking schemes are robust against various types of attacks such as Gaussian noise, shearing, scaling, rotation, flipping, affine transform, signal processing and JPEG compression. Performance analysis results are measured using image processing metrics.

  6. Method for measuring anterior chamber volume by image analysis

    NASA Astrophysics Data System (ADS)

    Zhai, Gaoshou; Zhang, Junhong; Wang, Ruichang; Wang, Bingsong; Wang, Ningli

    2007-12-01

    Anterior chamber volume (ACV) is very important for an oculist to make rational pathological diagnosis as to patients who have some optic diseases such as glaucoma and etc., yet it is always difficult to be measured accurately. In this paper, a method is devised to measure anterior chamber volumes based on JPEG-formatted image files that have been transformed from medical images using the anterior-chamber optical coherence tomographer (AC-OCT) and corresponding image-processing software. The corresponding algorithms for image analysis and ACV calculation are implemented in VC++ and a series of anterior chamber images of typical patients are analyzed, while anterior chamber volumes are calculated and are verified that they are in accord with clinical observation. It shows that the measurement method is effective and feasible and it has potential to improve accuracy of ACV calculation. Meanwhile, some measures should be taken to simplify the handcraft preprocess working as to images.

  7. Analysis of pregerminated barley using hyperspectral image analysis.

    PubMed

    Arngren, Morten; Hansen, Per Waaben; Eriksen, Birger; Larsen, Jan; Larsen, Rasmus

    2011-11-01

    Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel's lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI(95%): 29-35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI(95%): 0-15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley. PMID:21932866

  8. Seismoelectric beamforming imaging: a sensitivity analysis

    NASA Astrophysics Data System (ADS)

    El Khoury, P.; Revil, A.; Sava, P.

    2015-06-01

    The electrical current density generated by the propagation of a seismic wave at the interface characterized by a drop in electrical, hydraulic or mechanical properties produces an electrical field of electrokinetic nature. This field can be measured remotely with a signal-to-noise ratio depending on the background noise and signal attenuation. The seismoelectric beamforming approach is an emerging imaging technique based on scanning a porous material using appropriately delayed seismic sources. The idea is to focus the hydromechanical energy on a regular spatial grid and measure the converted electric field remotely at each focus time. This method can be used to image heterogeneities with a high definition and to provide structural information to classical geophysical methods. A numerical experiment is performed to investigate the resolution of the seismoelectric beamforming approach with respect to the main wavelength of the seismic waves. The 2-D model consists of a fictitious water-filled bucket in which a cylindrical sandstone core sample is set up vertically. The hydrophones/seismic sources are located on a 50-cm diameter circle in the bucket and the seismic energy is focused on the grid points in order to scan the medium and determine the geometry of the porous plug using the output electric potential image. We observe that the resolution of the method is given by a density of eight scanning points per wavelength. Additional numerical tests were also performed to see the impact of a wrong velocity model upon the seismoelectric map displaying the heterogeneities of the material.

  9. 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.

  10. New approach to gallbladder ultrasonic images analysis and lesions recognition.

    PubMed

    Bodzioch, Sławomir; Ogiela, Marek R

    2009-03-01

    This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards detection of disease symptoms on processed images. First, in this paper, there is presented a new method of filtering gallbladder contours from USG images. A major stage in this filtration is to segment and section off areas occupied by the said organ. In most cases this procedure is based on filtration that plays a key role in the process of diagnosing pathological changes. Unfortunately ultrasound images present among the most troublesome methods of analysis owing to the echogenic inconsistency of structures under observation. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours. The algorithm is based on rank filtration, as well as on the analysis of histogram sections on tested organs. The second part concerns detecting lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. Usually the final stage is to make a diagnosis based on the detected symptoms. This last stage can be carried out through either dedicated expert systems or more classic pattern analysis approach like using rules to determine illness basing on detected symptoms. This paper discusses the pattern analysis algorithms for gallbladder image interpretation towards classification of the most frequent illness symptoms of this organ. PMID:19124224