The Pan-STARRS PS1 Image Processing Pipeline

NASA Astrophysics Data System (ADS)

Magnier, E.

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

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

PubMed

Kostopoulos, Spiros; Ravazoula, Panagiota; Asvestas, Pantelis; Kalatzis, Ioannis; Xenogiannopoulos, George; Cavouras, Dionisis; Glotsos, Dimitris

2017-06-01

Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.

An Approach to Knowledge-Directed Image Analysis,

DTIC Science & Technology

1977-09-01

34AN APPROACH TO KNOWLEDGE -DIRECTED IMAGE ANALYSIS D.H. Ballard, C.M.’Brown, J.A. Feldman Computer Science Department iThe University of Rochester...Rochester, New York 14627 DTII EECTE UTIC FILE COPY o n I, n 83 - ’ f t 8 11 28 19 1f.. AN APPROACH TO KNOWLEDGE -DIRECTED IMAGE ANALYSIS 5*., D.H...semantic network model and a distributed control structure to accomplish the image analysis process. The process of " understanding an image" leads to

Volumetric image interpretation in radiology: scroll behavior and cognitive processes.

PubMed

den Boer, Larissa; van der Schaaf, Marieke F; Vincken, Koen L; Mol, Chris P; Stuijfzand, Bobby G; van der Gijp, Anouk

2018-05-16

The interpretation of medical images is a primary task for radiologists. Besides two-dimensional (2D) images, current imaging technologies allow for volumetric display of medical images. Whereas current radiology practice increasingly uses volumetric images, the majority of studies on medical image interpretation is conducted on 2D images. The current study aimed to gain deeper insight into the volumetric image interpretation process by examining this process in twenty radiology trainees who all completed four volumetric image cases. Two types of data were obtained concerning scroll behaviors and think-aloud data. Types of scroll behavior concerned oscillations, half runs, full runs, image manipulations, and interruptions. Think-aloud data were coded by a framework of knowledge and skills in radiology including three cognitive processes: perception, analysis, and synthesis. Relating scroll behavior to cognitive processes showed that oscillations and half runs coincided more often with analysis and synthesis than full runs, whereas full runs coincided more often with perception than oscillations and half runs. Interruptions were characterized by synthesis and image manipulations by perception. In addition, we investigated relations between cognitive processes and found an overall bottom-up way of reasoning with dynamic interactions between cognitive processes, especially between perception and analysis. In sum, our results highlight the dynamic interactions between these processes and the grounding of cognitive processes in scroll behavior. It suggests, that the types of scroll behavior are relevant to describe how radiologists interact with and manipulate volumetric images.

Digital image processing and analysis for activated sludge wastewater treatment.

PubMed

Khan, Muhammad Burhan; Lee, Xue Yong; Nisar, Humaira; Ng, Choon Aun; Yeap, Kim Ho; Malik, Aamir Saeed

2015-01-01

Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.

Image Analysis, Microscopic, and Spectrochemical Study of the PVC Dry Blending Process,

DTIC Science & Technology

The dry blending process used in the production of electrical grade pvc formulations has been studies using a combination of image analysis , microscopic...by image analysis techniques. Optical and scanning electron microscopy were used to assess morphological differences. Spectrochemical techniques were used to indicate chemical changes.

Methods in Astronomical Image Processing

NASA Astrophysics Data System (ADS)

Jörsäter, S.

A Brief Introductory Note History of Astronomical Imaging Astronomical Image Data Images in Various Formats Digitized Image Data Digital Image Data Philosophy of Astronomical Image Processing Properties of Digital Astronomical Images Human Image Processing Astronomical vs. Computer Science Image Processing Basic Tools of Astronomical Image Processing Display Applications Calibration of Intensity Scales Calibration of Length Scales Image Re-shaping Feature Enhancement Noise Suppression Noise and Error Analysis Image Processing Packages: Design of AIPS and MIDAS AIPS MIDAS Reduction of CCD Data Bias Subtraction Clipping Preflash Subtraction Dark Subtraction Flat Fielding Sky Subtraction Extinction Correction Deconvolution Methods Rebinning/Combining Summary and Prospects for the Future

Viewpoints on Medical Image Processing: From Science to Application

PubMed Central

Deserno (né Lehmann), Thomas M.; Handels, Heinz; Maier-Hein (né Fritzsche), Klaus H.; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-01-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment. PMID:24078804

Viewpoints on Medical Image Processing: From Science to Application.

PubMed

Deserno Né Lehmann, Thomas M; Handels, Heinz; Maier-Hein Né Fritzsche, Klaus H; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-05-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.

Looking back to inform the future: The role of cognition in forest disturbance characterization from remote sensing imagery

NASA Astrophysics Data System (ADS)

Bianchetti, Raechel Anne

Remotely sensed images have become a ubiquitous part of our daily lives. From novice users, aiding in search and rescue missions using tools such as TomNod, to trained analysts, synthesizing disparate data to address complex problems like climate change, imagery has become central to geospatial problem solving. Expert image analysts are continually faced with rapidly developing sensor technologies and software systems. In response to these cognitively demanding environments, expert analysts develop specialized knowledge and analytic skills to address increasingly complex problems. This study identifies the knowledge, skills, and analytic goals of expert image analysts tasked with identification of land cover and land use change. Analysts participating in this research are currently working as part of a national level analysis of land use change, and are well versed with the use of TimeSync, forest science, and image analysis. The results of this study benefit current analysts as it improves their awareness of their mental processes used during the image interpretation process. The study also can be generalized to understand the types of knowledge and visual cues that analysts use when reasoning with imagery for purposes beyond land use change studies. Here a Cognitive Task Analysis framework is used to organize evidence from qualitative knowledge elicitation methods for characterizing the cognitive aspects of the TimeSync image analysis process. Using a combination of content analysis, diagramming, semi-structured interviews, and observation, the study highlights the perceptual and cognitive elements of expert remote sensing interpretation. Results show that image analysts perform several standard cognitive processes, but flexibly employ these processes in response to various contextual cues. Expert image analysts' ability to think flexibly during their analysis process was directly related to their amount of image analysis experience. Additionally, results show that the basic Image Interpretation Elements continue to be important despite technological augmentation of the interpretation process. These results are used to derive a set of design guidelines for developing geovisual analytic tools and training to support image analysis.

APPLEPIPS /Apple Personal Image Processing System/ - An interactive digital image processing system for the Apple II microcomputer

NASA Technical Reports Server (NTRS)

Masuoka, E.; Rose, J.; Quattromani, M.

1981-01-01

Recent developments related to microprocessor-based personal computers have made low-cost digital image processing systems a reality. Image analysis systems built around these microcomputers provide color image displays for images as large as 256 by 240 pixels in sixteen colors. Descriptive statistics can be computed for portions of an image, and supervised image classification can be obtained. The systems support Basic, Fortran, Pascal, and assembler language. A description is provided of a system which is representative of the new microprocessor-based image processing systems currently on the market. While small systems may never be truly independent of larger mainframes, because they lack 9-track tape drives, the independent processing power of the microcomputers will help alleviate some of the turn-around time problems associated with image analysis and display on the larger multiuser systems.

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

PubMed

Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

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

PubMed

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

2013-07-15

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

Heuristic Analysis Model of Nitrided Layers’ Formation Consisting of the Image Processing and Analysis and Elements of Artificial Intelligence

PubMed Central

Wójcicki, Tomasz; Nowicki, Michał

2016-01-01

The article presents a selected area of research and development concerning the methods of material analysis based on the automatic image recognition of the investigated metallographic sections. The objectives of the analyses of the materials for gas nitriding technology are described. The methods of the preparation of nitrided layers, the steps of the process and the construction and operation of devices for gas nitriding are given. We discuss the possibility of using the methods of digital images processing in the analysis of the materials, as well as their essential task groups: improving the quality of the images, segmentation, morphological transformations and image recognition. The developed analysis model of the nitrided layers formation, covering image processing and analysis techniques, as well as selected methods of artificial intelligence are presented. The model is divided into stages, which are formalized in order to better reproduce their actions. The validation of the presented method is performed. The advantages and limitations of the developed solution, as well as the possibilities of its practical use, are listed. PMID:28773389

Image data processing system requirements study. Volume 1: Analysis. [for Earth Resources Survey Program

NASA Technical Reports Server (NTRS)

Honikman, T.; Mcmahon, E.; Miller, E.; Pietrzak, L.; Yorsz, W.

1973-01-01

Digital image processing, image recorders, high-density digital data recorders, and data system element processing for use in an Earth Resources Survey image data processing system are studied. Loading to various ERS systems is also estimated by simulation.

An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

PubMed

Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan; Wang, Yaming; Xia, Shunren; Yang, Zhong

2014-08-01

Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. © 2014 Wiley Periodicals, Inc.

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

Development of image analysis software for quantification of viable cells in microchips.

PubMed

Georg, Maximilian; Fernández-Cabada, Tamara; Bourguignon, Natalia; Karp, Paola; Peñaherrera, Ana B; Helguera, Gustavo; Lerner, Betiana; Pérez, Maximiliano S; Mertelsmann, Roland

2018-01-01

Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.

A New Color Image Encryption Scheme Using CML and a Fractional-Order Chaotic System

PubMed Central

Wu, Xiangjun; Li, Yang; Kurths, Jürgen

2015-01-01

The chaos-based image cryptosystems have been widely investigated in recent years to provide real-time encryption and transmission. In this paper, a novel color image encryption algorithm by using coupled-map lattices (CML) and a fractional-order chaotic system is proposed to enhance the security and robustness of the encryption algorithms with a permutation-diffusion structure. To make the encryption procedure more confusing and complex, an image division-shuffling process is put forward, where the plain-image is first divided into four sub-images, and then the position of the pixels in the whole image is shuffled. In order to generate initial conditions and parameters of two chaotic systems, a 280-bit long external secret key is employed. The key space analysis, various statistical analysis, information entropy analysis, differential analysis and key sensitivity analysis are introduced to test the security of the new image encryption algorithm. The cryptosystem speed is analyzed and tested as well. Experimental results confirm that, in comparison to other image encryption schemes, the new algorithm has higher security and is fast for practical image encryption. Moreover, an extensive tolerance analysis of some common image processing operations such as noise adding, cropping, JPEG compression, rotation, brightening and darkening, has been performed on the proposed image encryption technique. Corresponding results reveal that the proposed image encryption method has good robustness against some image processing operations and geometric attacks. PMID:25826602

Spatial recurrence analysis: A sensitive and fast detection tool in digital mammography

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

Prado, T. L.; Galuzio, P. P.; Lopes, S. R.

Efficient diagnostics of breast cancer requires fast digital mammographic image processing. Many breast lesions, both benign and malignant, are barely visible to the untrained eye and requires accurate and reliable methods of image processing. We propose a new method of digital mammographic image analysis that meets both needs. It uses the concept of spatial recurrence as the basis of a spatial recurrence quantification analysis, which is the spatial extension of the well-known time recurrence analysis. The recurrence-based quantifiers are able to evidence breast lesions in a way as good as the best standard image processing methods available, but with amore » better control over the spurious fragments in the image.« less

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.

SlideJ: An ImageJ plugin for automated processing of whole slide images.

PubMed

Della Mea, Vincenzo; Baroni, Giulia L; Pilutti, David; Di Loreto, Carla

2017-01-01

The digital slide, or Whole Slide Image, is a digital image, acquired with specific scanners, that represents a complete tissue sample or cytological specimen at microscopic level. While Whole Slide image analysis is recognized among the most interesting opportunities, the typical size of such images-up to Gpixels- can be very demanding in terms of memory requirements. Thus, while algorithms and tools for processing and analysis of single microscopic field images are available, Whole Slide images size makes the direct use of such tools prohibitive or impossible. In this work a plugin for ImageJ, named SlideJ, is proposed with the objective to seamlessly extend the application of image analysis algorithms implemented in ImageJ for single microscopic field images to a whole digital slide analysis. The plugin has been complemented by examples of macro in the ImageJ scripting language to demonstrate its use in concrete situations.

Quantitative assessment of the impact of biomedical image acquisition on the results obtained from image analysis and processing.

PubMed

Koprowski, Robert

2014-07-04

Dedicated, automatic algorithms for image analysis and processing are becoming more and more common in medical diagnosis. When creating dedicated algorithms, many factors must be taken into consideration. They are associated with selecting the appropriate algorithm parameters and taking into account the impact of data acquisition on the results obtained. An important feature of algorithms is the possibility of their use in other medical units by other operators. This problem, namely operator's (acquisition) impact on the results obtained from image analysis and processing, has been shown on a few examples. The analysed images were obtained from a variety of medical devices such as thermal imaging, tomography devices and those working in visible light. The objects of imaging were cellular elements, the anterior segment and fundus of the eye, postural defects and others. In total, almost 200'000 images coming from 8 different medical units were analysed. All image analysis algorithms were implemented in C and Matlab. For various algorithms and methods of medical imaging, the impact of image acquisition on the results obtained is different. There are different levels of algorithm sensitivity to changes in the parameters, for example: (1) for microscope settings and the brightness assessment of cellular elements there is a difference of 8%; (2) for the thyroid ultrasound images there is a difference in marking the thyroid lobe area which results in a brightness assessment difference of 2%. The method of image acquisition in image analysis and processing also affects: (3) the accuracy of determining the temperature in the characteristic areas on the patient's back for the thermal method - error of 31%; (4) the accuracy of finding characteristic points in photogrammetric images when evaluating postural defects - error of 11%; (5) the accuracy of performing ablative and non-ablative treatments in cosmetology - error of 18% for the nose, 10% for the cheeks, and 7% for the forehead. Similarly, when: (7) measuring the anterior eye chamber - there is an error of 20%; (8) measuring the tooth enamel thickness - error of 15%; (9) evaluating the mechanical properties of the cornea during pressure measurement - error of 47%. The paper presents vital, selected issues occurring when assessing the accuracy of designed automatic algorithms for image analysis and processing in bioengineering. The impact of acquisition of images on the problems arising in their analysis has been shown on selected examples. It has also been indicated to which elements of image analysis and processing special attention should be paid in their design.

Graphical user interface for image acquisition and processing

DOEpatents

Goldberg, Kenneth A.

2002-01-01

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

Performance of an image analysis processing system for hen tracking in an environmental preference chamber.

PubMed

Kashiha, Mohammad Amin; Green, Angela R; Sales, Tatiana Glogerley; Bahr, Claudia; Berckmans, Daniel; Gates, Richard S

2014-10-01

Image processing systems have been widely used in monitoring livestock for many applications, including identification, tracking, behavior analysis, occupancy rates, and activity calculations. The primary goal of this work was to quantify image processing performance when monitoring laying hens by comparing length of stay in each compartment as detected by the image processing system with the actual occurrences registered by human observations. In this work, an image processing system was implemented and evaluated for use in an environmental animal preference chamber to detect hen navigation between 4 compartments of the chamber. One camera was installed above each compartment to produce top-view images of the whole compartment. An ellipse-fitting model was applied to captured images to detect whether the hen was present in a compartment. During a choice-test study, mean ± SD success detection rates of 95.9 ± 2.6% were achieved when considering total duration of compartment occupancy. These results suggest that the image processing system is currently suitable for determining the response measures for assessing environmental choices. Moreover, the image processing system offered a comprehensive analysis of occupancy while substantially reducing data processing time compared with the time-intensive alternative of manual video analysis. The above technique was used to monitor ammonia aversion in the chamber. As a preliminary pilot study, different levels of ammonia were applied to different compartments while hens were allowed to navigate between compartments. Using the automated monitor tool to assess occupancy, a negative trend of compartment occupancy with ammonia level was revealed, though further examination is needed. ©2014 Poultry Science Association Inc.

SlideJ: An ImageJ plugin for automated processing of whole slide images

PubMed Central

Baroni, Giulia L.; Pilutti, David; Di Loreto, Carla

2017-01-01

The digital slide, or Whole Slide Image, is a digital image, acquired with specific scanners, that represents a complete tissue sample or cytological specimen at microscopic level. While Whole Slide image analysis is recognized among the most interesting opportunities, the typical size of such images—up to Gpixels- can be very demanding in terms of memory requirements. Thus, while algorithms and tools for processing and analysis of single microscopic field images are available, Whole Slide images size makes the direct use of such tools prohibitive or impossible. In this work a plugin for ImageJ, named SlideJ, is proposed with the objective to seamlessly extend the application of image analysis algorithms implemented in ImageJ for single microscopic field images to a whole digital slide analysis. The plugin has been complemented by examples of macro in the ImageJ scripting language to demonstrate its use in concrete situations. PMID:28683129

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Radiography for intensive care: participatory process analysis in a PACS-equipped and film/screen environment

NASA Astrophysics Data System (ADS)

Peer, Regina; Peer, Siegfried; Sander, Heike; Marsolek, Ingo; Koller, Wolfgang; Pappert, Dirk; Hierholzer, Johannes

2002-05-01

If new technology is introduced into medical practice it must prove to make a difference. However traditional approaches of outcome analysis failed to show a direct benefit of PACS on patient care and economical benefits are still in debate. A participatory process analysis was performed to compare workflow in a film based hospital and a PACS environment. This included direct observation of work processes, interview of involved staff, structural analysis and discussion of observations with staff members. After definition of common structures strong and weak workflow steps were evaluated. With a common workflow structure in both hospitals, benefits of PACS were revealed in workflow steps related to image reporting with simultaneous image access for ICU-physicians and radiologists, archiving of images as well as image and report distribution. However PACS alone is not able to cover the complete process of 'radiography for intensive care' from ordering of an image till provision of the final product equals image + report. Interference of electronic workflow with analogue process steps such as paper based ordering reduces the potential benefits of PACS. In this regard workflow modeling proved to be very helpful for the evaluation of complex work processes linking radiology and the ICU.

Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing.

PubMed

Mari, João Fernando; Saito, José Hiroki; Neves, Amanda Ferreira; Lotufo, Celina Monteiro da Cruz; Destro-Filho, João-Batista; Nicoletti, Maria do Carmo

2015-12-01

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis.

PubMed

Knecht, Avi C; Campbell, Malachy T; Caprez, Adam; Swanson, David R; Walia, Harkamal

2016-05-01

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

Parallel processing considerations for image recognition tasks

NASA Astrophysics Data System (ADS)

Simske, Steven J.

2011-01-01

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

High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately.

PubMed

Chen, Wenjin; Wong, Chung; Vosburgh, Evan; Levine, Arnold J; Foran, David J; Xu, Eugenia Y

2014-07-08

The increasing number of applications of three-dimensional (3D) tumor spheroids as an in vitro model for drug discovery requires their adaptation to large-scale screening formats in every step of a drug screen, including large-scale image analysis. Currently there is no ready-to-use and free image analysis software to meet this large-scale format. Most existing methods involve manually drawing the length and width of the imaged 3D spheroids, which is a tedious and time-consuming process. This study presents a high-throughput image analysis software application - SpheroidSizer, which measures the major and minor axial length of the imaged 3D tumor spheroids automatically and accurately; calculates the volume of each individual 3D tumor spheroid; then outputs the results in two different forms in spreadsheets for easy manipulations in the subsequent data analysis. The main advantage of this software is its powerful image analysis application that is adapted for large numbers of images. It provides high-throughput computation and quality-control workflow. The estimated time to process 1,000 images is about 15 min on a minimally configured laptop, or around 1 min on a multi-core performance workstation. The graphical user interface (GUI) is also designed for easy quality control, and users can manually override the computer results. The key method used in this software is adapted from the active contour algorithm, also known as Snakes, which is especially suitable for images with uneven illumination and noisy background that often plagues automated imaging processing in high-throughput screens. The complimentary "Manual Initialize" and "Hand Draw" tools provide the flexibility to SpheroidSizer in dealing with various types of spheroids and diverse quality images. This high-throughput image analysis software remarkably reduces labor and speeds up the analysis process. Implementing this software is beneficial for 3D tumor spheroids to become a routine in vitro model for drug screens in industry and academia.

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

PubMed

Saladra, Dawid; Kopernik, Magdalena

2016-10-01

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

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

NASA Technical Reports Server (NTRS)

Kruse, F. A.; Lefkoff, A. B.; Boardman, J. W.; Heidebrecht, K. B.; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H.

1992-01-01

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below.

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.

Processing Satellite Images on Tertiary Storage: A Study of the Impact of Tile Size on Performance

NASA Technical Reports Server (NTRS)

Yu, JieBing; DeWitt, David J.

1996-01-01

Before raw data from a satellite can be used by an Earth scientist, it must first undergo a number of processing steps including basic processing, cleansing, and geo-registration. Processing actually expands the volume of data collected by a factor of 2 or 3 and the original data is never deleted. Thus processing and storage requirements can exceed 2 terrabytes/day. Once processed data is ready for analysis, a series of algorithms (typically developed by the Earth scientists) is applied to a large number of images in a data set. The focus of this paper is how best to handle such images stored on tape using the following assumptions: (1) all images of interest to a scientist are stored on a single tape, (2) images are accessed and processed in the order that they are stored on tape, and (3) the analysis requires access to only a portion of each image and not the entire image.

LANDSAT-4 image data quality analysis for energy related applications. [nuclear power plant sites

NASA Technical Reports Server (NTRS)

Wukelic, G. E. (Principal Investigator)

1983-01-01

No useable LANDSAT 4 TM data were obtained for the Hanford site in the Columbia Plateau region, but TM simulator data for a Virginia Electric Company nuclear power plant was used to test image processing algorithms. Principal component analyses of this data set clearly indicated that thermal plumes in surface waters used for reactor cooling would be discrenible. Image processing and analysis programs were successfully testing using the 7 band Arkansas test scene and preliminary analysis of TM data for the Savanah River Plant shows that current interactive, image enhancement, analysis and integration techniques can be effectively used for LANDSAT 4 data. Thermal band data appear adequate for gross estimates of thermal changes occurring near operating nuclear facilities especially in surface water bodies being used for reactor cooling purposes. Additional image processing software was written and tested which provides for more rapid and effective analysis of the 7 band TM data.

Analysis of Variance in Statistical Image Processing

NASA Astrophysics Data System (ADS)

Kurz, Ludwik; Hafed Benteftifa, M.

1997-04-01

A key problem in practical image processing is the detection of specific features in a noisy image. Analysis of variance (ANOVA) techniques can be very effective in such situations, and this book gives a detailed account of the use of ANOVA in statistical image processing. The book begins by describing the statistical representation of images in the various ANOVA models. The authors present a number of computationally efficient algorithms and techniques to deal with such problems as line, edge, and object detection, as well as image restoration and enhancement. By describing the basic principles of these techniques, and showing their use in specific situations, the book will facilitate the design of new algorithms for particular applications. It will be of great interest to graduate students and engineers in the field of image processing and pattern recognition.

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.

Evidential Reasoning in Expert Systems for Image Analysis.

DTIC Science & Technology

1985-02-01

techniques to image analysis (IA). There is growing evidence that these techniques offer significant improvements in image analysis , particularly in the...2) to provide a common framework for analysis, (3) to structure the ER process for major expert-system tasks in image analysis , and (4) to identify...approaches to three important tasks for expert systems in the domain of image analysis . This segment concluded with an assessment of the strengths

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

Shoaf, S.; APS Engineering Support Division

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.

A comparison of performance of automatic cloud coverage assessment algorithm for Formosat-2 image using clustering-based and spatial thresholding methods

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2012-11-01

Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel's method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore, in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that include Otsu's, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method, for performance comparison. The result shows that Otsu's and GE methods both perform better than others for Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu's method as the threshoding method has successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.

Hyperspectral imaging in medicine: image pre-processing problems and solutions in Matlab.

PubMed

Koprowski, Robert

2015-11-01

The paper presents problems and solutions related to hyperspectral image pre-processing. New methods of preliminary image analysis are proposed. The paper shows problems occurring in Matlab when trying to analyse this type of images. Moreover, new methods are discussed which provide the source code in Matlab that can be used in practice without any licensing restrictions. The proposed application and sample result of hyperspectral image analysis. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Blood pulsation measurement using cameras operating in visible light: limitations.

PubMed

Koprowski, Robert

2016-10-03

The paper presents an automatic method for analysis and processing of images from a camera operating in visible light. This analysis applies to images containing the human facial area (body) and enables to measure the blood pulse rate. Special attention was paid to the limitations of this measurement method taking into account the possibility of using consumer cameras in real conditions (different types of lighting, different camera resolution, camera movement). The proposed new method of image analysis and processing was associated with three stages: (1) image pre-processing-allowing for the image filtration and stabilization (object location tracking); (2) main image processing-allowing for segmentation of human skin areas, acquisition of brightness changes; (3) signal analysis-filtration, FFT (Fast Fourier Transformation) analysis, pulse calculation. The presented algorithm and method for measuring the pulse rate has the following advantages: (1) it allows for non-contact and non-invasive measurement; (2) it can be carried out using almost any camera, including webcams; (3) it enables to track the object on the stage, which allows for the measurement of the heart rate when the patient is moving; (4) for a minimum of 40,000 pixels, it provides a measurement error of less than ±2 beats per minute for p < 0.01 and sunlight, or a slightly larger error (±3 beats per minute) for artificial lighting; (5) analysis of a single image takes about 40 ms in Matlab Version 7.11.0.584 (R2010b) with Image Processing Toolbox Version 7.1 (R2010b).

Information theoretic analysis of edge detection in visual communication

NASA Astrophysics Data System (ADS)

Jiang, Bo; Rahman, Zia-ur

2010-08-01

Generally, the designs of digital image processing algorithms and image gathering devices remain separate. Consequently, the performance of digital image processing algorithms is evaluated without taking into account the artifacts introduced into the process by the image gathering process. However, experiments show that the image gathering process profoundly impacts the performance of digital image processing and the quality of the resulting images. Huck et al. proposed one definitive theoretic analysis of visual communication channels, where the different parts, such as image gathering, processing, and display, are assessed in an integrated manner using Shannon's information theory. In this paper, we perform an end-to-end information theory based system analysis to assess edge detection methods. We evaluate the performance of the different algorithms as a function of the characteristics of the scene, and the parameters, such as sampling, additive noise etc., that define the image gathering system. The edge detection algorithm is regarded to have high performance only if the information rate from the scene to the edge approaches the maximum possible. This goal can be achieved only by jointly optimizing all processes. People generally use subjective judgment to compare different edge detection methods. There is not a common tool that can be used to evaluate the performance of the different algorithms, and to give people a guide for selecting the best algorithm for a given system or scene. Our information-theoretic assessment becomes this new tool to which allows us to compare the different edge detection operators in a common environment.

Comparison of approaches for mobile document image analysis using server supported smartphones

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-03-01

With the recent advances in mobile technologies, new capabilities are emerging, such as mobile document image analysis. However, mobile phones are still less powerful than servers, and they have some resource limitations. One approach to overcome these limitations is performing resource-intensive processes of the application on remote servers. In mobile document image analysis, the most resource consuming process is the Optical Character Recognition (OCR) process, which is used to extract text in mobile phone captured images. In this study, our goal is to compare the in-phone and the remote server processing approaches for mobile document image analysis in order to explore their trade-offs. For the inphone approach, all processes required for mobile document image analysis run on the mobile phone. On the other hand, in the remote-server approach, core OCR process runs on the remote server and other processes run on the mobile phone. Results of the experiments show that the remote server approach is considerably faster than the in-phone approach in terms of OCR time, but adds extra delays such as network delay. Since compression and downscaling of images significantly reduce file sizes and extra delays, the remote server approach overall outperforms the in-phone approach in terms of selected speed and correct recognition metrics, if the gain in OCR time compensates for the extra delays. According to the results of the experiments, using the most preferable settings, the remote server approach performs better than the in-phone approach in terms of speed and acceptable correct recognition metrics.

A modeling analysis program for the JPL table mountain Io sodium cloud data

NASA Technical Reports Server (NTRS)

Smyth, W. H.; Goldberg, B. A.

1984-01-01

A detailed review of 110 of the 263 Region B/C images of the 1981 data set is undertaken and a preliminary assessment of 39 images of the 1976-79 data set is presented. The basic spatial characteristics of these images are discussed. Modeling analysis of these images after further data processing will provide useful information about Io and the planetary magnetosphere. Plans for data processing and modeling analysis are outlined. Results of very preliminary modeling activities are presented.

Developing tools for digital radar image data evaluation

NASA Technical Reports Server (NTRS)

Domik, G.; Leberl, F.; Raggam, J.

1986-01-01

The refinement of radar image analysis methods has led to a need for a systems approach to radar image processing software. Developments stimulated through satellite radar are combined with standard image processing techniques to create a user environment to manipulate and analyze airborne and satellite radar images. One aim is to create radar products for the user from the original data to enhance the ease of understanding the contents. The results are called secondary image products and derive from the original digital images. Another aim is to support interactive SAR image analysis. Software methods permit use of a digital height model to create ortho images, synthetic images, stereo-ortho images, radar maps or color combinations of different component products. Efforts are ongoing to integrate individual tools into a combined hardware/software environment for interactive radar image analysis.

V-Sipal - a Virtual Laboratory for Satellite Image Processing and Analysis

NASA Astrophysics Data System (ADS)

Buddhiraju, K. M.; Eeti, L.; Tiwari, K. K.

2011-09-01

In this paper a virtual laboratory for the Satellite Image Processing and Analysis (v-SIPAL) being developed at the Indian Institute of Technology Bombay is described. v-SIPAL comprises a set of experiments that are normally carried out by students learning digital processing and analysis of satellite images using commercial software. Currently, the experiments that are available on the server include Image Viewer, Image Contrast Enhancement, Image Smoothing, Edge Enhancement, Principal Component Transform, Texture Analysis by Co-occurrence Matrix method, Image Indices, Color Coordinate Transforms, Fourier Analysis, Mathematical Morphology, Unsupervised Image Classification, Supervised Image Classification and Accuracy Assessment. The virtual laboratory includes a theory module for each option of every experiment, a description of the procedure to perform each experiment, the menu to choose and perform the experiment, a module on interpretation of results when performed with a given image and pre-specified options, bibliography, links to useful internet resources and user-feedback. The user can upload his/her own images for performing the experiments and can also reuse outputs of one experiment in another experiment where applicable. Some of the other experiments currently under development include georeferencing of images, data fusion, feature evaluation by divergence andJ-M distance, image compression, wavelet image analysis and change detection. Additions to the theory module include self-assessment quizzes, audio-video clips on selected concepts, and a discussion of elements of visual image interpretation. V-SIPAL is at the satge of internal evaluation within IIT Bombay and will soon be open to selected educational institutions in India for evaluation.

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.

Interactive Image Analysis System Design,

DTIC Science & Technology

1982-12-01

This report describes a design for an interactive image analysis system (IIAS), which implements terrain data extraction techniques. The design... analysis system. Additionally, the system is fully capable of supporting many generic types of image analysis and data processing, and is modularly...employs commercially available, state of the art minicomputers and image display devices with proven software to achieve a cost effective, reliable image

Counting pollen grains using readily available, free image processing and analysis software.

PubMed

Costa, Clayton M; Yang, Suann

2009-10-01

Although many methods exist for quantifying the number of pollen grains in a sample, there are few standard methods that are user-friendly, inexpensive and reliable. The present contribution describes a new method of counting pollen using readily available, free image processing and analysis software. Pollen was collected from anthers of two species, Carduus acanthoides and C. nutans (Asteraceae), then illuminated on slides and digitally photographed through a stereomicroscope. Using ImageJ (NIH), these digital images were processed to remove noise and sharpen individual pollen grains, then analysed to obtain a reliable total count of the number of grains present in the image. A macro was developed to analyse multiple images together. To assess the accuracy and consistency of pollen counting by ImageJ analysis, counts were compared with those made by the human eye. Image analysis produced pollen counts in 60 s or less per image, considerably faster than counting with the human eye (5-68 min). In addition, counts produced with the ImageJ procedure were similar to those obtained by eye. Because count parameters are adjustable, this image analysis protocol may be used for many other plant species. Thus, the method provides a quick, inexpensive and reliable solution to counting pollen from digital images, not only reducing the chance of error but also substantially lowering labour requirements.

PICASSO: an end-to-end image simulation tool for space and airborne imaging systems

NASA Astrophysics Data System (ADS)

Cota, Steve A.; Bell, Jabin T.; Boucher, Richard H.; Dutton, Tracy E.; Florio, Chris J.; Franz, Geoffrey A.; Grycewicz, Thomas J.; Kalman, Linda S.; Keller, Robert A.; Lomheim, Terrence S.; Paulson, Diane B.; Willkinson, Timothy S.

2008-08-01

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. Image chain analysis - the prediction of image quality from fundamental design parameters - is an important part of this design process. At The Aerospace Corporation we have been using a variety of image chain analysis tools for many years, the Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) among them. In this paper we describe our PICASSO tool, showing how, starting with a high quality input image and hypothetical design descriptions representative of the current state of the art in commercial imaging satellites, PICASSO can generate standard metrics of image quality in support of the decision processes of designers and program managers alike.

PICASSO: an end-to-end image simulation tool for space and airborne imaging systems

NASA Astrophysics Data System (ADS)

Cota, Stephen A.; Bell, Jabin T.; Boucher, Richard H.; Dutton, Tracy E.; Florio, Christopher J.; Franz, Geoffrey A.; Grycewicz, Thomas J.; Kalman, Linda S.; Keller, Robert A.; Lomheim, Terrence S.; Paulson, Diane B.; Wilkinson, Timothy S.

2010-06-01

The design of any modern imaging system is the end result of many trade studies, each seeking to optimize image quality within real world constraints such as cost, schedule and overall risk. Image chain analysis - the prediction of image quality from fundamental design parameters - is an important part of this design process. At The Aerospace Corporation we have been using a variety of image chain analysis tools for many years, the Parameterized Image Chain Analysis & Simulation SOftware (PICASSO) among them. In this paper we describe our PICASSO tool, showing how, starting with a high quality input image and hypothetical design descriptions representative of the current state of the art in commercial imaging satellites, PICASSO can generate standard metrics of image quality in support of the decision processes of designers and program managers alike.

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.

Numerical image manipulation and display in solar astronomy

NASA Technical Reports Server (NTRS)

Levine, R. H.; Flagg, J. C.

1977-01-01

The paper describes the system configuration and data manipulation capabilities of a solar image display system which allows interactive analysis of visual images and on-line manipulation of digital data. Image processing features include smoothing or filtering of images stored in the display, contrast enhancement, and blinking or flickering images. A computer with a core memory of 28,672 words provides the capacity to perform complex calculations based on stored images, including computing histograms, selecting subsets of images for further analysis, combining portions of images to produce images with physical meaning, and constructing mathematical models of features in an image. Some of the processing modes are illustrated by some image sequences from solar observations.

Multiscale Image Processing of Solar Image Data

NASA Astrophysics Data System (ADS)

Young, C.; Myers, D. C.

2001-12-01

It is often said that the blessing and curse of solar physics is too much data. Solar missions such as Yohkoh, SOHO and TRACE have shown us the Sun with amazing clarity but have also increased the amount of highly complex data. 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 several applications of multiscale techniques applied to solar image data. Specifically, we discuss uses of the wavelet, curvelet, and related transforms to define a multiresolution support for EIT, LASCO and TRACE images.

Sensor, signal, and image informatics - state of the art and current topics.

PubMed

Lehmann, T M; Aach, T; Witte, H

2006-01-01

The number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments. Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CT-based diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ. The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered. Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.

Eliminating "Hotspots" in Digital Image Processing

NASA Technical Reports Server (NTRS)

Salomon, P. M.

1984-01-01

Signals from defective picture elements rejected. Image processing program for use with charge-coupled device (CCD) or other mosaic imager augmented with algorithm that compensates for common type of electronic defect. Algorithm prevents false interpretation of "hotspots". Used for robotics, image enhancement, image analysis and digital television.

STAMPS: Software Tool for Automated MRI Post-processing on a supercomputer.

PubMed

Bigler, Don C; Aksu, Yaman; Miller, David J; Yang, Qing X

2009-08-01

This paper describes a Software Tool for Automated MRI Post-processing (STAMP) of multiple types of brain MRIs on a workstation and for parallel processing on a supercomputer (STAMPS). This software tool enables the automation of nonlinear registration for a large image set and for multiple MR image types. The tool uses standard brain MRI post-processing tools (such as SPM, FSL, and HAMMER) for multiple MR image types in a pipeline fashion. It also contains novel MRI post-processing features. The STAMP image outputs can be used to perform brain analysis using Statistical Parametric Mapping (SPM) or single-/multi-image modality brain analysis using Support Vector Machines (SVMs). Since STAMPS is PBS-based, the supercomputer may be a multi-node computer cluster or one of the latest multi-core computers.

Evaluation of clinical image processing algorithms used in digital mammography.

PubMed

Zanca, Federica; Jacobs, Jurgen; Van Ongeval, Chantal; Claus, Filip; Celis, Valerie; Geniets, Catherine; Provost, Veerle; Pauwels, Herman; Marchal, Guy; Bosmans, Hilde

2009-03-01

Screening is the only proven approach to reduce the mortality of breast cancer, but significant numbers of breast cancers remain undetected even when all quality assurance guidelines are implemented. With the increasing adoption of digital mammography systems, image processing may be a key factor in the imaging chain. Although to our knowledge statistically significant effects of manufacturer-recommended image processings have not been previously demonstrated, the subjective experience of our radiologists, that the apparent image quality can vary considerably between different algorithms, motivated this study. This article addresses the impact of five such algorithms on the detection of clusters of microcalcifications. A database of unprocessed (raw) images of 200 normal digital mammograms, acquired with the Siemens Novation DR, was collected retrospectively. Realistic simulated microcalcification clusters were inserted in half of the unprocessed images. All unprocessed images were subsequently processed with five manufacturer-recommended image processing algorithms (Agfa Musica 1, IMS Raffaello Mammo 1.2, Sectra Mamea AB Sigmoid, Siemens OPVIEW v2, and Siemens OPVIEW v1). Four breast imaging radiologists were asked to locate and score the clusters in each image on a five point rating scale. The free-response data were analyzed by the jackknife free-response receiver operating characteristic (JAFROC) method and, for comparison, also with the receiver operating characteristic (ROC) method. JAFROC analysis revealed highly significant differences between the image processings (F = 8.51, p < 0.0001), suggesting that image processing strongly impacts the detectability of clusters. Siemens OPVIEW2 and Siemens OPVIEW1 yielded the highest and lowest performances, respectively. ROC analysis of the data also revealed significant differences between the processing but at lower significance (F = 3.47, p = 0.0305) than JAFROC. Both statistical analysis methods revealed that the same six pairs of modalities were significantly different, but the JAFROC confidence intervals were about 32% smaller than ROC confidence intervals. This study shows that image processing has a significant impact on the detection of microcalcifications in digital mammograms. Objective measurements, such as described here, should be used by the manufacturers to select the optimal image processing algorithm.

Research and Analysis of Image Processing Technologies Based on DotNet Framework

NASA Astrophysics Data System (ADS)

Ya-Lin, Song; Chen-Xi, Bai

Microsoft.Net is a kind of most popular program development tool. This paper gave a detailed analysis concluded about some image processing technologies of the advantages and disadvantages by .Net processed image while the same algorithm is used in Programming experiments. The result shows that the two best efficient methods are unsafe pointer and Direct 3D, and Direct 3D used to 3D simulation development, and the others are useful in some fields while these technologies are poor efficiency and not suited to real-time processing. The experiment results in paper will help some projects about image processing and simulation based DotNet and it has strong practicability.

Pattern recognition and expert image analysis systems in biomedical image processing (Invited Paper)

NASA Astrophysics Data System (ADS)

Oosterlinck, A.; Suetens, P.; Wu, Q.; Baird, M.; F. M., C.

1987-09-01

This paper gives an overview of pattern recoanition techniques (P.R.) used in biomedical image processing and problems related to the different P.R. solutions. Also the use of knowledge based systems to overcome P.R. difficulties, is described. This is illustrated by a common example ofabiomedical image processing application.

Liver CT image processing: a short introduction of the technical elements.

PubMed

Masutani, Y; Uozumi, K; Akahane, Masaaki; Ohtomo, Kuni

2006-05-01

In this paper, we describe the technical aspects of image analysis for liver diagnosis and treatment, including the state-of-the-art of liver image analysis and its applications. After discussion on modalities for liver image analysis, various technical elements for liver image analysis such as registration, segmentation, modeling, and computer-assisted detection are covered with examples performed with clinical data sets. Perspective in the imaging technologies is also reviewed and discussed.

A simple 2D composite image analysis technique for the crystal growth study of L-ascorbic acid.

PubMed

Kumar, Krishan; Kumar, Virender; Lal, Jatin; Kaur, Harmeet; Singh, Jasbir

2017-06-01

This work was destined for 2D crystal growth studies of L-ascorbic acid using the composite image analysis technique. Growth experiments on the L-ascorbic acid crystals were carried out by standard (optical) microscopy, laser diffraction analysis, and composite image analysis. For image analysis, the growth of L-ascorbic acid crystals was captured as digital 2D RGB images, which were then processed to composite images. After processing, the crystal boundaries emerged as white lines against the black (cancelled) background. The crystal boundaries were well differentiated by peaks in the intensity graphs generated for the composite images. The lengths of crystal boundaries measured from the intensity graphs of composite images were in good agreement (correlation coefficient "r" = 0.99) with the lengths measured by standard microscopy. On the contrary, the lengths measured by laser diffraction were poorly correlated with both techniques. Therefore, the composite image analysis can replace the standard microscopy technique for the crystal growth studies of L-ascorbic acid. © 2017 Wiley Periodicals, Inc.

Image Processing Software

NASA Technical Reports Server (NTRS)

1990-01-01

The Ames digital image velocimetry technology has been incorporated in a commercially available image processing software package that allows motion measurement of images on a PC alone. The software, manufactured by Werner Frei Associates, is IMAGELAB FFT. IMAGELAB FFT is a general purpose image processing system with a variety of other applications, among them image enhancement of fingerprints and use by banks and law enforcement agencies for analysis of videos run during robberies.

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.

A Unified Mathematical Approach to Image Analysis.

DTIC Science & Technology

1987-08-31

describes four instances of the paradigm in detail. Directions for ongoing and future research are also indicated. Keywords: Image processing; Algorithms; Segmentation; Boundary detection; tomography; Global image analysis .

Dedicated computer system AOTK for image processing and analysis of horse navicular bone

NASA Astrophysics Data System (ADS)

Zaborowicz, M.; Fojud, A.; Koszela, K.; Mueller, W.; Górna, K.; Okoń, P.; Piekarska-Boniecka, H.

2017-07-01

The aim of the research was made the dedicated application AOTK (pol. Analiza Obrazu Trzeszczki Kopytowej) for image processing and analysis of horse navicular bone. The application was produced by using specialized software like Visual Studio 2013 and the .NET platform. To implement algorithms of image processing and analysis were used libraries of Aforge.NET. Implemented algorithms enabling accurate extraction of the characteristics of navicular bones and saving data to external files. Implemented in AOTK modules allowing the calculations of distance selected by user, preliminary assessment of conservation of structure of the examined objects. The application interface is designed in a way that ensures user the best possible view of the analyzed images.

Amplitude image processing by diffractive optics.

PubMed

Cagigal, Manuel P; Valle, Pedro J; Canales, V F

2016-02-22

In contrast to the standard digital image processing, which operates over the detected image intensity, we propose to perform amplitude image processing. Amplitude processing, like low pass or high pass filtering, is carried out using diffractive optics elements (DOE) since it allows to operate over the field complex amplitude before it has been detected. We show the procedure for designing the DOE that corresponds to each operation. Furthermore, we accomplish an analysis of amplitude image processing performances. In particular, a DOE Laplacian filter is applied to simulated astronomical images for detecting two stars one Airy ring apart. We also check by numerical simulations that the use of a Laplacian amplitude filter produces less noisy images than the standard digital image processing.

Observation of the Earth by radar

NASA Technical Reports Server (NTRS)

Elachi, C.

1982-01-01

Techniques and applications of radar observation from Earth satellites are discussed. Images processing and analysis of these images are discussed. Also discussed is radar imaging from aircraft. Uses of this data include ocean wave analysis, surface water evaluation, and topographic analysis.

Image Analysis and Modeling

DTIC Science & Technology

1976-03-01

This report summarizes the results of the research program on Image Analysis and Modeling supported by the Defense Advanced Research Projects Agency...The objective is to achieve a better understanding of image structure and to use this knowledge to develop improved image models for use in image ... analysis and processing tasks such as information extraction, image enhancement and restoration, and coding. The ultimate objective of this research is

Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.

PubMed

Cao, Lu; Graauw, Marjo de; Yan, Kuan; Winkel, Leah; Verbeek, Fons J

2016-05-03

Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system. In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated. The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal analysis of EGFR endocytosis. Moreover, it is shown that the addition of the wavelet-based texture features contributes to this improvement. Our workflow can be applied to drug discovery to analyze defected EGFR endocytosis processes.

Digital image analysis techniques for fiber and soil mixtures.

DOT National Transportation Integrated Search

1999-05-01

The objective of image processing is to visually enhance, quantify, and/or statistically evaluate some aspect of an image not readily apparent in its original form. Processed digital image data can be analyzed in numerous ways. In order to summarize ...

Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

NASA Astrophysics Data System (ADS)

Ogiela, M. R.; Bodzioch, S.

2011-06-01

This paper presents a new approach to gallbladder ultrasonic image processing and analysis towards automatic detection and interpretation of disease symptoms on processed US images. First, in this paper, there is presented a new heuristic method of filtering gallbladder contours from images. A major stage in this filtration is to segment and section off areas occupied by the said organ. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours, based on rank filtration, as well as on the analysis of line profile sections on tested organs. The second part concerns detecting the most important 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. The methodology of computer analysis of US gallbladder images presented here is clearly utilitarian in nature and after standardising can be used as a technique for supporting the diagnostics of selected gallbladder disorders using the images of this organ.

An architecture for a brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.

2000-01-01

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

Automatic cloud coverage assessment of Formosat-2 image

NASA Astrophysics Data System (ADS)

Hsu, Kuo-Hsien

2011-11-01

Formosat-2 satellite equips with the high-spatial-resolution (2m ground sampling distance) remote sensing instrument. It has been being operated on the daily-revisiting mission orbit by National Space organization (NSPO) of Taiwan since May 21 2004. NSPO has also serving as one of the ground receiving stations for daily processing the received Formosat- 2 images. The current cloud coverage assessment of Formosat-2 image for NSPO Image Processing System generally consists of two major steps. Firstly, an un-supervised K-means method is used for automatically estimating the cloud statistic of Formosat-2 image. Secondly, manual estimation of cloud coverage from Formosat-2 image is processed by manual examination. Apparently, a more accurate Automatic Cloud Coverage Assessment (ACCA) method certainly increases the efficiency of processing step 2 with a good prediction of cloud statistic. In this paper, mainly based on the research results from Chang et al, Irish, and Gotoh, we propose a modified Formosat-2 ACCA method which considered pre-processing and post-processing analysis. For pre-processing analysis, cloud statistic is determined by using un-supervised K-means classification, Sobel's method, Otsu's method, non-cloudy pixels reexamination, and cross-band filter method. Box-Counting fractal method is considered as a post-processing tool to double check the results of pre-processing analysis for increasing the efficiency of manual examination.

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.

[Quantitative data analysis for live imaging of bone.

PubMed

Seno, Shigeto

Bone tissue is a hard tissue, it was difficult to observe the interior of the bone tissue alive. With the progress of microscopic technology and fluorescent probe technology in recent years, it becomes possible to observe various activities of various cells forming bone society. On the other hand, the quantitative increase in data and the diversification and complexity of the images makes it difficult to perform quantitative analysis by visual inspection. It has been expected to develop a methodology for processing microscopic images and data analysis. In this article, we introduce the research field of bioimage informatics which is the boundary area of biology and information science, and then outline the basic image processing technology for quantitative analysis of live imaging data of bone.

Noise removal using factor analysis of dynamic structures: application to cardiac gated studies.

PubMed

Bruyant, P P; Sau, J; Mallet, J J

1999-10-01

Factor analysis of dynamic structures (FADS) facilitates the extraction of relevant data, usually with physiologic meaning, from a dynamic set of images. The result of this process is a set of factor images and curves plus some residual activity. The set of factor images and curves can be used to retrieve the original data with reduced noise using an inverse factor analysis process (iFADS). This improvement in image quality is expected because the inverse process does not use the residual activity, assumed to be made of noise. The goal of this work is to quantitate and assess the efficiency of this method on gated cardiac images. A computer simulation of a planar cardiac gated study was performed. The simulated images were added with noise and processed by the FADS-iFADS program. The signal-to-noise ratios (SNRs) were compared between original and processed data. Planar gated cardiac studies from 10 patients were tested. The data processed by FADS-iFADS were subtracted to the original data. The result of the substraction was studied to evaluate its noisy nature. The SNR is about five times greater after the FADS-iFADS process. The difference between original and processed data is noise only, i.e., processed data equals original data minus some white noise. The FADS-iFADS process is successful in the removal of an important part of the noise and therefore is a tool to improve the image quality of cardiac images. This tool does not decrease the spatial resolution (compared with smoothing filters) and does not lose details (compared with frequential filters). Once the number of factors is chosen, this method is not operator dependent.

Stable image acquisition for mobile image processing applications

NASA Astrophysics Data System (ADS)

Henning, Kai-Fabian; Fritze, Alexander; Gillich, Eugen; Mönks, Uwe; Lohweg, Volker

2015-02-01

Today, mobile devices (smartphones, tablets, etc.) are widespread and of high importance for their users. Their performance as well as versatility increases over time. This leads to the opportunity to use such devices for more specific tasks like image processing in an industrial context. For the analysis of images requirements like image quality (blur, illumination, etc.) as well as a defined relative position of the object to be inspected are crucial. Since mobile devices are handheld and used in constantly changing environments the challenge is to fulfill these requirements. We present an approach to overcome the obstacles and stabilize the image capturing process such that image analysis becomes significantly improved on mobile devices. Therefore, image processing methods are combined with sensor fusion concepts. The approach consists of three main parts. First, pose estimation methods are used to guide a user moving the device to a defined position. Second, the sensors data and the pose information are combined for relative motion estimation. Finally, the image capturing process is automated. It is triggered depending on the alignment of the device and the object as well as the image quality that can be achieved under consideration of motion and environmental effects.

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.

Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge Based Image Analysis

DTIC Science & Technology

1989-08-01

Automatic Line Network Extraction from Aerial Imangery of Urban Areas Sthrough KnowledghBased Image Analysis N 04 Final Technical ReportI December...Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge Based Image Analysis Accesion For NTIS CRA&I DTIC TAB 0...paittern re’ognlition. blac’kboardl oriented symbollic processing, knowledge based image analysis , image understanding, aer’ial imsagery, urban area, 17

Novel Diffusion-Weighted MRI for High-Grade Prostate Cancer Detection

DTIC Science & Technology

2016-10-01

in image resolution and scale.This process is critical for evaluating new imaging modalities.Our initial findings illustrate the potential of the...eligible for analysis as determined by adequate pathologic processing and MR images deemed to be of adequate quality by the study team.  The...histology samples have been requested from the UIC biorepository for digitization  All MR images have been collected and prepared for image processing

Visual Communications and Image Processing

NASA Astrophysics Data System (ADS)

Hsing, T. Russell

1987-07-01

This special issue of Optical Engineering is concerned with visual communications and image processing. The increase in communication of visual information over the past several decades has resulted in many new image processing and visual communication systems being put into service. The growth of this field has been rapid in both commercial and military applications. The objective of this special issue is to intermix advent technology in visual communications and image processing with ideas generated from industry, universities, and users through both invited and contributed papers. The 15 papers of this issue are organized into four different categories: image compression and transmission, image enhancement, image analysis and pattern recognition, and image processing in medical applications.

An image-processing method to detect sub-optical features based on understanding noise in intensity measurements.

PubMed

Bhatia, Tripta

2018-07-01

Accurate quantitative analysis of image data requires that we distinguish between fluorescence intensity (true signal) and the noise inherent to its measurements to the extent possible. We image multilamellar membrane tubes and beads that grow from defects in the fluid lamellar phase of the lipid 1,2-dioleoyl-sn-glycero-3-phosphocholine dissolved in water and water-glycerol mixtures by using fluorescence confocal polarizing microscope. We quantify image noise and determine the noise statistics. Understanding the nature of image noise also helps in optimizing image processing to detect sub-optical features, which would otherwise remain hidden. We use an image-processing technique "optimum smoothening" to improve the signal-to-noise ratio of features of interest without smearing their structural details. A high SNR renders desired positional accuracy with which it is possible to resolve features of interest with width below optical resolution. Using optimum smoothening, the smallest and the largest core diameter detected is of width [Formula: see text] and [Formula: see text] nm, respectively, discussed in this paper. The image-processing and analysis techniques and the noise modeling discussed in this paper can be used for detailed morphological analysis of features down to sub-optical length scales that are obtained by any kind of fluorescence intensity imaging in the raster mode.

Image data-processing system for solar astronomy

NASA Technical Reports Server (NTRS)

Wilson, R. M.; Teuber, D. L.; Watkins, J. R.; Thomas, D. T.; Cooper, C. M.

1977-01-01

The paper describes an image data processing system (IDAPS), its hardware/software configuration, and interactive and batch modes of operation for the analysis of the Skylab/Apollo Telescope Mount S056 X-Ray Telescope experiment data. Interactive IDAPS is primarily designed to provide on-line interactive user control of image processing operations for image familiarization, sequence and parameter optimization, and selective feature extraction and analysis. Batch IDAPS follows the normal conventions of card control and data input and output, and is best suited where the desired parameters and sequence of operations are known and when long image-processing times are required. Particular attention is given to the way in which this system has been used in solar astronomy and other investigations. Some recent results obtained by means of IDAPS are presented.

Information Acquisition, Analysis and Integration

DTIC Science & Technology

2016-08-03

of sensing and processing, theory, applications, signal processing, image and video processing, machine learning , technology transfer. 16. SECURITY... learning . 5. Solved elegantly old problems like image and video debluring, intro- ducing new revolutionary approaches. 1 DISTRIBUTION A: Distribution...Polatkan, G. Sapiro, D. Blei, D. B. Dunson, and L. Carin, “ Deep learning with hierarchical convolution factor analysis,” IEEE 6 DISTRIBUTION A

Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

PubMed

Milchenko, Mikhail; Snyder, Abraham Z; LaMontagne, Pamela; Shimony, Joshua S; Benzinger, Tammie L; Fouke, Sarah Jost; Marcus, Daniel S

2016-07-01

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

Flame analysis using image processing techniques

NASA Astrophysics Data System (ADS)

Her Jie, Albert Chang; Zamli, Ahmad Faizal Ahmad; Zulazlan Shah Zulkifli, Ahmad; Yee, Joanne Lim Mun; Lim, Mooktzeng

2018-04-01

This paper presents image processing techniques with the use of fuzzy logic and neural network approach to perform flame analysis. Flame diagnostic is important in the industry to extract relevant information from flame images. Experiment test is carried out in a model industrial burner with different flow rates. Flame features such as luminous and spectral parameters are extracted using image processing and Fast Fourier Transform (FFT). Flame images are acquired using FLIR infrared camera. Non-linearities such as thermal acoustic oscillations and background noise affect the stability of flame. Flame velocity is one of the important characteristics that determines stability of flame. In this paper, an image processing method is proposed to determine flame velocity. Power spectral density (PSD) graph is a good tool for vibration analysis where flame stability can be approximated. However, a more intelligent diagnostic system is needed to automatically determine flame stability. In this paper, flame features of different flow rates are compared and analyzed. The selected flame features are used as inputs to the proposed fuzzy inference system to determine flame stability. Neural network is used to test the performance of the fuzzy inference system.

Rotation covariant image processing for biomedical applications.

PubMed

Skibbe, Henrik; Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences.

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip.

PubMed

Heo, Young Jin; Lee, Donghyeon; Kang, Junsu; Lee, Keondo; Chung, Wan Kyun

2017-09-14

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

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

PubMed

Pizzonia, J

2001-06-01

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

Automatic Assessment and Reduction of Noise using Edge Pattern Analysis in Non-Linear Image Enhancement

NASA Technical Reports Server (NTRS)

Jobson, Daniel J.; Rahman, Zia-Ur; Woodell, Glenn A.; Hines, Glenn D.

2004-01-01

Noise is the primary visibility limit in the process of non-linear image enhancement, and is no longer a statistically stable additive noise in the post-enhancement image. Therefore novel approaches are needed to both assess and reduce spatially variable noise at this stage in overall image processing. Here we will examine the use of edge pattern analysis both for automatic assessment of spatially variable noise and as a foundation for new noise reduction methods.

Image analysis of multiple moving wood pieces in real time

NASA Astrophysics Data System (ADS)

Wang, Weixing

2006-02-01

This paper presents algorithms for image processing and image analysis of wood piece materials. The algorithms were designed for auto-detection of wood piece materials on a moving conveyor belt or a truck. When wood objects on moving, the hard task is to trace the contours of the objects in n optimal way. To make the algorithms work efficiently in the plant, a flexible online system was designed and developed, which mainly consists of image acquisition, image processing, object delineation and analysis. A number of newly-developed algorithms can delineate wood objects with high accuracy and high speed, and in the wood piece analysis part, each wood piece can be characterized by a number of visual parameters which can also be used for constructing experimental models directly in the system.

Quantitative image processing in fluid mechanics

NASA Technical Reports Server (NTRS)

Hesselink, Lambertus; Helman, James; Ning, Paul

1992-01-01

The current status of digital image processing in fluid flow research is reviewed. In particular, attention is given to a comprehensive approach to the extraction of quantitative data from multivariate databases and examples of recent developments. The discussion covers numerical simulations and experiments, data processing, generation and dissemination of knowledge, traditional image processing, hybrid processing, fluid flow vector field topology, and isosurface analysis using Marching Cubes.

The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data

NASA Technical Reports Server (NTRS)

Kruse, F. A.; Lefkoff, A. B.; Boardman, J. W.; Heidebrecht, K. B.; Shapiro, A. T.; Barloon, P. J.; Goetz, A. F. H.

1993-01-01

The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides 'point and click' operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).

Visually enhanced CCTV digital surveillance utilizing Intranet and Internet.

PubMed

Ozaki, Nobuyuki

2002-07-01

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

Video image processing to create a speed sensor

DOT National Transportation Integrated Search

1999-11-01

Image processing has been applied to traffic analysis in recent years, with different goals. In the report, a new approach is presented for extracting vehicular speed information, given a sequence of real-time traffic images. We extract moving edges ...

Spreadsheet-Like Image Analysis

DTIC Science & Technology

1992-08-01

1 " DTIC AD-A254 395 S LECTE D, ° AD-E402 350 Technical Report ARPAD-TR-92002 SPREADSHEET-LIKE IMAGE ANALYSIS Paul Willson August 1992 U.S. ARMY...August 1992 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS SPREADSHEET-LIKE IMAGE ANALYSIS 6. AUTHOR(S) Paul Willson 7. PERFORMING ORGANIZATION NAME(S) AND...14. SUBJECT TERMS 15. NUMBER OF PAGES Image analysis , nondestructive inspection, spreadsheet, Macintosh software, 14 neural network, signal processing

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

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.

Image Processing for Bioluminescence Resonance Energy Transfer Measurement-BRET-Analyzer.

PubMed

Chastagnier, Yan; Moutin, Enora; Hemonnot, Anne-Laure; Perroy, Julie

2017-01-01

A growing number of tools now allow live recordings of various signaling pathways and protein-protein interaction dynamics in time and space by ratiometric measurements, such as Bioluminescence Resonance Energy Transfer (BRET) Imaging. Accurate and reproducible analysis of ratiometric measurements has thus become mandatory to interpret quantitative imaging. In order to fulfill this necessity, we have developed an open source toolset for Fiji- BRET-Analyzer -allowing a systematic analysis, from image processing to ratio quantification. We share this open source solution and a step-by-step tutorial at https://github.com/ychastagnier/BRET-Analyzer. This toolset proposes (1) image background subtraction, (2) image alignment over time, (3) a composite thresholding method of the image used as the denominator of the ratio to refine the precise limits of the sample, (4) pixel by pixel division of the images and efficient distribution of the ratio intensity on a pseudocolor scale, and (5) quantification of the ratio mean intensity and standard variation among pixels in chosen areas. In addition to systematize the analysis process, we show that the BRET-Analyzer allows proper reconstitution and quantification of the ratiometric image in time and space, even from heterogeneous subcellular volumes. Indeed, analyzing twice the same images, we demonstrate that compared to standard analysis BRET-Analyzer precisely define the luminescent specimen limits, enlightening proficient strengths from small and big ensembles over time. For example, we followed and quantified, in live, scaffold proteins interaction dynamics in neuronal sub-cellular compartments including dendritic spines, for half an hour. In conclusion, BRET-Analyzer provides a complete, versatile and efficient toolset for automated reproducible and meaningful image ratio analysis.

Adjacent slice prostate cancer prediction to inform MALDI imaging biomarker analysis

NASA Astrophysics Data System (ADS)

Chuang, Shao-Hui; Sun, Xiaoyan; Cazares, Lisa; Nyalwidhe, Julius; Troyer, Dean; Semmes, O. John; Li, Jiang; McKenzie, Frederic D.

2010-03-01

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice.

Multi-Dimensional Signal Processing Research Program

DTIC Science & Technology

1981-09-30

applications to real-time image processing and analysis. A specific long-range application is the automated processing of aerial reconnaissance imagery...Non-supervised image segmentation is a potentially im- portant operation in the automated processing of aerial reconnaissance pho- tographs since it

An improved K-means clustering algorithm in agricultural image segmentation

NASA Astrophysics Data System (ADS)

Cheng, Huifeng; Peng, Hui; Liu, Shanmei

Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.

The ImageJ ecosystem: an open platform for biomedical image analysis

PubMed Central

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 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 life science, how it inspires other projects, and how it is self-influenced by coevolving projects within the ImageJ ecosystem. PMID:26153368

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. © 2015 Wiley Periodicals, Inc.

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

NASA Technical Reports Server (NTRS)

Libert, J. M.

1982-01-01

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

Mirion--a software package for automatic processing of mass spectrometric images.

PubMed

Paschke, C; Leisner, A; Hester, A; Maass, K; Guenther, S; Bouschen, W; Spengler, B

2013-08-01

Mass spectrometric imaging (MSI) techniques are of growing interest for the Life Sciences. In recent years, the development of new instruments employing ion sources that are tailored for spatial scanning allowed the acquisition of large data sets. A subsequent data processing, however, is still a bottleneck in the analytical process, as a manual data interpretation is impossible within a reasonable time frame. The transformation of mass spectrometric data into spatial distribution images of detected compounds turned out to be the most appropriate method to visualize the results of such scans, as humans are able to interpret images faster and easier than plain numbers. Image generation, thus, is a time-consuming and complex yet very efficient task. The free software package "Mirion," presented in this paper, allows the handling and analysis of data sets acquired by mass spectrometry imaging. Mirion can be used for image processing of MSI data obtained from many different sources, as it uses the HUPO-PSI-based standard data format imzML, which is implemented in the proprietary software of most of the mass spectrometer companies. Different graphical representations of the recorded data are available. Furthermore, automatic calculation and overlay of mass spectrometric images promotes direct comparison of different analytes for data evaluation. The program also includes tools for image processing and image analysis.

Digital Radiographic Image Processing and Analysis.

PubMed

Yoon, Douglas C; Mol, André; Benn, Douglas K; Benavides, Erika

2018-07-01

This article describes digital radiographic imaging and analysis from the basics of image capture to examples of some of the most advanced digital technologies currently available. The principles underlying the imaging technologies are described to provide a better understanding of their strengths and limitations. Copyright © 2018 Elsevier Inc. All rights reserved.

My Body Looks Like That Girl’s: Body Mass Index Modulates Brain Activity during Body Image Self-Reflection among Young Women

PubMed Central

Wen, Xin; She, Ying; Vinke, Petra Corianne; Chen, Hong

2016-01-01

Body image distress or body dissatisfaction is one of the most common consequences of obesity and overweight. We investigated the neural bases of body image processing in overweight and average weight young women to understand whether brain regions that were previously found to be involved in processing self-reflective, perspective and affective components of body image would show different activation between two groups. Thirteen overweight (O-W group, age = 20.31±1.70 years) and thirteen average weight (A-W group, age = 20.15±1.62 years) young women underwent functional magnetic resonance imaging while performing a body image self-reflection task. Among both groups, whole-brain analysis revealed activations of a brain network related to perceptive and affective components of body image processing. ROI analysis showed a main effect of group in ACC as well as a group by condition interaction within bilateral EBA, bilateral FBA, right IPL, bilateral DLPFC, left amygdala and left MPFC. For the A-W group, simple effect analysis revealed stronger activations in Thin-Control compared to Fat-Control condition within regions related to perceptive (including bilateral EBA, bilateral FBA, right IPL) and affective components of body image processing (including bilateral DLPFC, left amygdala), as well as self-reference (left MPFC). The O-W group only showed stronger activations in Fat-Control than in Thin-Control condition within regions related to the perceptive component of body image processing (including left EBA and left FBA). Path analysis showed that in the Fat-Thin contrast, body dissatisfaction completely mediated the group difference in brain response in left amygdala across the whole sample. Our data are the first to demonstrate differences in brain response to body pictures between average weight and overweight young females involved in a body image self-reflection task. These results provide insights for understanding the vulnerability to body image distress among overweight or obese young females. PMID:27764116

My Body Looks Like That Girl's: Body Mass Index Modulates Brain Activity during Body Image Self-Reflection among Young Women.

PubMed

Gao, Xiao; Deng, Xiao; Wen, Xin; She, Ying; Vinke, Petra Corianne; Chen, Hong

2016-01-01

Body image distress or body dissatisfaction is one of the most common consequences of obesity and overweight. We investigated the neural bases of body image processing in overweight and average weight young women to understand whether brain regions that were previously found to be involved in processing self-reflective, perspective and affective components of body image would show different activation between two groups. Thirteen overweight (O-W group, age = 20.31±1.70 years) and thirteen average weight (A-W group, age = 20.15±1.62 years) young women underwent functional magnetic resonance imaging while performing a body image self-reflection task. Among both groups, whole-brain analysis revealed activations of a brain network related to perceptive and affective components of body image processing. ROI analysis showed a main effect of group in ACC as well as a group by condition interaction within bilateral EBA, bilateral FBA, right IPL, bilateral DLPFC, left amygdala and left MPFC. For the A-W group, simple effect analysis revealed stronger activations in Thin-Control compared to Fat-Control condition within regions related to perceptive (including bilateral EBA, bilateral FBA, right IPL) and affective components of body image processing (including bilateral DLPFC, left amygdala), as well as self-reference (left MPFC). The O-W group only showed stronger activations in Fat-Control than in Thin-Control condition within regions related to the perceptive component of body image processing (including left EBA and left FBA). Path analysis showed that in the Fat-Thin contrast, body dissatisfaction completely mediated the group difference in brain response in left amygdala across the whole sample. Our data are the first to demonstrate differences in brain response to body pictures between average weight and overweight young females involved in a body image self-reflection task. These results provide insights for understanding the vulnerability to body image distress among overweight or obese young females.

Image-Processing Program

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

Quantitative analysis of phosphoinositide 3-kinase (PI3K) signaling using live-cell total internal reflection fluorescence (TIRF) microscopy.

PubMed

Johnson, Heath E; Haugh, Jason M

2013-12-02

This unit focuses on the use of total internal reflection fluorescence (TIRF) microscopy and image analysis methods to study the dynamics of signal transduction mediated by class I phosphoinositide 3-kinases (PI3Ks) in mammalian cells. The first four protocols cover live-cell imaging experiments, image acquisition parameters, and basic image processing and segmentation. These methods are generally applicable to live-cell TIRF experiments. The remaining protocols outline more advanced image analysis methods, which were developed in our laboratory for the purpose of characterizing the spatiotemporal dynamics of PI3K signaling. These methods may be extended to analyze other cellular processes monitored using fluorescent biosensors. Copyright © 2013 John Wiley & Sons, Inc.

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.

Digital Imaging

NASA Technical Reports Server (NTRS)

1986-01-01

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

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

PubMed

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

2007-02-01

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

FRAP Analysis: Accounting for Bleaching during Image Capture

PubMed Central

Wu, Jun; Shekhar, Nandini; Lele, Pushkar P.; Lele, Tanmay P.

2012-01-01

The analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments involves mathematical modeling of the fluorescence recovery process. An important feature of FRAP experiments that tends to be ignored in the modeling is that there can be a significant loss of fluorescence due to bleaching during image capture. In this paper, we explicitly include the effects of bleaching during image capture in the model for the recovery process, instead of correcting for the effects of bleaching using reference measurements. Using experimental examples, we demonstrate the usefulness of such an approach in FRAP analysis. PMID:22912750

Knowledge-based low-level image analysis for computer vision systems

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.; Baxi, Himanshu; Ranganath, M. V.

1988-01-01

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Real-time hyperspectral imaging for food safety applications

USDA-ARS?s Scientific Manuscript database

Multispectral imaging systems with selected bands can commonly be used for real-time applications of food processing. Recent research has demonstrated several image processing methods including binning, noise removal filter, and appropriate morphological analysis in real-time mode can remove most fa...

Processing Cones: A Computational Structure for Image Analysis.

DTIC Science & Technology

1981-12-01

image analysis applications, referred to as a processing cone, is described and sample algorithms are presented. A fundamental characteristic of the structure is its hierarchical organization into two-dimensional arrays of decreasing resolution. In this architecture, a protypical function is defined on a local window of data and applied uniformly to all windows in a parallel manner. Three basic modes of processing are supported in the cone: reduction operations (upward processing), horizontal operations (processing at a single level) and projection operations (downward

Computer-assisted image processing to detect spores from the fungus Pandora neoaphidis.

PubMed

Korsnes, Reinert; Westrum, Karin; Fløistad, Erling; Klingen, Ingeborg

2016-01-01

This contribution demonstrates an example of experimental automatic image analysis to detect spores prepared on microscope slides derived from trapping. The application is to monitor aerial spore counts of the entomopathogenic fungus Pandora neoaphidis which may serve as a biological control agent for aphids. Automatic detection of such spores can therefore play a role in plant protection. The present approach for such detection is a modification of traditional manual microscopy of prepared slides, where autonomous image recording precedes computerised image analysis. The purpose of the present image analysis is to support human visual inspection of imagery data - not to replace it. The workflow has three components:•Preparation of slides for microscopy.•Image recording.•Computerised image processing where the initial part is, as usual, segmentation depending on the actual data product. Then comes identification of blobs, calculation of principal axes of blobs, symmetry operations and projection on a three parameter egg shape space.

Rotation Covariant Image Processing for Biomedical Applications

PubMed Central

Reisert, Marco

2013-01-01

With the advent of novel biomedical 3D image acquisition techniques, the efficient and reliable analysis of volumetric images has become more and more important. The amount of data is enormous and demands an automated processing. The applications are manifold, ranging from image enhancement, image reconstruction, and image description to object/feature detection and high-level contextual feature extraction. In most scenarios, it is expected that geometric transformations alter the output in a mathematically well-defined manner. In this paper we emphasis on 3D translations and rotations. Many algorithms rely on intensity or low-order tensorial-like descriptions to fulfill this demand. This paper proposes a general mathematical framework based on mathematical concepts and theories transferred from mathematical physics and harmonic analysis into the domain of image analysis and pattern recognition. Based on two basic operations, spherical tensor differentiation and spherical tensor multiplication, we show how to design a variety of 3D image processing methods in an efficient way. The framework has already been applied to several biomedical applications ranging from feature and object detection tasks to image enhancement and image restoration techniques. In this paper, the proposed methods are applied on a variety of different 3D data modalities stemming from medical and biological sciences. PMID:23710255

Analysis of the Growth Process of Neural Cells in Culture Environment Using Image Processing Techniques

NASA Astrophysics Data System (ADS)

Mirsafianf, Atefeh S.; Isfahani, Shirin N.; Kasaei, Shohreh; Mobasheri, Hamid

Here we present an approach for processing neural cells images to analyze their growth process in culture environment. We have applied several image processing techniques for: 1- Environmental noise reduction, 2- Neural cells segmentation, 3- Neural cells classification based on their dendrites' growth conditions, and 4- neurons' features Extraction and measurement (e.g., like cell body area, number of dendrites, axon's length, and so on). Due to the large amount of noise in the images, we have used feed forward artificial neural networks to detect edges more precisely.

Display system for imaging scientific telemetric information

NASA Technical Reports Server (NTRS)

Zabiyakin, G. I.; Rykovanov, S. N.

1979-01-01

A system for imaging scientific telemetric information, based on the M-6000 minicomputer and the SIGD graphic display, is described. Two dimensional graphic display of telemetric information and interaction with the computer, in analysis and processing of telemetric parameters displayed on the screen is provided. The running parameter information output method is presented. User capabilities in the analysis and processing of telemetric information imaged on the display screen and the user language are discussed and illustrated.

Data Visualization and Animation Lab (DVAL) overview

NASA Technical Reports Server (NTRS)

Stacy, Kathy; Vonofenheim, Bill

1994-01-01

The general capabilities of the Langley Research Center Data Visualization and Animation Laboratory is described. These capabilities include digital image processing, 3-D interactive computer graphics, data visualization and analysis, video-rate acquisition and processing of video images, photo-realistic modeling and animation, video report generation, and color hardcopies. A specialized video image processing system is also discussed.

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

PubMed Central

Dixit, Sudeepa; Fox, Mark; Pal, Anupam

2014-01-01

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

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.

Parallel Wavefront Analysis for a 4D Interferometer

NASA Technical Reports Server (NTRS)

Rao, Shanti R.

2011-01-01

This software provides a programming interface for automating data collection with a PhaseCam interferometer from 4D Technology, and distributing the image-processing algorithm across a cluster of general-purpose computers. Multiple instances of 4Sight (4D Technology s proprietary software) run on a networked cluster of computers. Each connects to a single server (the controller) and waits for instructions. The controller directs the interferometer to several images, then assigns each image to a different computer for processing. When the image processing is finished, the server directs one of the computers to collate and combine the processed images, saving the resulting measurement in a file on a disk. The available software captures approximately 100 images and analyzes them immediately. This software separates the capture and analysis processes, so that analysis can be done at a different time and faster by running the algorithm in parallel across several processors. The PhaseCam family of interferometers can measure an optical system in milliseconds, but it takes many seconds to process the data so that it is usable. In characterizing an adaptive optics system, like the next generation of astronomical observatories, thousands of measurements are required, and the processing time quickly becomes excessive. A programming interface distributes data processing for a PhaseCam interferometer across a Windows computing cluster. A scriptable controller program coordinates data acquisition from the interferometer, storage on networked hard disks, and parallel processing. Idle time of the interferometer is minimized. This architecture is implemented in Python and JavaScript, and may be altered to fit a customer s needs.

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. © 2014 John Wiley & Sons Ltd.

Automated processing of zebrafish imaging data: a survey.

PubMed

Mikut, Ralf; Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A; Kausler, Bernhard X; Ledesma-Carbayo, María J; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-09-01

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Automated Processing of Zebrafish Imaging Data: A Survey

PubMed Central

Dickmeis, Thomas; Driever, Wolfgang; Geurts, Pierre; Hamprecht, Fred A.; Kausler, Bernhard X.; Ledesma-Carbayo, María J.; Marée, Raphaël; Mikula, Karol; Pantazis, Periklis; Ronneberger, Olaf; Santos, Andres; Stotzka, Rainer; Strähle, Uwe; Peyriéras, Nadine

2013-01-01

Abstract Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines. PMID:23758125

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.

Prediction of processing tomato peeling outcomes

USDA-ARS?s Scientific Manuscript database

Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After ...

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 grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods. PMID:27551746

Reducing noise component on medical images

NASA Astrophysics Data System (ADS)

Semenishchev, Evgeny; Voronin, Viacheslav; Dub, Vladimir; Balabaeva, Oksana

2018-04-01

Medical visualization and analysis of medical data is an actual direction. Medical images are used in microbiology, genetics, roentgenology, oncology, surgery, ophthalmology, etc. Initial data processing is a major step towards obtaining a good diagnostic result. The paper considers the approach allows an image filtering with preservation of objects borders. The algorithm proposed in this paper is based on sequential data processing. At the first stage, local areas are determined, for this purpose the method of threshold processing, as well as the classical ICI algorithm, is applied. The second stage uses a method based on based on two criteria, namely, L2 norm and the first order square difference. To preserve the boundaries of objects, we will process the transition boundary and local neighborhood the filtering algorithm with a fixed-coefficient. For example, reconstructed images of CT, x-ray, and microbiological studies are shown. The test images show the effectiveness of the proposed algorithm. This shows the applicability of analysis many medical imaging applications.

Optical coherence tomography imaging based on non-harmonic analysis

NASA Astrophysics Data System (ADS)

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

2009-11-01

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

Some uses of wavelets for imaging dynamic processes in live cochlear structures

NASA Astrophysics Data System (ADS)

Boutet de Monvel, J.

2007-09-01

A variety of image and signal processing algorithms based on wavelet filtering tools have been developed during the last few decades, that are well adapted to the experimental variability typically encountered in live biological microscopy. A number of processing tools are reviewed, that use wavelets for adaptive image restoration and for motion or brightness variation analysis by optical flow computation. The usefulness of these tools for biological imaging is illustrated in the context of the restoration of images of the inner ear and the analysis of cochlear motion patterns in two and three dimensions. I also report on recent work that aims at capturing fluorescence intensity changes associated with vesicle dynamics at synaptic zones of sensory hair cells. This latest application requires one to separate the intensity variations associated with the physiological process under study from the variations caused by motion of the observed structures. A wavelet optical flow algorithm for doing this is presented, and its effectiveness is demonstrated on artificial and experimental image sequences.

Rapid Disaster Damage Estimation

NASA Astrophysics Data System (ADS)

Vu, T. T.

2012-07-01

The experiences from recent disaster events showed that detailed information derived from high-resolution satellite images could accommodate the requirements from damage analysts and disaster management practitioners. Richer information contained in such high-resolution images, however, increases the complexity of image analysis. As a result, few image analysis solutions can be practically used under time pressure in the context of post-disaster and emergency responses. To fill the gap in employment of remote sensing in disaster response, this research develops a rapid high-resolution satellite mapping solution built upon a dual-scale contextual framework to support damage estimation after a catastrophe. The target objects are building (or building blocks) and their condition. On the coarse processing level, statistical region merging deployed to group pixels into a number of coarse clusters. Based on majority rule of vegetation index, water and shadow index, it is possible to eliminate the irrelevant clusters. The remaining clusters likely consist of building structures and others. On the fine processing level details, within each considering clusters, smaller objects are formed using morphological analysis. Numerous indicators including spectral, textural and shape indices are computed to be used in a rule-based object classification. Computation time of raster-based analysis highly depends on the image size or number of processed pixels in order words. Breaking into 2 level processing helps to reduce the processed number of pixels and the redundancy of processing irrelevant information. In addition, it allows a data- and tasks- based parallel implementation. The performance is demonstrated with QuickBird images captured a disaster-affected area of Phanga, Thailand by the 2004 Indian Ocean tsunami are used for demonstration of the performance. The developed solution will be implemented in different platforms as well as a web processing service for operational uses.

CSAM Metrology Software Tool

NASA Technical Reports Server (NTRS)

Vu, Duc; Sandor, Michael; Agarwal, Shri

2005-01-01

CSAM Metrology Software Tool (CMeST) is a computer program for analysis of false-color CSAM images of plastic-encapsulated microcircuits. (CSAM signifies C-mode scanning acoustic microscopy.) The colors in the images indicate areas of delamination within the plastic packages. Heretofore, the images have been interpreted by human examiners. Hence, interpretations have not been entirely consistent and objective. CMeST processes the color information in image-data files to detect areas of delamination without incurring inconsistencies of subjective judgement. CMeST can be used to create a database of baseline images of packages acquired at given times for comparison with images of the same packages acquired at later times. Any area within an image can be selected for analysis, which can include examination of different delamination types by location. CMeST can also be used to perform statistical analyses of image data. Results of analyses are available in a spreadsheet format for further processing. The results can be exported to any data-base-processing software.

Internet (WWW) based system of ultrasonic image processing tools for remote image analysis.

PubMed

Zeng, Hong; Fei, Ding-Yu; Fu, Cai-Ting; Kraft, Kenneth A

2003-07-01

Ultrasonic Doppler color imaging can provide anatomic information and simultaneously render flow information within blood vessels for diagnostic purpose. Many researchers are currently developing ultrasound image processing algorithms in order to provide physicians with accurate clinical parameters from the images. Because researchers use a variety of computer languages and work on different computer platforms to implement their algorithms, it is difficult for other researchers and physicians to access those programs. A system has been developed using World Wide Web (WWW) technologies and HTTP communication protocols to publish our ultrasonic Angle Independent Doppler Color Image (AIDCI) processing algorithm and several general measurement tools on the Internet, where authorized researchers and physicians can easily access the program using web browsers to carry out remote analysis of their local ultrasonic images or images provided from the database. In order to overcome potential incompatibility between programs and users' computer platforms, ActiveX technology was used in this project. The technique developed may also be used for other research fields.

In-line monitoring of pellet coating thickness growth by means of visual imaging.

PubMed

Oman Kadunc, Nika; Sibanc, Rok; Dreu, Rok; Likar, Boštjan; Tomaževič, Dejan

2014-08-15

Coating thickness is the most important attribute of coated pharmaceutical pellets as it directly affects release profiles and stability of the drug. Quality control of the coating process of pharmaceutical pellets is thus of utmost importance for assuring the desired end product characteristics. A visual imaging technique is presented and examined as a process analytic technology (PAT) tool for noninvasive continuous in-line and real time monitoring of coating thickness of pharmaceutical pellets during the coating process. Images of pellets were acquired during the coating process through an observation window of a Wurster coating apparatus. Image analysis methods were developed for fast and accurate determination of pellets' coating thickness during a coating process. The accuracy of the results for pellet coating thickness growth obtained in real time was evaluated through comparison with an off-line reference method and a good agreement was found. Information about the inter-pellet coating uniformity was gained from further statistical analysis of the measured pellet size distributions. Accuracy and performance analysis of the proposed method showed that visual imaging is feasible as a PAT tool for in-line and real time monitoring of the coating process of pharmaceutical pellets. Copyright © 2014 Elsevier B.V. All rights reserved.

Quantitative evaluation method of the bubble structure of sponge cake by using morphology image processing

NASA Astrophysics Data System (ADS)

Tatebe, Hironobu; Kato, Kunihito; Yamamoto, Kazuhiko; Katsuta, Yukio; Nonaka, Masahiko

2005-12-01

Now a day, many evaluation methods for the food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that are using for the quality evaluation. An advantage of the image processing is to be able to evaluate objectively. The goal of our research is structure evaluation of sponge cake by using image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner. Because the depth of field of this type scanner is very shallow, the bubble region of the surface has low gray scale values, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. First, input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

Instant Grainification: Real-Time Grain-Size Analysis from Digital Images in the Field

NASA Astrophysics Data System (ADS)

Rubin, D. M.; Chezar, H.

2007-12-01

Over the past few years, digital cameras and underwater microscopes have been developed to collect in-situ images of sand-sized bed sediment, and software has been developed to measure grain size from those digital images (Chezar and Rubin, 2004; Rubin, 2004; Rubin et al., 2006). Until now, all image processing and grain- size analysis was done back in the office where images were uploaded from cameras and processed on desktop computers. Computer hardware has become small and rugged enough to process images in the field, which for the first time allows real-time grain-size analysis of sand-sized bed sediment. We present such a system consisting of weatherproof tablet computer, open source image-processing software (autocorrelation code of Rubin, 2004, running under Octave and Cygwin), and digital camera with macro lens. Chezar, H., and Rubin, D., 2004, Underwater microscope system: U.S. Patent and Trademark Office, patent number 6,680,795, January 20, 2004. Rubin, D.M., 2004, A simple autocorrelation algorithm for determining grain size from digital images of sediment: Journal of Sedimentary Research, v. 74, p. 160-165. Rubin, D.M., Chezar, H., Harney, J.N., Topping, D.J., Melis, T.S., and Sherwood, C.R., 2006, Underwater microscope for measuring spatial and temporal changes in bed-sediment grain size: USGS Open-File Report 2006-1360.

3D Texture Features Mining for MRI Brain Tumor Identification

NASA Astrophysics Data System (ADS)

Rahim, Mohd Shafry Mohd; Saba, Tanzila; Nayer, Fatima; Syed, Afraz Zahra

2014-03-01

Medical image segmentation is a process to extract region of interest and to divide an image into its individual meaningful, homogeneous components. Actually, these components will have a strong relationship with the objects of interest in an image. For computer-aided diagnosis and therapy process, medical image segmentation is an initial mandatory step. Medical image segmentation is a sophisticated and challenging task because of the sophisticated nature of the medical images. Indeed, successful medical image analysis heavily dependent on the segmentation accuracy. Texture is one of the major features to identify region of interests in an image or to classify an object. 2D textures features yields poor classification results. Hence, this paper represents 3D features extraction using texture analysis and SVM as segmentation technique in the testing methodologies.

High resolution image processing on low-cost microcomputers

NASA Technical Reports Server (NTRS)

Miller, R. L.

1993-01-01

Recent advances in microcomputer technology have resulted in systems that rival the speed, storage, and display capabilities of traditionally larger machines. Low-cost microcomputers can provide a powerful environment for image processing. A new software program which offers sophisticated image display and analysis on IBM-based systems is presented. Designed specifically for a microcomputer, this program provides a wide-range of functions normally found only on dedicated graphics systems, and therefore can provide most students, universities and research groups with an affordable computer platform for processing digital images. The processing of AVHRR images within this environment is presented as an example.

Efficient processing of fluorescence images using directional multiscale representations.

PubMed

Labate, D; Laezza, F; Negi, P; Ozcan, B; Papadakis, M

2014-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data.

Efficient processing of fluorescence images using directional multiscale representations

PubMed Central

Labate, D.; Laezza, F.; Negi, P.; Ozcan, B.; Papadakis, M.

2017-01-01

Recent advances in high-resolution fluorescence microscopy have enabled the systematic study of morphological changes in large populations of cells induced by chemical and genetic perturbations, facilitating the discovery of signaling pathways underlying diseases and the development of new pharmacological treatments. In these studies, though, due to the complexity of the data, quantification and analysis of morphological features are for the vast majority handled manually, slowing significantly data processing and limiting often the information gained to a descriptive level. Thus, there is an urgent need for developing highly efficient automated analysis and processing tools for fluorescent images. In this paper, we present the application of a method based on the shearlet representation for confocal image analysis of neurons. The shearlet representation is a newly emerged method designed to combine multiscale data analysis with superior directional sensitivity, making this approach particularly effective for the representation of objects defined over a wide range of scales and with highly anisotropic features. Here, we apply the shearlet representation to problems of soma detection of neurons in culture and extraction of geometrical features of neuronal processes in brain tissue, and propose it as a new framework for large-scale fluorescent image analysis of biomedical data. PMID:28804225

Processing and analysis of cardiac optical mapping data obtained with potentiometric dyes

PubMed Central

Laughner, Jacob I.; Ng, Fu Siong; Sulkin, Matthew S.; Arthur, R. Martin

2012-01-01

Optical mapping has become an increasingly important tool to study cardiac electrophysiology in the past 20 years. Multiple methods are used to process and analyze cardiac optical mapping data, and no consensus currently exists regarding the optimum methods. The specific methods chosen to process optical mapping data are important because inappropriate data processing can affect the content of the data and thus alter the conclusions of the studies. Details of the different steps in processing optical imaging data, including image segmentation, spatial filtering, temporal filtering, and baseline drift removal, are provided in this review. We also provide descriptions of the common analyses performed on data obtained from cardiac optical imaging, including activation mapping, action potential duration mapping, repolarization mapping, conduction velocity measurements, and optical action potential upstroke analysis. Optical mapping is often used to study complex arrhythmias, and we also discuss dominant frequency analysis and phase mapping techniques used for the analysis of cardiac fibrillation. PMID:22821993

Theory on data processing and instrumentation. [remote sensing

NASA Technical Reports Server (NTRS)

Billingsley, F. C.

1978-01-01

A selection of NASA Earth observations programs are reviewed, emphasizing hardware capabilities. Sampling theory, noise and detection considerations, and image evaluation are discussed for remote sensor imagery. Vision and perception are considered, leading to numerical image processing. The use of multispectral scanners and of multispectral data processing systems, including digital image processing, is depicted. Multispectral sensing and analysis in application with land use and geographical data systems are also covered.

Evaluation of browning ratio in an image analysis of apple slices at different stages of instant controlled pressure drop-assisted hot-air drying (AD-DIC).

PubMed

Gao, Kun; Zhou, Linyan; Bi, Jinfeng; Yi, Jianyong; Wu, Xinye; Zhou, Mo; Wang, Xueyuan; Liu, Xuan

2017-06-01

Computer vision-based image analysis systems are widely used in food processing to evaluate quality changes. They are able to objectively measure the surface colour of various products since, providing some obvious advantages with their objectivity and quantitative capabilities. In this study, a computer vision-based image analysis system was used to investigate the colour changes of apple slices dried by instant controlled pressure drop-assisted hot air drying (AD-DIC). The CIE L* value and polyphenol oxidase activity in apple slices decreased during the entire drying process, whereas other colour indexes, including CIE a*, b*, ΔE and C* values, increased. The browning ratio calculated by image analysis increased during the drying process, and a sharp increment was observed for the DIC process. The change in 5-hydroxymethylfurfural (5-HMF) and fluorescent compounds (FIC) showed the same trend with browning ratio due to Maillard reaction. Moreover, the concentrations of 5-HMF and FIC both had a good quadratic correlation (R 2  > 0.998) with the browning ratio. Browning ratio was a reliable indicator of 5-HMF and FIC changes in apple slices during drying. The image analysis system could be used to monitor colour changes, 5-HMF and FIC in dehydrated apple slices during the AD-DIC process. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order.

PubMed

Aron, Miles; Browning, Richard; Carugo, Dario; Sezgin, Erdinc; Bernardino de la Serna, Jorge; Eggeling, Christian; Stride, Eleanor

2017-05-12

Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and model membrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by the generalized polarization (GP) parameter. With the development of commercial microscopes equipped with spectral detectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membrane properties. The existing tools for spectral image processing, however, are insufficient for processing the large data sets afforded by this technological advancement, and are unsuitable for processing images acquired with rapidly internalized fluorescent probes. Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to common operations, such as the calculation of distributions of GP values, generation of pseudo-colored GP maps, and spectral analysis, a key highlight of this tool is reliable membrane segmentation for probes that are rapidly internalized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generated by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is determined, which can provide insight into the mechanisms underlying changes in membrane properties and is desirable for e.g. studies involving model membranes and surfactant coated particles. Analysis is demonstrated for cell membranes, cell-derived vesicles, model membranes, and microbubbles with environmentally-sensitive probes Laurdan, carboxyl-modified Laurdan (C-Laurdan), Di-4-ANEPPDHQ, and Di-4-AN(F)EPPTEA (FE), for quantification of the local lateral density of lipids or lipid packing. The Spectral Imaging Toolbox is a powerful tool for the segmentation and processing of large spectral imaging datasets with a reliable method for membrane segmentation and no ability in programming required. The Spectral Imaging Toolbox can be downloaded from https://uk.mathworks.com/matlabcentral/fileexchange/62617-spectral-imaging-toolbox .

Comparison of pre-processing techniques for fluorescence microscopy images of cells labeled for actin.

PubMed

Muralidhar, Gautam S; Channappayya, Sumohana S; Slater, John H; Blinka, Ellen M; Bovik, Alan C; Frey, Wolfgang; Markey, Mia K

2008-11-06

Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.

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.

Directional ratio based on parabolic molecules and its application to the analysis of tubular structures

NASA Astrophysics Data System (ADS)

Labate, Demetrio; Negi, Pooran; Ozcan, Burcin; Papadakis, Manos

2015-09-01

As advances in imaging technologies make more and more data available for biomedical applications, there is an increasing need to develop efficient quantitative algorithms for the analysis and processing of imaging data. In this paper, we introduce an innovative multiscale approach called Directional Ratio which is especially effective to distingush isotropic from anisotropic structures. This task is especially useful in the analysis of images of neurons, the main units of the nervous systems which consist of a main cell body called the soma and many elongated processes called neurites. We analyze the theoretical properties of our method on idealized models of neurons and develop a numerical implementation of this approach for analysis of fluorescent images of cultured neurons. We show that this algorithm is very effective for the detection of somas and the extraction of neurites in images of small circuits of neurons.

Redundancy Analysis of Capacitance Data of a Coplanar Electrode Array for Fast and Stable Imaging Processing

PubMed Central

Wen, Yintang; Zhang, Zhenda; Zhang, Yuyan; Sun, Dongtao

2017-01-01

A coplanar electrode array sensor is established for the imaging of composite-material adhesive-layer defect detection. The sensor is based on the capacitive edge effect, which leads to capacitance data being considerably weak and susceptible to environmental noise. The inverse problem of coplanar array electrical capacitance tomography (C-ECT) is ill-conditioning, in which a small error of capacitance data can seriously affect the quality of reconstructed images. In order to achieve a stable image reconstruction process, a redundancy analysis method for capacitance data is proposed. The proposed method is based on contribution rate and anti-interference capability. According to the redundancy analysis, the capacitance data are divided into valid and invalid data. When the image is reconstructed by valid data, the sensitivity matrix needs to be changed accordingly. In order to evaluate the effectiveness of the sensitivity map, singular value decomposition (SVD) is used. Finally, the two-dimensional (2D) and three-dimensional (3D) images are reconstructed by the Tikhonov regularization method. Through comparison of the reconstructed images of raw capacitance data, the stability of the image reconstruction process can be improved, and the quality of reconstructed images is not degraded. As a result, much invalid data are not collected, and the data acquisition time can also be reduced. PMID:29295537

The effect of image processing on the detection of cancers in digital mammography.

PubMed

Warren, Lucy M; Given-Wilson, Rosalind M; Wallis, Matthew G; Cooke, Julie; Halling-Brown, Mark D; Mackenzie, Alistair; Chakraborty, Dev P; Bosmans, Hilde; Dance, David R; Young, Kenneth C

2014-08-01

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.

An efficient approach to integrated MeV ion imaging.

PubMed

Nikbakht, T; Kakuee, O; Solé, V A; Vosuoghi, Y; Lamehi-Rachti, M

2018-03-01

An ionoluminescence (IL) spectral imaging system, besides the common MeV ion imaging facilities such as µ-PIXE and µ-RBS, is implemented at the Van de Graaff laboratory of Tehran. A versatile processing software is required to handle the large amount of data concurrently collected in µ-IL and common MeV ion imaging measurements through the respective methodologies. The open-source freeware PyMca, with image processing and multivariate analysis capabilities, is employed to simultaneously process common MeV ion imaging and µ-IL data. Herein, the program was adapted to support the OM_DAQ listmode data format. The appropriate performance of the µ-IL data acquisition system is confirmed through a case study. Moreover, the capabilities of the software for simultaneous analysis of µ-PIXE and µ-RBS experimental data are presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Low-cost digital image processing at the University of Oklahoma

NASA Technical Reports Server (NTRS)

Harrington, J. A., Jr.

1981-01-01

Computer assisted instruction in remote sensing at the University of Oklahoma involves two separate approaches and is dependent upon initial preprocessing of a LANDSAT computer compatible tape using software developed for an IBM 370/158 computer. In-house generated preprocessing algorithms permits students or researchers to select a subset of a LANDSAT scene for subsequent analysis using either general purpose statistical packages or color graphic image processing software developed for Apple II microcomputers. Procedures for preprocessing the data and image analysis using either of the two approaches for low-cost LANDSAT data processing are described.

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

NASA Astrophysics Data System (ADS)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

Statistical Smoothing Methods and Image Analysis

DTIC Science & Technology

1988-12-01

83 - 111. Rosenfeld, A. and Kak, A.C. (1982). Digital Picture Processing. Academic Press,Qrlando. Serra, J. (1982). Image Analysis and Mat hematical ...hypothesis testing. IEEE Trans. Med. Imaging, MI-6, 313-319. Wicksell, S.D. (1925) The corpuscle problem. A mathematical study of a biometric problem

Exploration of mineral resource deposits based on analysis of aerial and satellite image data employing artificial intelligence methods

NASA Astrophysics Data System (ADS)

Osipov, Gennady

2013-04-01

We propose a solution to the problem of exploration of various mineral resource deposits, determination of their forms / classification of types (oil, gas, minerals, gold, etc.) with the help of satellite photography of the region of interest. Images received from satellite are processed and analyzed to reveal the presence of specific signs of deposits of various minerals. Course of data processing and making forecast can be divided into some stages: Pre-processing of images. Normalization of color and luminosity characteristics, determination of the necessary contrast level and integration of a great number of separate photos into a single map of the region are performed. Construction of semantic map image. Recognition of bitmapped image and allocation of objects and primitives known to system are realized. Intelligent analysis. At this stage acquired information is analyzed with the help of a knowledge base, which contain so-called "attention landscapes" of experts. Used methods of recognition and identification of images: a) combined method of image recognition, b)semantic analysis of posterized images, c) reconstruction of three-dimensional objects from bitmapped images, d)cognitive technology of processing and interpretation of images. This stage is fundamentally new and it distinguishes suggested technology from all others. Automatic registration of allocation of experts` attention - registration of so-called "attention landscape" of experts - is the base of the technology. Landscapes of attention are, essentially, highly effective filters that cut off unnecessary information and emphasize exactly the factors used by an expert for making a decision. The technology based on denoted principles involves the next stages, which are implemented in corresponding program agents. Training mode -> Creation of base of ophthalmologic images (OI) -> Processing and making generalized OI (GOI) -> Mode of recognition and interpretation of unknown images. Training mode includes noncontact registration of eye motion, reconstruction of "attention landscape" fixed by the expert, recording the comments of the expert who is a specialist in the field of images` interpretation, and transfer this information into knowledge base.Creation of base of ophthalmologic images (OI) includes making semantic contacts from great number of OI based on analysis of OI and expert's comments.Processing of OI and making generalized OI (GOI) is realized by inductive logic algorithms and consists in synthesis of structural invariants of OI. The mode of recognition and interpretation of unknown images consists of several stages, which include: comparison of unknown image with the base of structural invariants of OI; revealing of structural invariants in unknown images; ynthesis of interpretive message of the structural invariants base and OI base (the experts` comments stored in it). We want to emphasize that the training mode does not assume special involvement of experts to teach the system - it is realized in the process of regular experts` work on image interpretation and it becomes possible after installation of a special apparatus for non contact registration of experts` attention. Consequently, the technology, which principles is described there, provides fundamentally new effective solution to the problem of exploration of mineral resource deposits based on computer analysis of aerial and satellite image data.

Image acquisitions, processing and analysis in the process of obtaining characteristics of horse navicular bone

NASA Astrophysics Data System (ADS)

Zaborowicz, M.; Włodarek, J.; Przybylak, A.; Przybył, K.; Wojcieszak, D.; Czekała, W.; Ludwiczak, A.; Boniecki, P.; Koszela, K.; Przybył, J.; Skwarcz, J.

2015-07-01

The aim of this study was investigate the possibility of using methods of computer image analysis for the assessment and classification of morphological variability and the state of health of horse navicular bone. Assumption was that the classification based on information contained in the graphical form two-dimensional digital images of navicular bone and information of horse health. The first step in the research was define the classes of analyzed bones, and then using methods of computer image analysis for obtaining characteristics from these images. This characteristics were correlated with data concerning the animal, such as: side of hooves, number of navicular syndrome (scale 0-3), type, sex, age, weight, information about lace, information about heel. This paper shows the introduction to the study of use the neural image analysis in the diagnosis of navicular bone syndrome. Prepared method can provide an introduction to the study of non-invasive way to assess the condition of the horse navicular bone.

In-line agglomeration degree estimation in fluidized bed pellet coating processes using visual imaging.

PubMed

Mehle, Andraž; Kitak, Domen; Podrekar, Gregor; Likar, Boštjan; Tomaževič, Dejan

2018-05-09

Agglomeration of pellets in fluidized bed coating processes is an undesirable phenomenon that affects the yield and quality of the product. In scope of PAT guidance, we present a system that utilizes visual imaging for in-line monitoring of the agglomeration degree. Seven pilot-scale Wurster coating processes were executed under various process conditions, providing a wide spectrum of process outcomes. Images of pellets were acquired during the coating processes in a contactless manner through an observation window of the coating apparatus. Efficient image analysis methods were developed for automatic recognition of discrete pellets and agglomerates in the acquired images. In-line obtained agglomeration degree trends revealed the agglomeration dynamics in distinct phases of the coating processes. We compared the in-line estimated agglomeration degree in the end point of each process to the results obtained by the off-line sieve analysis reference method. A strong positive correlation was obtained (coefficient of determination R 2 =0.99), confirming the feasibility of the approach. The in-line estimated agglomeration degree enables early detection of agglomeration and provides means for timely interventions to retain it in an acceptable range. Copyright © 2018 Elsevier B.V. All rights reserved.

High Throughput Multispectral Image Processing with Applications in Food Science.

PubMed

Tsakanikas, Panagiotis; Pavlidis, Dimitris; Nychas, George-John

2015-01-01

Recently, machine vision is gaining attention in food science as well as in food industry concerning food quality assessment and monitoring. Into the framework of implementation of Process Analytical Technology (PAT) in the food industry, image processing can be used not only in estimation and even prediction of food quality but also in detection of adulteration. Towards these applications on food science, we present here a novel methodology for automated image analysis of several kinds of food products e.g. meat, vanilla crème and table olives, so as to increase objectivity, data reproducibility, low cost information extraction and faster quality assessment, without human intervention. Image processing's outcome will be propagated to the downstream analysis. The developed multispectral image processing method is based on unsupervised machine learning approach (Gaussian Mixture Models) and a novel unsupervised scheme of spectral band selection for segmentation process optimization. Through the evaluation we prove its efficiency and robustness against the currently available semi-manual software, showing that the developed method is a high throughput approach appropriate for massive data extraction from food samples.

A 3D THz image processing methodology for a fully integrated, semi-automatic and near real-time operational system

NASA Astrophysics Data System (ADS)

Brook, A.; Cristofani, E.; Vandewal, M.; Matheis, C.; Jonuscheit, J.; Beigang, R.

2012-05-01

The present study proposes a fully integrated, semi-automatic and near real-time mode-operated image processing methodology developed for Frequency-Modulated Continuous-Wave (FMCW) THz images with the center frequencies around: 100 GHz and 300 GHz. The quality control of aeronautics composite multi-layered materials and structures using Non-Destructive Testing is the main focus of this work. Image processing is applied on the 3-D images to extract useful information. The data is processed by extracting areas of interest. The detected areas are subjected to image analysis for more particular investigation managed by a spatial model. Finally, the post-processing stage examines and evaluates the spatial accuracy of the extracted information.

Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments

PubMed Central

Sachs, Christian Carsten; Grünberger, Alexander; Helfrich, Stefan; Probst, Christopher; Wiechert, Wolfgang; Kohlheyer, Dietrich; Nöh, Katharina

2016-01-01

Background Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. Results We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. Conclusion Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso. PMID:27661996

Analysis on unevenness of skin color using the melanin and hemoglobin components separated by independent component analysis of skin color image

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Fujiwara, Izumi; Inoue, Yayoi; Tsumura, Norimichi; Nakaguchi, Toshiya; Iwata, Kayoko

2011-03-01

Uneven distribution of skin color is one of the biggest concerns about facial skin appearance. Recently several techniques to analyze skin color have been introduced by separating skin color information into chromophore components, such as melanin and hemoglobin. However, there are not many reports on quantitative analysis of unevenness of skin color by considering type of chromophore, clusters of different sizes and concentration of the each chromophore. We propose a new image analysis and simulation method based on chromophore analysis and spatial frequency analysis. This method is mainly composed of three techniques: independent component analysis (ICA) to extract hemoglobin and melanin chromophores from a single skin color image, an image pyramid technique which decomposes each chromophore into multi-resolution images, which can be used for identifying different sizes of clusters or spatial frequencies, and analysis of the histogram obtained from each multi-resolution image to extract unevenness parameters. As the application of the method, we also introduce an image processing technique to change unevenness of melanin component. As the result, the method showed high capabilities to analyze unevenness of each skin chromophore: 1) Vague unevenness on skin could be discriminated from noticeable pigmentation such as freckles or acne. 2) By analyzing the unevenness parameters obtained from each multi-resolution image for Japanese ladies, agerelated changes were observed in the parameters of middle spatial frequency. 3) An image processing system modulating the parameters was proposed to change unevenness of skin images along the axis of the obtained age-related change in real time.

Paediatric x-ray radiation dose reduction and image quality analysis.

PubMed

Martin, L; Ruddlesden, R; Makepeace, C; Robinson, L; Mistry, T; Starritt, H

2013-09-01

Collaboration of multiple staff groups has resulted in significant reduction in the risk of radiation-induced cancer from radiographic x-ray exposure during childhood. In this study at an acute NHS hospital trust, a preliminary audit identified initial exposure factors. These were compared with European and UK guidance, leading to the introduction of new factors that were in compliance with European guidance on x-ray tube potentials. Image quality was assessed using standard anatomical criteria scoring, and visual grading characteristics analysis assessed the impact on image quality of changes in exposure factors. This analysis determined the acceptability of gradual radiation dose reduction below the European and UK guidance levels. Chest and pelvis exposures were optimised, achieving dose reduction for each age group, with 7%-55% decrease in critical organ dose. Clinicians confirmed diagnostic image quality throughout the iterative process. Analysis of images acquired with preliminary and final exposure factors indicated an average visual grading analysis result of 0.5, demonstrating equivalent image quality. The optimisation process and final radiation doses are reported for Carestream computed radiography to aid other hospitals in minimising radiation risks to children.

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

PubMed Central

Bayır, Şafak

2016-01-01

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. PMID:27110272

Image processing and analysis of Saturn's rings

NASA Technical Reports Server (NTRS)

Yagi, G. M.; Jepsen, P. L.; Garneau, G. W.; Mosher, J. A.; Doyle, L. R.; Lorre, J. J.; Avis, C. C.; Korsmo, E. P.

1981-01-01

Processing of Voyager image data of Saturn's rings at JPL's Image Processing Laboratory is described. A software system to navigate the flight images, facilitate feature tracking, and to project the rings has been developed. This system has been used to make measurements of ring radii and to measure the velocities of the spoke features in the B-Ring. A projected ring movie to study the development of these spoke features has been generated. Finally, processing to facilitate comparison of the photometric properties of Saturn's rings at various phase angles is described.

ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging

PubMed Central

Ovesný, Martin; Křížek, Pavel; Borkovec, Josef; Švindrych, Zdeněk; Hagen, Guy M.

2014-01-01

Summary: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations. Availability and implementation: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/ Contact: guy.hagen@lf1.cuni.cz Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24771516

Medical Imaging System

NASA Technical Reports Server (NTRS)

1991-01-01

The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

AOIPS - An interactive image processing system. [Atmospheric and Oceanic Information Processing System

NASA Technical Reports Server (NTRS)

Bracken, P. A.; Dalton, J. T.; Quann, J. J.; Billingsley, J. B.

1978-01-01

The Atmospheric and Oceanographic Information Processing System (AOIPS) was developed to help applications investigators perform required interactive image data analysis rapidly and to eliminate the inefficiencies and problems associated with batch operation. This paper describes the configuration and processing capabilities of AOIPS and presents unique subsystems for displaying, analyzing, storing, and manipulating digital image data. Applications of AOIPS to research investigations in meteorology and earth resources are featured.

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.

Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis

NASA Astrophysics Data System (ADS)

Markiewicz, P. J.; Thielemans, K.; Schott, J. M.; Atkinson, D.; Arridge, S. R.; Hutton, B. F.; Ourselin, S.

2016-07-01

In this technical note we propose a rapid and scalable software solution for the processing of PET list-mode data, which allows the efficient integration of list mode data processing into the workflow of image reconstruction and analysis. All processing is performed on the graphics processing unit (GPU), making use of streamed and concurrent kernel execution together with data transfers between disk and CPU memory as well as CPU and GPU memory. This approach leads to fast generation of multiple bootstrap realisations, and when combined with fast image reconstruction and analysis, it enables assessment of uncertainties of any image statistic and of any component of the image generation process (e.g. random correction, image processing) within reasonable time frames (e.g. within five minutes per realisation). This is of particular value when handling complex chains of image generation and processing. The software outputs the following: (1) estimate of expected random event data for noise reduction; (2) dynamic prompt and random sinograms of span-1 and span-11 and (3) variance estimates based on multiple bootstrap realisations of (1) and (2) assuming reasonable count levels for acceptable accuracy. In addition, the software produces statistics and visualisations for immediate quality control and crude motion detection, such as: (1) count rate curves; (2) centre of mass plots of the radiodistribution for motion detection; (3) video of dynamic projection views for fast visual list-mode skimming and inspection; (4) full normalisation factor sinograms. To demonstrate the software, we present an example of the above processing for fast uncertainty estimation of regional SUVR (standard uptake value ratio) calculation for a single PET scan of 18F-florbetapir using the Siemens Biograph mMR scanner.

Estimation of Damaged Areas due to the 2010 Chile Earthquake and Tsunami Using SAR Imagery of Alos/palsar

NASA Astrophysics Data System (ADS)

Made, Pertiwi Jaya Ni; Miura, Fusanori; Besse Rimba, A.

2016-06-01

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km2. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

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

White, Amanda M.; Daly, Don S.; Willse, Alan R.

The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source microarray image analysis tool that allows the user to customize analysis of sets of microarray images. This tool provides several methods of identifying and quantify spot statistics, as well as extensive diagnostic statistics and images to identify poor data quality or processing. The open nature of this software allows researchers to understand the algorithms used to provide intensity estimates and to modify them easily if desired.

Fast and objective detection and analysis of structures in downhole images

NASA Astrophysics Data System (ADS)

Wedge, Daniel; Holden, Eun-Jung; Dentith, Mike; Spadaccini, Nick

2017-09-01

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

Column ratio mapping: a processing technique for atomic resolution high-angle annular dark-field (HAADF) images.

PubMed

Robb, Paul D; Craven, Alan J

2008-12-01

An image processing technique is presented for atomic resolution high-angle annular dark-field (HAADF) images that have been acquired using scanning transmission electron microscopy (STEM). This technique is termed column ratio mapping and involves the automated process of measuring atomic column intensity ratios in high-resolution HAADF images. This technique was developed to provide a fuller analysis of HAADF images than the usual method of drawing single intensity line profiles across a few areas of interest. For instance, column ratio mapping reveals the compositional distribution across the whole HAADF image and allows a statistical analysis and an estimation of errors. This has proven to be a very valuable technique as it can provide a more detailed assessment of the sharpness of interfacial structures from HAADF images. The technique of column ratio mapping is described in terms of a [110]-oriented zinc-blende structured AlAs/GaAs superlattice using the 1 angstroms-scale resolution capability of the aberration-corrected SuperSTEM 1 instrument.

Near-infrared hyperspectral imaging for quality analysis of agricultural and food products

NASA Astrophysics Data System (ADS)

Singh, C. B.; Jayas, D. S.; Paliwal, J.; White, N. D. G.

2010-04-01

Agricultural and food processing industries are always looking to implement real-time quality monitoring techniques as a part of good manufacturing practices (GMPs) to ensure high-quality and safety of their products. Near-infrared (NIR) hyperspectral imaging is gaining popularity as a powerful non-destructive tool for quality analysis of several agricultural and food products. This technique has the ability to analyse spectral data in a spatially resolved manner (i.e., each pixel in the image has its own spectrum) by applying both conventional image processing and chemometric tools used in spectral analyses. Hyperspectral imaging technique has demonstrated potential in detecting defects and contaminants in meats, fruits, cereals, and processed food products. This paper discusses the methodology of hyperspectral imaging in terms of hardware, software, calibration, data acquisition and compression, and development of prediction and classification algorithms and it presents a thorough review of the current applications of hyperspectral imaging in the analyses of agricultural and food products.

Implementation of the Pan-STARRS Image Processing Pipeline

NASA Astrophysics Data System (ADS)

Fang, Julia; Aspin, C.

2007-12-01

Pan-STARRS, or Panoramic Survey Telescope and Rapid Response System, is a wide-field imaging facility that combines small mirrors with gigapixel cameras. It surveys the entire available sky several times a month, which ultimately requires large amounts of data to be processed and stored right away. Accordingly, the Image Processing Pipeline--the IPP--is a collection of software tools that is responsible for the primary image analysis for Pan-STARRS. It includes data registration, basic image analysis such as obtaining master images and detrending the exposures, mosaic calibration when applicable, and lastly, image sum and difference. In this paper I present my work of the installation of IPP 2.1 and 2.2 on a Linux machine, running the Simtest, which is simulated data to test your installation, and finally applying the IPP to two different sets of UH 2.2m Tek data. This work was conducted by a Research Experience for Undergraduates (REU) position at the University of Hawaii's Institute for Astronomy and funded by the NSF.

Towards real-time medical diagnostics using hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Bjorgan, Asgeir; Randeberg, Lise L.

2015-07-01

Hyperspectral imaging provides non-contact, high resolution spectral images which has a substantial diagnostic potential. This can be used for e.g. diagnosis and early detection of arthritis in finger joints. Processing speed is currently a limitation for clinical use of the technique. A real-time system for analysis and visualization using GPU processing and threaded CPU processing is presented. Images showing blood oxygenation, blood volume fraction and vessel enhanced images are among the data calculated in real-time. This study shows the potential of real-time processing in this context. A combination of the processing modules will be used in detection of arthritic finger joints from hyperspectral reflectance and transmittance data.

Particle Morphology Analysis of Biomass Material Based on Improved Image Processing Method

PubMed Central

Lu, Zhaolin

2017-01-01

Particle morphology, including size and shape, is an important factor that significantly influences the physical and chemical properties of biomass material. Based on image processing technology, a method was developed to process sample images, measure particle dimensions, and analyse the particle size and shape distributions of knife-milled wheat straw, which had been preclassified into five nominal size groups using mechanical sieving approach. Considering the great variation of particle size from micrometer to millimeter, the powders greater than 250 μm were photographed by a flatbed scanner without zoom function, and the others were photographed using a scanning electron microscopy (SEM) with high-image resolution. Actual imaging tests confirmed the excellent effect of backscattered electron (BSE) imaging mode of SEM. Particle aggregation is an important factor that affects the recognition accuracy of the image processing method. In sample preparation, the singulated arrangement and ultrasonic dispersion methods were used to separate powders into particles that were larger and smaller than the nominal size of 250 μm. In addition, an image segmentation algorithm based on particle geometrical information was proposed to recognise the finer clustered powders. Experimental results demonstrated that the improved image processing method was suitable to analyse the particle size and shape distributions of ground biomass materials and solve the size inconsistencies in sieving analysis. PMID:28298925

Image sequence analysis workstation for multipoint motion analysis

NASA Astrophysics Data System (ADS)

Mostafavi, Hassan

1990-08-01

This paper describes an application-specific engineering workstation designed and developed to analyze motion of objects from video sequences. The system combines the software and hardware environment of a modem graphic-oriented workstation with the digital image acquisition, processing and display techniques. In addition to automation and Increase In throughput of data reduction tasks, the objective of the system Is to provide less invasive methods of measurement by offering the ability to track objects that are more complex than reflective markers. Grey level Image processing and spatial/temporal adaptation of the processing parameters is used for location and tracking of more complex features of objects under uncontrolled lighting and background conditions. The applications of such an automated and noninvasive measurement tool include analysis of the trajectory and attitude of rigid bodies such as human limbs, robots, aircraft in flight, etc. The system's key features are: 1) Acquisition and storage of Image sequences by digitizing and storing real-time video; 2) computer-controlled movie loop playback, freeze frame display, and digital Image enhancement; 3) multiple leading edge tracking in addition to object centroids at up to 60 fields per second from both live input video or a stored Image sequence; 4) model-based estimation and tracking of the six degrees of freedom of a rigid body: 5) field-of-view and spatial calibration: 6) Image sequence and measurement data base management; and 7) offline analysis software for trajectory plotting and statistical analysis.

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

PubMed Central

Teodoro, George; Pan, Tony; Kurc, Tahsin M.; Kong, Jun; Cooper, Lee A. D.; Podhorszki, Norbert; Klasky, Scott; Saltz, Joel H.

2014-01-01

Analysis of large pathology image datasets offers significant opportunities for the investigation of disease morphology, but the resource requirements of analysis pipelines limit the scale of such studies. Motivated by a brain cancer study, we propose and evaluate a parallel image analysis application pipeline for high throughput computation of large datasets of high resolution pathology tissue images on distributed CPU-GPU platforms. To achieve efficient execution on these hybrid systems, we have built runtime support that allows us to express the cancer image analysis application as a hierarchical data processing pipeline. The application is implemented as a coarse-grain pipeline of stages, where each stage may be further partitioned into another pipeline of fine-grain operations. The fine-grain operations are efficiently managed and scheduled for computation on CPUs and GPUs using performance aware scheduling techniques along with several optimizations, including architecture aware process placement, data locality conscious task assignment, data prefetching, and asynchronous data copy. These optimizations are employed to maximize the utilization of the aggregate computing power of CPUs and GPUs and minimize data copy overheads. Our experimental evaluation shows that the cooperative use of CPUs and GPUs achieves significant improvements on top of GPU-only versions (up to 1.6×) and that the execution of the application as a set of fine-grain operations provides more opportunities for runtime optimizations and attains better performance than coarser-grain, monolithic implementations used in other works. An implementation of the cancer image analysis pipeline using the runtime support was able to process an image dataset consisting of 36,848 4Kx4K-pixel image tiles (about 1.8TB uncompressed) in less than 4 minutes (150 tiles/second) on 100 nodes of a state-of-the-art hybrid cluster system. PMID:25419546

Operational Analysis of Time-Optimal Maneuvering for Imaging Spacecraft

DTIC Science & Technology

2013-03-01

imaging spacecraft. The analysis is facilitated through the use of AGI’s Systems Tool Kit ( STK ) software. An Analytic Hierarchy Process (AHP)-based...the Singapore-developed X-SAT imaging spacecraft. The analysis is facilitated through the use of AGI’s Systems Tool Kit ( STK ) software. An Analytic...89  B.  FUTURE WORK................................................................................. 90  APPENDIX A. STK DATA AND BENEFIT

Predictive images of postoperative levator resection outcome using image processing software.

PubMed

Mawatari, Yuki; Fukushima, Mikiko

2016-01-01

This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller's muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop ® ). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery.

Predictive images of postoperative levator resection outcome using image processing software

PubMed Central

Mawatari, Yuki; Fukushima, Mikiko

2016-01-01

Purpose This study aims to evaluate the efficacy of processed images to predict postoperative appearance following levator resection. Methods Analysis involved 109 eyes from 65 patients with blepharoptosis who underwent advancement of levator aponeurosis and Müller’s muscle complex (levator resection). Predictive images were prepared from preoperative photographs using the image processing software (Adobe Photoshop®). Images of selected eyes were digitally enlarged in an appropriate manner and shown to patients prior to surgery. Results Approximately 1 month postoperatively, we surveyed our patients using questionnaires. Fifty-six patients (89.2%) were satisfied with their postoperative appearances, and 55 patients (84.8%) positively responded to the usefulness of processed images to predict postoperative appearance. Conclusion Showing processed images that predict postoperative appearance to patients prior to blepharoptosis surgery can be useful for those patients concerned with their postoperative appearance. This approach may serve as a useful tool to simulate blepharoptosis surgery. PMID:27757008

MOSAIC: Software for creating mosaics from collections of images

NASA Technical Reports Server (NTRS)

Varosi, F.; Gezari, D. Y.

1992-01-01

We have developed a powerful, versatile image processing and analysis software package called MOSAIC, designed specifically for the manipulation of digital astronomical image data obtained with (but not limited to) two-dimensional array detectors. The software package is implemented using the Interactive Data Language (IDL), and incorporates new methods for processing, calibration, analysis, and visualization of astronomical image data, stressing effective methods for the creation of mosaic images from collections of individual exposures, while at the same time preserving the photometric integrity of the original data. Since IDL is available on many computers, the MOSAIC software runs on most UNIX and VAX workstations with the X-Windows or Sun View graphics interface.

Analyzing microtomography data with Python and the scikit-image library.

PubMed

Gouillart, Emmanuelle; Nunez-Iglesias, Juan; van der Walt, Stéfan

2017-01-01

The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit-image is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. scikit-image users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. scikit-image combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.

Method for Assessment of Changes in the Width of Cracks in Cement Composites with Use of Computer Image Processing and Analysis

NASA Astrophysics Data System (ADS)

Tomczak, Kamil; Jakubowski, Jacek; Fiołek, Przemysław

2017-06-01

Crack width measurement is an important element of research on the progress of self-healing cement composites. Due to the nature of this research, the method of measuring the width of cracks and their changes over time must meet specific requirements. The article presents a novel method of measuring crack width based on images from a scanner with an optical resolution of 6400 dpi, subject to initial image processing in the ImageJ development environment and further processing and analysis of results. After registering a series of images of the cracks at different times using SIFT conversion (Scale-Invariant Feature Transform), a dense network of line segments is created in all images, intersecting the cracks perpendicular to the local axes. Along these line segments, brightness profiles are extracted, which are the basis for determination of crack width. The distribution and rotation of the line of intersection in a regular layout, automation of transformations, management of images and profiles of brightness, and data analysis to determine the width of cracks and their changes over time are made automatically by own code in the ImageJ and VBA environment. The article describes the method, tests on its properties, sources of measurement uncertainty. It also presents an example of application of the method in research on autogenous self-healing of concrete, specifically the ability to reduce a sample crack width and its full closure within 28 days of the self-healing process.

A new automated assessment method for contrast-detail images by applying support vector machine and its robustness to nonlinear image processing.

PubMed

Takei, Takaaki; Ikeda, Mitsuru; Imai, Kuniharu; Yamauchi-Kawaura, Chiyo; Kato, Katsuhiko; Isoda, Haruo

2013-09-01

The automated contrast-detail (C-D) analysis methods developed so-far cannot be expected to work well on images processed with nonlinear methods, such as noise reduction methods. Therefore, we have devised a new automated C-D analysis method by applying support vector machine (SVM), and tested for its robustness to nonlinear image processing. We acquired the CDRAD (a commercially available C-D test object) images at a tube voltage of 120 kV and a milliampere-second product (mAs) of 0.5-5.0. A partial diffusion equation based technique was used as noise reduction method. Three radiologists and three university students participated in the observer performance study. The training data for our SVM method was the classification data scored by the one radiologist for the CDRAD images acquired at 1.6 and 3.2 mAs and their noise-reduced images. We also compared the performance of our SVM method with the CDRAD Analyser algorithm. The mean C-D diagrams (that is a plot of the mean of the smallest visible hole diameter vs. hole depth) obtained from our devised SVM method agreed well with the ones averaged across the six human observers for both original and noise-reduced CDRAD images, whereas the mean C-D diagrams from the CDRAD Analyser algorithm disagreed with the ones from the human observers for both original and noise-reduced CDRAD images. In conclusion, our proposed SVM method for C-D analysis will work well for the images processed with the non-linear noise reduction method as well as for the original radiographic images.

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin.

PubMed

Ristivojević, Petar; Trifković, Jelena; Vovk, Irena; Milojković-Opsenica, Dušanka

2017-01-01

Considering the introduction of phytochemical fingerprint analysis, as a method of screening the complex natural products for the presence of most bioactive compounds, use of chemometric classification methods, application of powerful scanning and image capturing and processing devices and algorithms, advancement in development of novel stationary phases as well as various separation modalities, high-performance thin-layer chromatography (HPTLC) fingerprinting is becoming attractive and fruitful field of separation science. Multivariate image analysis is crucial in the light of proper data acquisition. In a current study, different image processing procedures were studied and compared in detail on the example of HPTLC chromatograms of plant resins. In that sense, obtained variables such as gray intensities of pixels along the solvent front, peak area and mean values of peak were used as input data and compared to obtained best classification models. Important steps in image analysis, baseline removal, denoising, target peak alignment and normalization were pointed out. Numerical data set based on mean value of selected bands and intensities of pixels along the solvent front proved to be the most convenient for planar-chromatographic profiling, although required at least the basic knowledge on image processing methodology, and could be proposed for further investigation in HPLTC fingerprinting. Copyright © 2016 Elsevier B.V. All rights reserved.

The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH).

PubMed

García-Rojo, Marcial; Gonçalves, Luís; Blobel, Bernd

2012-01-01

The COST Action IC0604 "Telepathology Network in Europe" (EURO-TELEPATH) is a European COST Action that has been running from 2007 to 2011. COST Actions are funded by the COST (European Cooperation in the field of Scientific and Technical Research) Agency, supported by the Seventh Framework Programme for Research and Technological Development (FP7), of the European Union. EURO-TELEPATH's main objectives were evaluating and validating the common technological framework and communication standards required to access, transmit and manage digital medical records by pathologists and other medical professionals in a networked environment. The project was organized in four working groups. orking Group 1 "Business modeling in pathology" has designed main pathology processes - Frozen Study, Formalin Fixed Specimen Study, Telepathology, Cytology, and Autopsy -using Business Process Modeling Notation (BPMN). orking Group 2 "Informatics standards in pathology" has been dedicated to promoting the development and application of informatics standards in pathology, collaborating with Integrating the Healthcare Enterprise (IHE), Digital Imaging and Communications in Medicine (DICOM), Health Level Seven (HL7), and other standardization bodies. Working Group 3 "Images: Analysis, Processing, Retrieval and Management" worked on the use of virtual or digital slides that are fostering the use of image processing and analysis in pathology not only for research purposes, but also in daily practice. Working Group 4 "Technology and Automation in Pathology" was focused on studying the adequacy of current existing technical solutions, including, e.g., the quality of images obtained by slide scanners, or the efficiency of image analysis applications. Major outcome of this action are the collaboration with international health informatics standardization bodies to foster the development of standards for digital pathology, offering a new approach for workflow analysis, based in business process modeling. Health terminology standardization research has become a topic of high interest. Future research work should focus on standardization of automatic image analysis and tissue microarrays imaging.

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

PubMed

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

2014-01-01

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

Parallel ICA and its hardware implementation in hyperspectral image analysis

NASA Astrophysics Data System (ADS)

Du, Hongtao; Qi, Hairong; Peterson, Gregory D.

2004-04-01

Advances in hyperspectral images have dramatically boosted remote sensing applications by providing abundant information using hundreds of contiguous spectral bands. However, the high volume of information also results in excessive computation burden. Since most materials have specific characteristics only at certain bands, a lot of these information is redundant. This property of hyperspectral images has motivated many researchers to study various dimensionality reduction algorithms, including Projection Pursuit (PP), Principal Component Analysis (PCA), wavelet transform, and Independent Component Analysis (ICA), where ICA is one of the most popular techniques. It searches for a linear or nonlinear transformation which minimizes the statistical dependence between spectral bands. Through this process, ICA can eliminate superfluous but retain practical information given only the observations of hyperspectral images. One hurdle of applying ICA in hyperspectral image (HSI) analysis, however, is its long computation time, especially for high volume hyperspectral data sets. Even the most efficient method, FastICA, is a very time-consuming process. In this paper, we present a parallel ICA (pICA) algorithm derived from FastICA. During the unmixing process, pICA divides the estimation of weight matrix into sub-processes which can be conducted in parallel on multiple processors. The decorrelation process is decomposed into the internal decorrelation and the external decorrelation, which perform weight vector decorrelations within individual processors and between cooperative processors, respectively. In order to further improve the performance of pICA, we seek hardware solutions in the implementation of pICA. Until now, there are very few hardware designs for ICA-related processes due to the complicated and iterant computation. This paper discusses capacity limitation of FPGA implementations for pICA in HSI analysis. A synthesis of Application-Specific Integrated Circuit (ASIC) is designed for pICA-based dimensionality reduction in HSI analysis. The pICA design is implemented using standard-height cells and aimed at TSMC 0.18 micron process. During the synthesis procedure, three ICA-related reconfigurable components are developed for the reuse and retargeting purpose. Preliminary results show that the standard-height cell based ASIC synthesis provide an effective solution for pICA and ICA-related processes in HSI analysis.

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement

NASA Astrophysics Data System (ADS)

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

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. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Analysis of 3D Subharmonic Ultrasound Signals from Patients with Known Breast Masses for Lesion Differentiation

DTIC Science & Technology

2012-10-01

separate image processing course were attended and this programming language will be used for the research component of this project. Subharmonic...4 5 BODY ...lesions. 5 BODY 5.1 Training Component The training component of this research has been split into breast imaging and image processing arms

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. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Novel Bit-level Image Encryption Method Based on Chaotic Map and Dynamic Grouping

NASA Astrophysics Data System (ADS)

Zhang, Guo-Ji; Shen, Yan

2012-10-01

In this paper, a novel bit-level image encryption method based on dynamic grouping is proposed. In the proposed method, the plain-image is divided into several groups randomly, then permutation-diffusion process on bit level is carried out. The keystream generated by logistic map is related to the plain-image, which confuses the relationship between the plain-image and the cipher-image. The computer simulation results of statistical analysis, information entropy analysis and sensitivity analysis show that the proposed encryption method is secure and reliable enough to be used for communication application.

System design and implementation of digital-image processing using computational grids

NASA Astrophysics Data System (ADS)

Shen, Zhanfeng; Luo, Jiancheng; Zhou, Chenghu; Huang, Guangyu; Ma, Weifeng; Ming, Dongping

2005-06-01

As a special type of digital image, remotely sensed images are playing increasingly important roles in our daily lives. Because of the enormous amounts of data involved, and the difficulties of data processing and transfer, an important issue for current computer and geo-science experts is developing internet technology to implement rapid remotely sensed image processing. Computational grids are able to solve this problem effectively. These networks of computer workstations enable the sharing of data and resources, and are used by computer experts to solve imbalances of network resources and lopsided usage. In China, computational grids combined with spatial-information-processing technology have formed a new technology: namely, spatial-information grids. In the field of remotely sensed images, spatial-information grids work more effectively for network computing, data processing, resource sharing, task cooperation and so on. This paper focuses mainly on the application of computational grids to digital-image processing. Firstly, we describe the architecture of digital-image processing on the basis of computational grids, its implementation is then discussed in detail with respect to the technology of middleware. The whole network-based intelligent image-processing system is evaluated on the basis of the experimental analysis of remotely sensed image-processing tasks; the results confirm the feasibility of the application of computational grids to digital-image processing.

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.

Within-subject template estimation for unbiased longitudinal image analysis.

PubMed

Reuter, Martin; Schmansky, Nicholas J; Rosas, H Diana; Fischl, Bruce

2012-07-16

Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects. Copyright © 2012 Elsevier Inc. All rights reserved.

PCIPS 2.0: Powerful multiprofile image processing implemented on PCs

NASA Technical Reports Server (NTRS)

Smirnov, O. M.; Piskunov, N. E.

1992-01-01

Over the years, the processing power of personal computers has steadily increased. Now, 386- and 486-based PC's are fast enough for many image processing applications, and inexpensive enough even for amateur astronomers. PCIPS is an image processing system based on these platforms that was designed to satisfy a broad range of data analysis needs, while requiring minimum hardware and providing maximum expandability. It will run (albeit at a slow pace) even on a 80286 with 640K memory, but will take full advantage of bigger memory and faster CPU's. Because the actual image processing is performed by external modules, the system can be easily upgraded by the user for all sorts of scientific data analysis. PCIPS supports large format lD and 2D images in any numeric type from 8-bit integer to 64-bit floating point. The images can be displayed, overlaid, printed and any part of the data examined via an intuitive graphical user interface that employs buttons, pop-up menus, and a mouse. PCIPS automatically converts images between different types and sizes to satisfy the requirements of various applications. PCIPS features an API that lets users develop custom applications in C or FORTRAN. While doing so, a programmer can concentrate on the actual data processing, because PCIPS assumes responsibility for accessing images and interacting with the user. This also ensures that all applications, even custom ones, have a consistent and user-friendly interface. The API is compatible with factory programming, a metaphor for constructing image processing procedures that will be implemented in future versions of the system. Several application packages were created under PCIPS. The basic package includes elementary arithmetics and statistics, geometric transformations and import/export in various formats (FITS, binary, ASCII, and GIF). The CCD processing package and the spectral analysis package were successfully used to reduce spectra from the Nordic Telescope at La Palma. A photometry package is also available, and other packages are being developed. A multitasking version of PCIPS that utilizes the factory programming concept is currently under development. This version will remain compatible (on the source code level) with existing application packages and custom applications.

A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications

PubMed Central

Sechopoulos, Ioannis

2013-01-01

Many important post-acquisition aspects of breast tomosynthesis imaging can impact its clinical performance. Chief among them is the reconstruction algorithm that generates the representation of the three-dimensional breast volume from the acquired projections. But even after reconstruction, additional processes, such as artifact reduction algorithms, computer aided detection and diagnosis, among others, can also impact the performance of breast tomosynthesis in the clinical realm. In this two part paper, a review of breast tomosynthesis research is performed, with an emphasis on its medical physics aspects. In the companion paper, the first part of this review, the research performed relevant to the image acquisition process is examined. This second part will review the research on the post-acquisition aspects, including reconstruction, image processing, and analysis, as well as the advanced applications being investigated for breast tomosynthesis. PMID:23298127

Using normalization 3D model for automatic clinical brain quantative analysis and evaluation

NASA Astrophysics Data System (ADS)

Lin, Hong-Dun; Yao, Wei-Jen; Hwang, Wen-Ju; Chung, Being-Tau; Lin, Kang-Ping

2003-05-01

Functional medical imaging, such as PET or SPECT, is capable of revealing physiological functions of the brain, and has been broadly used in diagnosing brain disorders by clinically quantitative analysis for many years. In routine procedures, physicians manually select desired ROIs from structural MR images and then obtain physiological information from correspondent functional PET or SPECT images. The accuracy of quantitative analysis thus relies on that of the subjectively selected ROIs. Therefore, standardizing the analysis procedure is fundamental and important in improving the analysis outcome. In this paper, we propose and evaluate a normalization procedure with a standard 3D-brain model to achieve precise quantitative analysis. In the normalization process, the mutual information registration technique was applied for realigning functional medical images to standard structural medical images. Then, the standard 3D-brain model that shows well-defined brain regions was used, replacing the manual ROIs in the objective clinical analysis. To validate the performance, twenty cases of I-123 IBZM SPECT images were used in practical clinical evaluation. The results show that the quantitative analysis outcomes obtained from this automated method are in agreement with the clinical diagnosis evaluation score with less than 3% error in average. To sum up, the method takes advantage of obtaining precise VOIs, information automatically by well-defined standard 3-D brain model, sparing manually drawn ROIs slice by slice from structural medical images in traditional procedure. That is, the method not only can provide precise analysis results, but also improve the process rate for mass medical images in clinical.

Digital image processing of bone - Problems and potentials

NASA Technical Reports Server (NTRS)

Morey, E. R.; Wronski, T. J.

1980-01-01

The development of a digital image processing system for bone histomorphometry and fluorescent marker monitoring is discussed. The system in question is capable of making measurements of UV or light microscope features on a video screen with either video or computer-generated images, and comprises a microscope, low-light-level video camera, video digitizer and display terminal, color monitor, and PDP 11/34 computer. Capabilities demonstrated in the analysis of an undecalcified rat tibia include the measurement of perimeter and total bone area, and the generation of microscope images, false color images, digitized images and contoured images for further analysis. Software development will be based on an existing software library, specifically the mini-VICAR system developed at JPL. It is noted that the potentials of the system in terms of speed and reliability far exceed any problems associated with hardware and software development.

The microcomputer workstation - An alternate hardware architecture for remotely sensed image analysis

NASA Technical Reports Server (NTRS)

Erickson, W. K.; Hofman, L. B.; Donovan, W. E.

1984-01-01

Difficulties regarding the digital image analysis of remotely sensed imagery can arise in connection with the extensive calculations required. In the past, an expensive large to medium mainframe computer system was needed for performing these calculations. For image-processing applications smaller minicomputer-based systems are now used by many organizations. The costs for such systems are still in the range from $100K to $300K. Recently, as a result of new developments, the use of low-cost microcomputers for image processing and display systems appeared to have become feasible. These developments are related to the advent of the 16-bit microprocessor and the concept of the microcomputer workstation. Earlier 8-bit microcomputer-based image processing systems are briefly examined, and a computer workstation architecture is discussed. Attention is given to a microcomputer workstation developed by Stanford University, and the design and implementation of a workstation network.

Asymmetry and irregularity border as discrimination factor between melanocytic lesions

NASA Astrophysics Data System (ADS)

Sbrissa, David; Pratavieira, Sebastião.; Salvio, Ana Gabriela; Kurachi, Cristina; Bagnato, Vanderlei Salvadori; Costa, Luciano Da Fontoura; Travieso, Gonzalo

2015-06-01

Image processing tools have been widely used in systems supporting medical diagnosis. The use of mobile devices for the diagnosis of melanoma can assist doctors and improve their diagnosis of a melanocytic lesion. This study proposes a method of image analysis for melanoma discrimination from other types of melanocytic lesions, such as regular and atypical nevi. The process is based on extracting features related with asymmetry and border irregularity. It were collected 104 images, from medical database of two years. The images were obtained with standard digital cameras without lighting and scale control. Metrics relating to the characteristics of shape, asymmetry and curvature of the contour were extracted from segmented images. Linear Discriminant Analysis was performed for dimensionality reduction and data visualization. Segmentation results showed good efficiency in the process, with approximately 88:5% accuracy. Validation results presents sensibility and specificity 85% and 70% for melanoma detection, respectively.

Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.

PubMed

Verma, Garima; Palombo, Alessandro; Grigioni, Mauro; La Monaca, Morena; D'Avenio, Giuseppe

2018-01-01

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.

A new image encryption algorithm based on the fractional-order hyperchaotic Lorenz system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Huang, Xia; Li, Yu-Xia; Song, Xiao-Na

2013-01-01

We propose a new image encryption algorithm on the basis of the fractional-order hyperchaotic Lorenz system. While in the process of generating a key stream, the system parameters and the derivative order are embedded in the proposed algorithm to enhance the security. Such an algorithm is detailed in terms of security analyses, including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. The experimental results demonstrate that the proposed image encryption scheme has the advantages of large key space and high security for practical image encryption.

Image processing for optical mapping.

PubMed

Ravindran, Prabu; Gupta, Aditya

2015-01-01

Optical Mapping is an established single-molecule, whole-genome analysis system, which has been used to gain a comprehensive understanding of genomic structure and to study structural variation of complex genomes. A critical component of Optical Mapping system is the image processing module, which extracts single molecule restriction maps from image datasets of immobilized, restriction digested and fluorescently stained large DNA molecules. In this review, we describe robust and efficient image processing techniques to process these massive datasets and extract accurate restriction maps in the presence of noise, ambiguity and confounding artifacts. We also highlight a few applications of the Optical Mapping system.

[Image Feature Extraction and Discriminant Analysis of Xinjiang Uygur Medicine Based on Color Histogram].

PubMed

Hamit, Murat; Yun, Weikang; Yan, Chuanbo; Kutluk, Abdugheni; Fang, Yang; Alip, Elzat

2015-06-01

Image feature extraction is an important part of image processing and it is an important field of research and application of image processing technology. Uygur medicine is one of Chinese traditional medicine and researchers pay more attention to it. But large amounts of Uygur medicine data have not been fully utilized. In this study, we extracted the image color histogram feature of herbal and zooid medicine of Xinjiang Uygur. First, we did preprocessing, including image color enhancement, size normalizition and color space transformation. Then we extracted color histogram feature and analyzed them with statistical method. And finally, we evaluated the classification ability of features by Bayes discriminant analysis. Experimental results showed that high accuracy for Uygur medicine image classification was obtained by using color histogram feature. This study would have a certain help for the content-based medical image retrieval for Xinjiang Uygur medicine.

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with digital remote sensing data

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Meisner, D. E. (Principal Investigator)

1980-01-01

An investigation was conducted into ways to improve the involvement of state and local user personnel in the digital image analysis process by isolating those elements of the analysis process which require extensive involvement by field personnel and providing means for performing those activities apart from a computer facility. In this way, the analysis procedure can be converted from a centralized activity focused on a computer facility to a distributed activity in which users can interact with the data at the field office level or in the field itself. A general image processing software was developed on the University of Minnesota computer system (Control Data Cyber models 172 and 74). The use of color hardcopy image data as a primary medium in supervised training procedures was investigated and digital display equipment and a coordinate digitizer were procured.

Connected component analysis of review-SEM images for sub-10nm node process verification

NASA Astrophysics Data System (ADS)

Halder, Sandip; Leray, Philippe; Sah, Kaushik; Cross, Andrew; Parisi, Paolo

2017-03-01

Analysis of hotspots is becoming more and more critical as we scale from node to node. To define true process windows at sub-14 nm technology nodes, often defect inspections are being included to weed out design weak spots (often referred to as hotspots). Defect inspection sub 28 nm nodes is a two pass process. Defect locations identified by optical inspection tools need to be reviewed by review-SEM's to understand exactly which feature is failing in the region flagged by the optical tool. The images grabbed by the review-SEM tool are used for classification but rarely for quantification. The goal of this paper is to see if the thousands of review-SEM images which are existing can be used for quantification and further analysis. More specifically we address the SEM quantification problem with connected component analysis.

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.

[Research applications in digital radiology. Big data and co].

PubMed

Müller, H; Hanbury, A

2016-02-01

Medical imaging produces increasingly complex images (e.g. thinner slices and higher resolution) with more protocols, so that image reading has also become much more complex. More information needs to be processed and usually the number of radiologists available for these tasks has not increased to the same extent. The objective of this article is to present current research results from projects on the use of image data for clinical decision support. An infrastructure that can allow large volumes of data to be accessed is presented. In this way the best performing tools can be identified without the medical data having to leave secure servers. The text presents the results of the VISCERAL and Khresmoi EU-funded projects, which allow the analysis of previous cases from institutional archives to support decision-making and for process automation. The results also represent a secure evaluation environment for medical image analysis. This allows the use of data extracted from past cases to solve information needs occurring when diagnosing new cases. The presented research prototypes allow direct extraction of knowledge from the visual data of the images and to use this for decision support or process automation. Real clinical use has not been tested but several subjective user tests showed the effectiveness and efficiency of the process. The future in radiology will clearly depend on better use of the important knowledge in clinical image archives to automate processes and aid decision-making via big data analysis. This can help concentrate the work of radiologists towards the most important parts of diagnostics.

Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

PubMed

Fu, Dongmei; Tong, Hejun; Zheng, Shuang; Luo, Ling; Gao, Fulin; Minar, Jiri

2016-07-22

Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis.

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

PubMed

Lee, Unseok; Chang, Sungyul; Putra, Gian Anantrio; Kim, Hyoungseok; Kim, Dong Hwan

2018-01-01

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Toshiba TDF-500 High Resolution Viewing And Analysis System

NASA Astrophysics Data System (ADS)

Roberts, Barry; Kakegawa, M.; Nishikawa, M.; Oikawa, D.

1988-06-01

A high resolution, operator interactive, medical viewing and analysis system has been developed by Toshiba and Bio-Imaging Research. This system provides many advanced features including high resolution displays, a very large image memory and advanced image processing capability. In particular, the system provides CRT frame buffers capable of update in one frame period, an array processor capable of image processing at operator interactive speeds, and a memory system capable of updating multiple frame buffers at frame rates whilst supporting multiple array processors. The display system provides 1024 x 1536 display resolution at 40Hz frame and 80Hz field rates. In particular, the ability to provide whole or partial update of the screen at the scanning rate is a key feature. This allows multiple viewports or windows in the display buffer with both fixed and cine capability. To support image processing features such as windowing, pan, zoom, minification, filtering, ROI analysis, multiplanar and 3D reconstruction, a high performance CPU is integrated into the system. This CPU is an array processor capable of up to 400 million instructions per second. To support the multiple viewer and array processors' instantaneous high memory bandwidth requirement, an ultra fast memory system is used. This memory system has a bandwidth capability of 400MB/sec and a total capacity of 256MB. This bandwidth is more than adequate to support several high resolution CRT's and also the fast processing unit. This fully integrated approach allows effective real time image processing. The integrated design of viewing system, memory system and array processor are key to the imaging system. It is the intention to describe the architecture of the image system in this paper.

Detection of Glaucoma Using Image Processing Techniques: A Critique.

PubMed

Kumar, B Naveen; Chauhan, R P; Dahiya, Nidhi

2018-01-01

The primary objective of this article is to present a summary of different types of image processing methods employed for the detection of glaucoma, a serious eye disease. Glaucoma affects the optic nerve in which retinal ganglion cells become dead, and this leads to loss of vision. The principal cause is the increase in intraocular pressure, which occurs in open-angle and angle-closure glaucoma, the two major types affecting the optic nerve. In the early stages of glaucoma, no perceptible symptoms appear. As the disease progresses, vision starts to become hazy, leading to blindness. Therefore, early detection of glaucoma is needed for prevention. Manual analysis of ophthalmic images is fairly time-consuming and accuracy depends on the expertise of the professionals. Automatic analysis of retinal images is an important tool. Automation aids in the detection, diagnosis, and prevention of risks associated with the disease. Fundus images obtained from a fundus camera have been used for the analysis. Requisite pre-processing techniques have been applied to the image and, depending upon the technique, various classifiers have been used to detect glaucoma. The techniques mentioned in the present review have certain advantages and disadvantages. Based on this study, one can determine which technique provides an optimum result.

Automatic Image Processing Workflow for the Keck/NIRC2 Vortex Coronagraph

NASA Astrophysics Data System (ADS)

Xuan, Wenhao; Cook, Therese; Ngo, Henry; Zawol, Zoe; Ruane, Garreth; Mawet, Dimitri

2018-01-01

The Keck/NIRC2 camera, equipped with the vortex coronagraph, is an instrument targeted at the high contrast imaging of extrasolar planets. To uncover a faint planet signal from the overwhelming starlight, we utilize the Vortex Image Processing (VIP) library, which carries out principal component analysis to model and remove the stellar point spread function. To bridge the gap between data acquisition and data reduction, we implement a workflow that 1) downloads, sorts, and processes data with VIP, 2) stores the analysis products into a database, and 3) displays the reduced images, contrast curves, and auxiliary information on a web interface. Both angular differential imaging and reference star differential imaging are implemented in the analysis module. A real-time version of the workflow runs during observations, allowing observers to make educated decisions about time distribution on different targets, hence optimizing science yield. The post-night version performs a standardized reduction after the observation, building up a valuable database that not only helps uncover new discoveries, but also enables a statistical study of the instrument itself. We present the workflow, and an examination of the contrast performance of the NIRC2 vortex with respect to factors including target star properties and observing conditions.

The Analysis of Image Segmentation Hierarchies with a Graph-based Knowledge Discovery System

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cooke, diane J.; Ketkar, Nikhil; Aksoy, Selim

2008-01-01

Currently available pixel-based analysis techniques do not effectively extract the information content from the increasingly available high spatial resolution remotely sensed imagery data. A general consensus is that object-based image analysis (OBIA) is required to effectively analyze this type of data. OBIA is usually a two-stage process; image segmentation followed by an analysis of the segmented objects. We are exploring an approach to OBIA in which hierarchical image segmentations provided by the Recursive Hierarchical Segmentation (RHSEG) software developed at NASA GSFC are analyzed by the Subdue graph-based knowledge discovery system developed by a team at Washington State University. In this paper we discuss out initial approach to representing the RHSEG-produced hierarchical image segmentations in a graphical form understandable by Subdue, and provide results on real and simulated data. We also discuss planned improvements designed to more effectively and completely convey the hierarchical segmentation information to Subdue and to improve processing efficiency.

Segmentation-free image processing and analysis of precipitate shapes in 2D and 3D

NASA Astrophysics Data System (ADS)

Bales, Ben; Pollock, Tresa; Petzold, Linda

2017-06-01

Segmentation based image analysis techniques are routinely employed for quantitative analysis of complex microstructures containing two or more phases. The primary advantage of these approaches is that spatial information on the distribution of phases is retained, enabling subjective judgements of the quality of the segmentation and subsequent analysis process. The downside is that computing micrograph segmentations with data from morphologically complex microstructures gathered with error-prone detectors is challenging and, if no special care is taken, the artifacts of the segmentation will make any subsequent analysis and conclusions uncertain. In this paper we demonstrate, using a two phase nickel-base superalloy microstructure as a model system, a new methodology for analysis of precipitate shapes using a segmentation-free approach based on the histogram of oriented gradients feature descriptor, a classic tool in image analysis. The benefits of this methodology for analysis of microstructure in two and three-dimensions are demonstrated.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Enhancements to the Image Analysis Tool for Core Punch Experiments and Simulations (vs. 2014)

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

Hogden, John Edward; Unal, Cetin

A previous paper (Hogden & Unal, 2012, Image Analysis Tool for Core Punch Experiments and Simulations) described an image processing computer program developed at Los Alamos National Laboratory. This program has proven useful so developement has been continued. In this paper we describe enhacements to the program as of 2014.

SEM AutoAnalysis: enhancing photomask and NIL defect disposition and review

NASA Astrophysics Data System (ADS)

Schulz, Kristian; Egodage, Kokila; Tabbone, Gilles; Ehrlich, Christian; Garetto, Anthony

2017-06-01

For defect disposition and repair verification regarding printability, AIMS™ is the state of the art measurement tool in industry. With its unique capability of capturing aerial images of photomasks it is the one method that comes closest to emulating the printing behaviour of a scanner. However for nanoimprint lithography (NIL) templates aerial images cannot be applied to evaluate the success of a repair process. Hence, for NIL defect dispositioning scanning, electron microscopy (SEM) imaging is the method of choice. In addition, it has been a standard imaging method for further root cause analysis of defects and defect review on optical photomasks which enables 2D or even 3D mask profiling at high resolutions. In recent years a trend observed in mask shops has been the automation of processes that traditionally were driven by operators. This of course has brought many advantages one of which is freeing cost intensive labour from conducting repetitive and tedious work. Furthermore, it reduces variability in processes due to different operator skill and experience levels which at the end contributes to eliminating the human factor. Taking these factors into consideration, one of the software based solutions available under the FAVOR® brand to support customer needs is the aerial image evaluation software, AIMS™ AutoAnalysis (AAA). It provides fully automated analysis of AIMS™ images and runs in parallel to measurements. This is enabled by its direct connection and communication with the AIMS™tools. As one of many positive outcomes, generating automated result reports is facilitated, standardizing the mask manufacturing workflow. Today, AAA has been successfully introduced into production at multiple customers and is supporting the workflow as described above. These trends indeed have triggered the demand for similar automation with respect to SEM measurements leading to the development of SEM AutoAnalysis (SAA). It aims towards a fully automated SEM image evaluation process utilizing a completely different algorithm due to the different nature of SEM images and aerial images. Both AAA and SAA are the building blocks towards an image evaluation suite in the mask shop industry.

Image retrieval and processing system version 2.0 development work

NASA Technical Reports Server (NTRS)

Slavney, Susan H.; Guinness, Edward A.

1991-01-01

The Image Retrieval and Processing System (IRPS) is a software package developed at Washington University and used by the NASA Regional Planetary Image Facilities (RPIF's). The IRPS combines data base management and image processing components to allow the user to examine catalogs of image data, locate the data of interest, and perform radiometric and geometric calibration of the data in preparation for analysis. Version 1.0 of IRPS was completed in Aug. 1989 and was installed at several IRPS's. Other RPIF's use remote logins via NASA Science Internet to access IRPS at Washington University. Work was begun on designing and population a catalog of Magellan image products that will be part of IRPS Version 2.0, planned for release by the end of calendar year 1991. With this catalog, a user will be able to search by orbit and by location for Magellan Basic Image Data Records (BIDR's), Mosaicked Image Data Records (MIDR's), and Altimetry-Radiometry Composite Data Records (ARCDR's). The catalog will include the Magellan CD-ROM volume, director, and file name for each data product. The image processing component of IRPS is based on the Planetary Image Cartography Software (PICS) developed by the U.S. Geological Survey, Flagstaff, Arizona. To augment PICS capabilities, a set of image processing programs were developed that are compatible with PICS-format images. This software includes general-purpose functions that PICS does not have, analysis and utility programs for specific data sets, and programs from other sources that were modified to work with PICS images. Some of the software will be integrated into the Version 2.0 release of IRPS. A table is presented that lists the programs with a brief functional description of each.

Image encryption based on a delayed fractional-order chaotic logistic system

NASA Astrophysics Data System (ADS)

Wang, Zhen; Huang, Xia; Li, Ning; Song, Xiao-Na

2012-05-01

A new image encryption scheme is proposed based on a delayed fractional-order chaotic logistic system. In the process of generating a key stream, the time-varying delay and fractional derivative are embedded in the proposed scheme to improve the security. Such a scheme is described in detail with security analyses including correlation analysis, information entropy analysis, run statistic analysis, mean-variance gray value analysis, and key sensitivity analysis. Experimental results show that the newly proposed image encryption scheme possesses high security.

X-ray CT analysis of pore structure in sand

NASA Astrophysics Data System (ADS)

Mukunoki, Toshifumi; Miyata, Yoshihisa; Mikami, Kazuaki; Shiota, Erika

2016-06-01

The development of microfocused X-ray computed tomography (CT) devices enables digital imaging analysis at the pore scale. The applications of these devices are diverse in soil mechanics, geotechnical and geoenvironmental engineering, petroleum engineering, and agricultural engineering. In particular, the imaging of the pore space in porous media has contributed to numerical simulations for single-phase and multiphase flows or contaminant transport through the pore structure as three-dimensional image data. These obtained results are affected by the pore diameter; therefore, it is necessary to verify the image preprocessing for the image analysis and to validate the pore diameters obtained from the CT image data. Moreover, it is meaningful to produce the physical parameters in a representative element volume (REV) and significant to define the dimension of the REV. This paper describes the underlying method of image processing and analysis and discusses the physical properties of Toyoura sand for the verification of the image analysis based on the definition of the REV. On the basis of the obtained verification results, a pore-diameter analysis can be conducted and validated by a comparison with the experimental work and image analysis. The pore diameter is deduced from Young-Laplace's law and a water retention test for the drainage process. The results from previous study and perforated-pore diameter originally proposed in this study, called the voxel-percolation method (VPM), are compared in this paper. In addition, the limitations of the REV, the definition of the pore diameter, and the effectiveness of the VPM for an assessment of the pore diameter are discussed.

Subband/Transform MATLAB Functions For Processing Images

NASA Technical Reports Server (NTRS)

Glover, D.

1995-01-01

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

Radar data processing and analysis

NASA Technical Reports Server (NTRS)

Ausherman, D.; Larson, R.; Liskow, C.

1976-01-01

Digitized four-channel radar images corresponding to particular areas from the Phoenix and Huntington test sites were generated in conjunction with prior experiments performed to collect X- and L-band synthetic aperture radar imagery of these two areas. The methods for generating this imagery are documented. A secondary objective was the investigation of digital processing techniques for extraction of information from the multiband radar image data. Following the digitization, the remaining resources permitted a preliminary machine analysis to be performed on portions of the radar image data. The results, although necessarily limited, are reported.

Image processing and analysis using neural networks for optometry area

NASA Astrophysics Data System (ADS)

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

2002-11-01

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

Validating a Geographical Image Retrieval System.

ERIC Educational Resources Information Center

Zhu, Bin; Chen, Hsinchun

2000-01-01

Summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. Describes an experiment to validate the performance of this image retrieval system against that of human subjects by examining similarity analysis…

Optical Fourier diffractometry applied to degraded bone structure recognition

NASA Astrophysics Data System (ADS)

Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej

1993-09-01

Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.

PubMed

Markiewicz, Pawel J; Ehrhardt, Matthias J; Erlandsson, Kjell; Noonan, Philip J; Barnes, Anna; Schott, Jonathan M; Atkinson, David; Arridge, Simon R; Hutton, Brian F; Ourselin, Sebastien

2018-01-01

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

The Hico Image Processing System: A Web-Accessible Hyperspectral Remote Sensing Toolbox

NASA Astrophysics Data System (ADS)

Harris, A. T., III; Goodman, J.; Justice, B.

2014-12-01

As the quantity of Earth-observation data increases, the use-case for hosting analytical tools in geospatial data centers becomes increasingly attractive. To address this need, HySpeed Computing and Exelis VIS have developed the HICO Image Processing System, a prototype cloud computing system that provides online, on-demand, scalable remote sensing image processing capabilities. The system provides a mechanism for delivering sophisticated image processing analytics and data visualization tools into the hands of a global user community, who will only need a browser and internet connection to perform analysis. Functionality of the HICO Image Processing System is demonstrated using imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), an imaging spectrometer located on the International Space Station (ISS) that is optimized for acquisition of aquatic targets. Example applications include a collection of coastal remote sensing algorithms that are directed at deriving critical information on water and habitat characteristics of our vulnerable coastal environment. The project leverages the ENVI Services Engine as the framework for all image processing tasks, and can readily accommodate the rapid integration of new algorithms, datasets and processing tools.

Digital management and regulatory submission of medical images from clinical trials: role and benefits of the core laboratory

NASA Astrophysics Data System (ADS)

Robbins, William L.; Conklin, James J.

1995-10-01

Medical images (angiography, CT, MRI, nuclear medicine, ultrasound, x ray) play an increasingly important role in the clinical development and regulatory review process for pharmaceuticals and medical devices. Since medical images are increasingly acquired and archived digitally, or are readily digitized from film, they can be visualized, processed and analyzed in a variety of ways using digital image processing and display technology. Moreover, with image-based data management and data visualization tools, medical images can be electronically organized and submitted to the U.S. Food and Drug Administration (FDA) for review. The collection, processing, analysis, archival, and submission of medical images in a digital format versus an analog (film-based) format presents both challenges and opportunities for the clinical and regulatory information management specialist. The medical imaging 'core laboratory' is an important resource for clinical trials and regulatory submissions involving medical imaging data. Use of digital imaging technology within a core laboratory can increase efficiency and decrease overall costs in the image data management and regulatory review process.

Processing and analysis of commercial satellite image data of the nuclear accident near Chernobyl, U. S. S. R

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

Sadowski, F.G.; Covington, S.J.

1987-01-01

Advanced digital processing techniques were applied to Landsat-5 Thematic Mapper (TM) data and SPOT high-resolution visible (HRV) panchromatic data to maximize the utility of images of a nuclear power plant emergency at Chernobyl in the Soviet Ukraine. The results of the data processing and analysis illustrate the spectral and spatial capabilities of the two sensor systems and provide information about the severity and duration of the events occurring at the power plant site.

Tracker: Image-Processing and Object-Tracking System Developed

NASA Technical Reports Server (NTRS)

Klimek, Robert B.; Wright, Theodore W.

1999-01-01

Tracker is an object-tracking and image-processing program designed and developed at the NASA Lewis Research Center to help with the analysis of images generated by microgravity combustion and fluid physics experiments. Experiments are often recorded on film or videotape for analysis later. Tracker automates the process of examining each frame of the recorded experiment, performing image-processing operations to bring out the desired detail, and recording the positions of the objects of interest. It can load sequences of images from disk files or acquire images (via a frame grabber) from film transports, videotape, laser disks, or a live camera. Tracker controls the image source to automatically advance to the next frame. It can employ a large array of image-processing operations to enhance the detail of the acquired images and can analyze an arbitrarily large number of objects simultaneously. Several different tracking algorithms are available, including conventional threshold and correlation-based techniques, and more esoteric procedures such as "snake" tracking and automated recognition of character data in the image. The Tracker software was written to be operated by researchers, thus every attempt was made to make the software as user friendly and self-explanatory as possible. Tracker is used by most of the microgravity combustion and fluid physics experiments performed by Lewis, and by visiting researchers. This includes experiments performed on the space shuttles, Mir, sounding rockets, zero-g research airplanes, drop towers, and ground-based laboratories. This software automates the analysis of the flame or liquid s physical parameters such as position, velocity, acceleration, size, shape, intensity characteristics, color, and centroid, as well as a number of other measurements. It can perform these operations on multiple objects simultaneously. Another key feature of Tracker is that it performs optical character recognition (OCR). This feature is useful in extracting numerical instrumentation data that are embedded in images. All the results are saved in files for further data reduction and graphing. There are currently three Tracking Systems (workstations) operating near the laboratories and offices of Lewis Microgravity Science Division researchers. These systems are used independently by students, scientists, and university-based principal investigators. The researchers bring their tapes or films to the workstation and perform the tracking analysis. The resultant data files generated by the tracking process can then be analyzed on the spot, although most of the time researchers prefer to transfer them via the network to their offices for further analysis or plotting. In addition, many researchers have installed Tracker on computers in their office for desktop analysis of digital image sequences, which can be digitized by the Tracking System or some other means. Tracker has not only provided a capability to efficiently and automatically analyze large volumes of data, saving many hours of tedious work, but has also provided new capabilities to extract valuable information and phenomena that was heretofore undetected and unexploited.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-01-01

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

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

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

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

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

Imaging of breast cancer with mid- and long-wave infrared camera.

PubMed

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

2008-01-01

In this novel study the breasts of 15 women with palpable breast cancer were preoperatively imaged with three technically different infrared (IR) cameras - micro bolometer (MB), quantum well (QWIP) and photo voltaic (PV) - to compare their ability to differentiate breast cancer from normal tissue. The IR images were processed, the data for frequency analysis were collected from dynamic IR images by pixel-based analysis and from each image selectively windowed regional analysis was carried out, based on angiogenesis and nitric oxide production of cancer tissue causing vasomotor and cardiogenic frequency differences compared to normal tissue. Our results show that the GaAs QWIP camera and the InSb PV camera demonstrate the frequency difference between normal and cancerous breast tissue; the PV camera more clearly. With selected image processing operations more detailed frequency analyses could be applied to the suspicious area. The MB camera was not suitable for tissue differentiation, as the difference between noise and effective signal was unsatisfactory.

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

DOE PAGES

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

2017-12-01

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

Image analysis and mathematical modelling for the supervision of the dough fermentation process

NASA Astrophysics Data System (ADS)

Zettel, Viktoria; Paquet-Durand, Olivier; Hecker, Florian; Hitzmann, Bernd

2016-10-01

The fermentation (proof) process of dough is one of the quality-determining steps in the production of baking goods. Beside the fluffiness, whose fundaments are built during fermentation, the flavour of the final product is influenced very much during this production stage. However, until now no on-line measurement system is available, which can supervise this important process step. In this investigation the potential of an image analysis system is evaluated, that enables the determination of the volume of fermented dough pieces. The camera is moving around the fermenting pieces and collects images from the objects by means of different angles (360° range). Using image analysis algorithms the volume increase of individual dough pieces is determined. Based on a detailed mathematical description of the volume increase, which based on the Bernoulli equation, carbon dioxide production rate of yeast cells and the diffusion processes of carbon dioxide, the fermentation process is supervised. Important process parameters, like the carbon dioxide production rate of the yeast cells and the dough viscosity can be estimated just after 300 s of proofing. The mean percentage error for forecasting the further evolution of the relative volume of the dough pieces is just 2.3 %. Therefore, a forecast of the further evolution can be performed and used for fault detection.

Colony image acquisition and genetic segmentation algorithm and colony analyses

NASA Astrophysics Data System (ADS)

Wang, W. X.

2012-01-01

Colony anaysis is used in a large number of engineerings such as food, dairy, beverages, hygiene, environmental monitoring, water, toxicology, sterility testing. In order to reduce laboring and increase analysis acuracy, many researchers and developers have made efforts for image analysis systems. The main problems in the systems are image acquisition, image segmentation and image analysis. In this paper, to acquire colony images with good quality, an illumination box was constructed. In the box, the distances between lights and dishe, camra lens and lights, and camera lens and dishe are adjusted optimally. In image segmentation, It is based on a genetic approach that allow one to consider the segmentation problem as a global optimization,. After image pre-processing and image segmentation, the colony analyses are perfomed. The colony image analysis consists of (1) basic colony parameter measurements; (2) colony size analysis; (3) colony shape analysis; and (4) colony surface measurements. All the above visual colony parameters can be selected and combined together, used to make a new engineeing parameters. The colony analysis can be applied into different applications.

BioImageXD: an open, general-purpose and high-throughput image-processing platform.

PubMed

Kankaanpää, Pasi; Paavolainen, Lassi; Tiitta, Silja; Karjalainen, Mikko; Päivärinne, Joacim; Nieminen, Jonna; Marjomäki, Varpu; Heino, Jyrki; White, Daniel J

2012-06-28

BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes. We demonstrate its performance in a study of integrin clustering in response to selected inhibitors.

Visual grading analysis of digital neonatal chest phantom X-ray images: Impact of detector type, dose and image processing on image quality.

PubMed

Smet, M H; Breysem, L; Mussen, E; Bosmans, H; Marshall, N W; Cockmartin, L

2018-07-01

To evaluate the impact of digital detector, dose level and post-processing on neonatal chest phantom X-ray image quality (IQ). A neonatal phantom was imaged using four different detectors: a CR powder phosphor (PIP), a CR needle phosphor (NIP) and two wireless CsI DR detectors (DXD and DRX). Five different dose levels were studied for each detector and two post-processing algorithms evaluated for each vendor. Three paediatric radiologists scored the images using European quality criteria plus additional questions on vascular lines, noise and disease simulation. Visual grading characteristics and ordinal regression statistics were used to evaluate the effect of detector type, post-processing and dose on VGA score (VGAS). No significant differences were found between the NIP, DXD and CRX detectors (p>0.05) whereas the PIP detector had significantly lower VGAS (p< 0.0001). Processing did not influence VGAS (p=0.819). Increasing dose resulted in significantly higher VGAS (p<0.0001). Visual grading analysis (VGA) identified a detector air kerma/image (DAK/image) of ~2.4 μGy as an ideal working point for NIP, DXD and DRX detectors. VGAS tracked IQ differences between detectors and dose levels but not image post-processing changes. VGA showed a DAK/image value above which perceived IQ did not improve, potentially useful for commissioning. • A VGA study detects IQ differences between detectors and dose levels. • The NIP detector matched the VGAS of the CsI DR detectors. • VGA data are useful in setting initial detector air kerma level. • Differences in NNPS were consistent with changes in VGAS.

Retinal imaging and image analysis.

PubMed

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

2010-01-01

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.

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:22275207

NeuronMetrics: Software for Semi-Automated Processing of Cultured-Neuron Images

PubMed Central

Narro, Martha L.; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L.

2007-01-01

Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics™ for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch-number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of ~60 2D images is 1.0–2.5 hours, from a folder of images to a table of numeric data. NeuronMetrics’ output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery. PMID:17270152

NeuronMetrics: software for semi-automated processing of cultured neuron images.

PubMed

Narro, Martha L; Yang, Fan; Kraft, Robert; Wenk, Carola; Efrat, Alon; Restifo, Linda L

2007-03-23

Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.

Study of ionospheric anomalies due to impact of typhoon using Principal Component Analysis and image processing

NASA Astrophysics Data System (ADS)

LIN, JYH-WOEI

2012-08-01

Principal Component Analysis (PCA) and image processing are used to determine Total Electron Content (TEC) anomalies in the F-layer of the ionosphere relating to Typhoon Nakri for 29 May, 2008 (UTC). PCA and image processing are applied to the global ionospheric map (GIM) with transforms conducted for the time period 12:00-14:00 UT on 29 May, 2008 when the wind was most intense. Results show that at a height of approximately 150-200 km the TEC anomaly is highly localized; however, it becomes more intense and widespread with height. Potential causes of these results are discussed with emphasis given to acoustic gravity waves caused by wind force.

Information granules in image histogram analysis.

PubMed

Wieclawek, Wojciech

2018-04-01

A concept of granular computing employed in intensity-based image enhancement is discussed. First, a weighted granular computing idea is introduced. Then, the implementation of this term in the image processing area is presented. Finally, multidimensional granular histogram analysis is introduced. The proposed approach is dedicated to digital images, especially to medical images acquired by Computed Tomography (CT). As the histogram equalization approach, this method is based on image histogram analysis. Yet, unlike the histogram equalization technique, it works on a selected range of the pixel intensity and is controlled by two parameters. Performance is tested on anonymous clinical CT series. Copyright © 2017 Elsevier Ltd. All rights reserved.

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

PubMed Central

Perez-Sanz, Fernando; Navarro, Pedro J

2017-01-01

Abstract The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion. PMID:29048559

MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data.

PubMed

He, Jiuming; Huang, Luojiao; Tian, Runtao; Li, Tiegang; Sun, Chenglong; Song, Xiaowei; Lv, Yiwei; Luo, Zhigang; Li, Xin; Abliz, Zeper

2018-07-26

Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Error analysis of filtering operations in pixel-duplicated images of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mehrubeoglu, Mehrube; McLauchlan, Lifford

2010-08-01

In this paper, diabetic retinopathy is chosen for a sample target image to demonstrate the effectiveness of image enlargement through pixel duplication in identifying regions of interest. Pixel duplication is presented as a simpler alternative to data interpolation techniques for detecting small structures in the images. A comparative analysis is performed on different image processing schemes applied to both original and pixel-duplicated images. Structures of interest are detected and and classification parameters optimized for minimum false positive detection in the original and enlarged retinal pictures. The error analysis demonstrates the advantages as well as shortcomings of pixel duplication in image enhancement when spatial averaging operations (smoothing filters) are also applied.

A manual for microcomputer image analysis

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

Rich, P.M.; Ranken, D.M.; George, J.S.

1989-12-01

This manual is intended to serve three basic purposes: as a primer in microcomputer image analysis theory and techniques, as a guide to the use of IMAGE{copyright}, a public domain microcomputer program for image analysis, and as a stimulus to encourage programmers to develop microcomputer software suited for scientific use. Topics discussed include the principals of image processing and analysis, use of standard video for input and display, spatial measurement techniques, and the future of microcomputer image analysis. A complete reference guide that lists the commands for IMAGE is provided. IMAGE includes capabilities for digitization, input and output of images,more » hardware display lookup table control, editing, edge detection, histogram calculation, measurement along lines and curves, measurement of areas, examination of intensity values, output of analytical results, conversion between raster and vector formats, and region movement and rescaling. The control structure of IMAGE emphasizes efficiency, precision of measurement, and scientific utility. 18 refs., 18 figs., 2 tabs.« less

Color line scan camera technology and machine vision: requirements to consider

NASA Astrophysics Data System (ADS)

Paernaenen, Pekka H. T.

1997-08-01

Color machine vision has shown a dynamic uptrend in use within the past few years as the introduction of new cameras and scanner technologies itself underscores. In the future, the movement from monochrome imaging to color will hasten, as machine vision system users demand more knowledge about their product stream. As color has come to the machine vision, certain requirements for the equipment used to digitize color images are needed. Color machine vision needs not only a good color separation but also a high dynamic range and a good linear response from the camera used. Good dynamic range and linear response is necessary for color machine vision. The importance of these features becomes even more important when the image is converted to another color space. There is always lost some information when converting integer data to another form. Traditionally the color image processing has been much slower technique than the gray level image processing due to the three times greater data amount per image. The same has applied for the three times more memory needed. The advancements in computers, memory and processing units has made it possible to handle even large color images today cost efficiently. In some cases he image analysis in color images can in fact even be easier and faster than with a similar gray level image because of more information per pixel. Color machine vision sets new requirements for lighting, too. High intensity and white color light is required in order to acquire good images for further image processing or analysis. New development in lighting technology is bringing eventually solutions for color imaging.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Study on polarization image methods in turbid medium

NASA Astrophysics Data System (ADS)

Fu, Qiang; Mo, Chunhe; Liu, Boyu; Duan, Jin; Zhang, Su; Zhu, Yong

2014-11-01

Polarization imaging detection technology in addition to the traditional imaging information, also can get polarization multi-dimensional information, thus improve the probability of target detection and recognition.Image fusion in turbid medium target polarization image research, is helpful to obtain high quality images. Based on visible light wavelength of light wavelength of laser polarization imaging, through the rotation Angle of polaroid get corresponding linear polarized light intensity, respectively to obtain the concentration range from 5% to 10% of turbid medium target stocks of polarization parameters, introduces the processing of image fusion technology, main research on access to the polarization of the image by using different polarization image fusion methods for image processing, discusses several kinds of turbid medium has superior performance of polarization image fusion method, and gives the treatment effect and analysis of data tables. Then use pixel level, feature level and decision level fusion algorithm on three levels of information fusion, DOLP polarization image fusion, the results show that: with the increase of the polarization Angle, polarization image will be more and more fuzzy, quality worse and worse. Than a single fused image contrast of the image be improved obviously, the finally analysis on reasons of the increase the image contrast and polarized light.

An image processing and analysis tool for identifying and analysing complex plant root systems in 3D soil using non-destructive analysis: Root1.

PubMed

Flavel, Richard J; Guppy, Chris N; Rabbi, Sheikh M R; Young, Iain M

2017-01-01

The objective of this study was to develop a flexible and free image processing and analysis solution, based on the Public Domain ImageJ platform, for the segmentation and analysis of complex biological plant root systems in soil from x-ray tomography 3D images. Contrasting root architectures from wheat, barley and chickpea root systems were grown in soil and scanned using a high resolution micro-tomography system. A macro (Root1) was developed that reliably identified with good to high accuracy complex root systems (10% overestimation for chickpea, 1% underestimation for wheat, 8% underestimation for barley) and provided analysis of root length and angle. In-built flexibility allowed the user interaction to (a) amend any aspect of the macro to account for specific user preferences, and (b) take account of computational limitations of the platform. The platform is free, flexible and accurate in analysing root system metrics.

Normalized Polarization Ratios for the Analysis of Cell Polarity

PubMed Central

Shimoni, Raz; Pham, Kim; Yassin, Mohammed; Ludford-Menting, Mandy J.; Gu, Min; Russell, Sarah M.

2014-01-01

The quantification and analysis of molecular localization in living cells is increasingly important for elucidating biological pathways, and new methods are rapidly emerging. The quantification of cell polarity has generated much interest recently, and ratiometric analysis of fluorescence microscopy images provides one means to quantify cell polarity. However, detection of fluorescence, and the ratiometric measurement, is likely to be sensitive to acquisition settings and image processing parameters. Using imaging of EGFP-expressing cells and computer simulations of variations in fluorescence ratios, we characterized the dependence of ratiometric measurements on processing parameters. This analysis showed that image settings alter polarization measurements; and that clustered localization is more susceptible to artifacts than homogeneous localization. To correct for such inconsistencies, we developed and validated a method for choosing the most appropriate analysis settings, and for incorporating internal controls to ensure fidelity of polarity measurements. This approach is applicable to testing polarity in all cells where the axis of polarity is known. PMID:24963926

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

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

An open data mining framework for the analysis of medical images: application on obstructive nephropathy microscopy images.

PubMed

Doukas, Charalampos; Goudas, Theodosis; Fischer, Simon; Mierswa, Ingo; Chatziioannou, Aristotle; Maglogiannis, Ilias

2010-01-01

This paper presents an open image-mining framework that provides access to tools and methods for the characterization of medical images. Several image processing and feature extraction operators have been implemented and exposed through Web Services. Rapid-Miner, an open source data mining system has been utilized for applying classification operators and creating the essential processing workflows. The proposed framework has been applied for the detection of salient objects in Obstructive Nephropathy microscopy images. Initial classification results are quite promising demonstrating the feasibility of automated characterization of kidney biopsy images.

Developing Matlab scripts for image analysis and quality assessment

NASA Astrophysics Data System (ADS)

Vaiopoulos, A. D.

2011-11-01

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

Cryo-imaging of fluorescently labeled single cells in a mouse

NASA Astrophysics Data System (ADS)

Steyer, Grant J.; Roy, Debashish; Salvado, Olivier; Stone, Meredith E.; Wilson, David L.

2009-02-01

We developed a cryo-imaging system to provide single-cell detection of fluorescently labeled cells in mouse, with particular applicability to stem cells and metastatic cancer. The Case cryoimaging system consists of a fluorescence microscope, robotic imaging positioner, customized cryostat, PC-based control system, and visualization/analysis software. The system alternates between sectioning (10-40 μm) and imaging, collecting color brightfield and fluorescent blockface image volumes >60GB. In mouse experiments, we imaged quantum-dot labeled stem cells, GFP-labeled cancer and stem cells, and cell-size fluorescent microspheres. To remove subsurface fluorescence, we used a simplified model of light-tissue interaction whereby the next image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing entropy of subtracted images. Tissue specific attenuation parameters were found [uT : heart (267 +/- 47.6 μm), liver (218 +/- 27.1 μm), brain (161 +/- 27.4 μm)] to be within the range of estimates in the literature. "Next image" processing removed subsurface fluorescence equally well across multiple tissues (brain, kidney, liver, adipose tissue, etc.), and analysis of 200 microsphere images in the brain gave 97+/-2% reduction of subsurface fluorescence. Fluorescent signals were determined to arise from single cells based upon geometric and integrated intensity measurements. Next image processing greatly improved axial resolution, enabled high quality 3D volume renderings, and improved enumeration of single cells with connected component analysis by up to 24%. Analysis of image volumes identified metastatic cancer sites, found homing of stem cells to injury sites, and showed microsphere distribution correlated with blood flow patterns. We developed and evaluated cryo-imaging to provide single-cell detection of fluorescently labeled cells in mouse. Our cryo-imaging system provides extreme (>60GB), micron-scale, fluorescence, and bright field image data. Here we describe our image preprocessing, analysis, and visualization techniques. Processing improves axial resolution, reduces subsurface fluorescence by 97%, and enables single cell detection and counting. High quality 3D volume renderings enable us to evaluate cell distribution patterns. Applications include the myriad of biomedical experiments using fluorescent reporter gene and exogenous fluorophore labeling of cells in applications such as stem cell regenerative medicine, cancer, tissue engineering, etc.

A Versatile Image Processor For Digital Diagnostic Imaging And Its Application In Computed Radiography

NASA Astrophysics Data System (ADS)

Blume, H.; Alexandru, R.; Applegate, R.; Giordano, T.; Kamiya, K.; Kresina, R.

1986-06-01

In a digital diagnostic imaging department, the majority of operations for handling and processing of images can be grouped into a small set of basic operations, such as image data buffering and storage, image processing and analysis, image display, image data transmission and image data compression. These operations occur in almost all nodes of the diagnostic imaging communications network of the department. An image processor architecture was developed in which each of these functions has been mapped into hardware and software modules. The modular approach has advantages in terms of economics, service, expandability and upgradeability. The architectural design is based on the principles of hierarchical functionality, distributed and parallel processing and aims at real time response. Parallel processing and real time response is facilitated in part by a dual bus system: a VME control bus and a high speed image data bus, consisting of 8 independent parallel 16-bit busses, capable of handling combined up to 144 MBytes/sec. The presented image processor is versatile enough to meet the video rate processing needs of digital subtraction angiography, the large pixel matrix processing requirements of static projection radiography, or the broad range of manipulation and display needs of a multi-modality diagnostic work station. Several hardware modules are described in detail. For illustrating the capabilities of the image processor, processed 2000 x 2000 pixel computed radiographs are shown and estimated computation times for executing the processing opera-tions are presented.

Identification, regression and validation of an image processing degradation model to assess the effects of aeromechanical turbulence due to installation aircraft

NASA Astrophysics Data System (ADS)

Miccoli, M.; Usai, A.; Tafuto, A.; Albertoni, A.; Togna, F.

2016-10-01

The propagation environment around airborne platforms may significantly degrade the performance of Electro-Optical (EO) self-protection systems installed onboard. To ensure the sufficient level of protection, it is necessary to understand that are the best sensors/effectors installation positions to guarantee that the aeromechanical turbulence, generated by the engine exhausts and the rotor downwash, does not interfere with the imaging systems normal operations. Since the radiation-propagation-in-turbulence is a hardly predictable process, it was proposed a high-level approach in which, instead of studying the medium under turbulence, the turbulence effects on the imaging systems processing are assessed by means of an equivalent statistical model representation, allowing a definition of a Turbulence index to classify different level of turbulence intensities. Hence, a general measurement methodology for the degradation of the imaging systems performance in turbulence conditions was developed. The analysis of the performance degradation started by evaluating the effects of turbulences with a given index on the image processing chain (i.e., thresholding, blob analysis). The processing in turbulence (PIT) index is then derived by combining the effects of the given turbulence on the different image processing primitive functions. By evaluating the corresponding PIT index for a sufficient number of testing directions, it is possible to map the performance degradation around the aircraft installation for a generic imaging system, and to identify the best installation position for sensors/effectors composing the EO self-protection suite.

Computer vision applications for coronagraphic optical alignment and image processing.

PubMed

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

2013-05-10

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

AstroImageJ: Image Processing and Photometric Extraction for Ultra-precise Astronomical Light Curves

NASA Astrophysics Data System (ADS)

Collins, Karen A.; Kielkopf, John F.; Stassun, Keivan G.; Hessman, Frederic V.

2017-02-01

ImageJ is a graphical user interface (GUI) driven, public domain, Java-based, software package for general image processing traditionally used mainly in life sciences fields. The image processing capabilities of ImageJ are useful and extendable to other scientific fields. Here we present AstroImageJ (AIJ), which provides an astronomy specific image display environment and tools for astronomy specific image calibration and data reduction. Although AIJ maintains the general purpose image processing capabilities of ImageJ, AIJ is streamlined for time-series differential photometry, light curve detrending and fitting, and light curve plotting, especially for applications requiring ultra-precise light curves (e.g., exoplanet transits). AIJ reads and writes standard Flexible Image Transport System (FITS) files, as well as other common image formats, provides FITS header viewing and editing, and is World Coordinate System aware, including an automated interface to the astrometry.net web portal for plate solving images. AIJ provides research grade image calibration and analysis tools with a GUI driven approach, and easily installed cross-platform compatibility. It enables new users, even at the level of undergraduate student, high school student, or amateur astronomer, to quickly start processing, modeling, and plotting astronomical image data with one tightly integrated software package.

Portable dynamic fundus instrument

NASA Technical Reports Server (NTRS)

Taylor, Gerald R. (Inventor); Meehan, Richard T. (Inventor); Hunter, Norwood R. (Inventor); Caputo, Michael P. (Inventor); Gibson, C. Robert (Inventor)

1992-01-01

A portable diagnostic image analysis instrument is disclosed for retinal funduscopy in which an eye fundus image is optically processed by a lens system to a charge coupled device (CCD) which produces recordable and viewable output data and is simultaneously viewable on an electronic view finder. The fundus image is processed to develop a representation of the vessel or vessels from the output data.

Microfluidic electrochemical device and process for chemical imaging and electrochemical analysis at the electrode-liquid interface in-situ

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

Yu, Xiao-Ying; Liu, Bingwen; Yang, Li

2016-03-01

A microfluidic electrochemical device and process are detailed that provide chemical imaging and electrochemical analysis under vacuum at the surface of the electrode-sample or electrode-liquid interface in-situ. The electrochemical device allows investigation of various surface layers including diffuse layers at selected depths populated with, e.g., adsorbed molecules in which chemical transformation in electrolyte solutions occurs.

Automatic high throughput empty ISO container verification

NASA Astrophysics Data System (ADS)

Chalmers, Alex

2007-04-01

Encouraging results are presented for the automatic analysis of radiographic images of a continuous stream of ISO containers to confirm they are truly empty. A series of image processing algorithms are described that process real-time data acquired during the actual inspection of each container and assigns each to one of the classes "empty", "not empty" or "suspect threat". This research is one step towards achieving fully automated analysis of cargo containers.

Dictionary-based image reconstruction for superresolution in integrated circuit imaging.

PubMed

Cilingiroglu, T Berkin; Uyar, Aydan; Tuysuzoglu, Ahmet; Karl, W Clem; Konrad, Janusz; Goldberg, Bennett B; Ünlü, M Selim

2015-06-01

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.

An Integrative Object-Based Image Analysis Workflow for Uav Images

NASA Astrophysics Data System (ADS)

Yu, Huai; Yan, Tianheng; Yang, Wen; Zheng, Hong

2016-06-01

In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake demonstrate the effectiveness and efficiency of our proposed method.

Fiji: an open-source platform for biological-image analysis.

PubMed

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

2012-06-28

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 new 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.

Neutron imaging data processing using the Mantid framework

NASA Astrophysics Data System (ADS)

Pouzols, Federico M.; Draper, Nicholas; Nagella, Sri; Yang, Erica; Sajid, Ahmed; Ross, Derek; Ritchie, Brian; Hill, John; Burca, Genoveva; Minniti, Triestino; Moreton-Smith, Christopher; Kockelmann, Winfried

2016-09-01

Several imaging instruments are currently being constructed at neutron sources around the world. The Mantid software project provides an extensible framework that supports high-performance computing for data manipulation, analysis and visualisation of scientific data. At ISIS, IMAT (Imaging and Materials Science & Engineering) will offer unique time-of-flight neutron imaging techniques which impose several software requirements to control the data reduction and analysis. Here we outline the extensions currently being added to Mantid to provide specific support for neutron imaging requirements.

Dedicated tool to assess the impact of a rhetorical task on human body temperature.

PubMed

Koprowski, Robert; Wilczyński, Sławomir; Martowska, Katarzyna; Gołuch, Dominik; Wrocławska-Warchala, Emilia

2017-10-01

Functional infrared thermal imaging is a method widely used in medicine, including analysis of the mechanisms related to the effect of emotions on physiological processes. The article shows how the body temperature may change during stress associated with performing a rhetorical task and proposes new parameters useful for dynamic thermal imaging measurements MATERIALS AND METHODS: 29 healthy male subjects were examined. They were given a rhetorical task that induced stress. Analysis and processing of collected body temperature data in a spatial resolution of 256×512pixels and a temperature resolution of 0.1°C enabled to show the dynamics of temperature changes. This analysis was preceded by dedicated image analysis and processing methods RESULTS: The presented dedicated algorithm for image analysis and processing allows for fully automated, reproducible and quantitative assessment of temperature changes and time constants in a sequence of thermal images of the patient. When performing the rhetorical task, the temperature rose by 0.47±0.19°C in 72.41% of the subjects, including 20.69% in whom the temperature decreased by 0.49±0.14°C after 237±141s. For 20.69% of the subjects only a drop in temperature was registered. For the remaining 6.89% of the cases, no temperature changes were registered CONCLUSIONS: The performance of the rhetorical task by the subjects causes body temperature changes. The ambiguous temperature response to the given stress factor indicates the complex mechanisms responsible for regulating stressful situations. Stress associated with the examination itself induces body temperature changes. These changes should always be taken into account in the analysis of infrared data. Copyright © 2017 Elsevier B.V. All rights reserved.

Uterus segmentation in dynamic MRI using LBP texture descriptors

NASA Astrophysics Data System (ADS)

Namias, R.; Bellemare, M.-E.; Rahim, M.; Pirró, N.

2014-03-01

Pelvic floor disorders cover pathologies of which physiopathology is not well understood. However cases get prevalent with an ageing population. Within the context of a project aiming at modelization of the dynamics of pelvic organs, we have developed an efficient segmentation process. It aims at alleviating the radiologist with a tedious one by one image analysis. From a first contour delineating the uterus-vagina set, the organ border is tracked along a dynamic mri sequence. The process combines movement prediction, local intensity and texture analysis and active contour geometry control. Movement prediction allows a contour intitialization for next image in the sequence. Intensity analysis provides image-based local contour detection enhanced by local binary pattern (lbp) texture descriptors. Geometry control prohibits self intersections and smoothes the contour. Results show the efficiency of the method with images produced in clinical routine.

Dependence of quantitative accuracy of CT perfusion imaging on system parameters

NASA Astrophysics Data System (ADS)

Li, Ke; Chen, Guang-Hong

2017-03-01

Deconvolution is a popular method to calculate parametric perfusion parameters from four dimensional CT perfusion (CTP) source images. During the deconvolution process, the four dimensional space is squeezed into three-dimensional space by removing the temporal dimension, and a prior knowledge is often used to suppress noise associated with the process. These additional complexities confound the understanding about deconvolution-based CTP imaging system and how its quantitative accuracy depends on parameters and sub-operations involved in the image formation process. Meanwhile, there has been a strong clinical need in answering this question, as physicians often rely heavily on the quantitative values of perfusion parameters to make diagnostic decisions, particularly during an emergent clinical situation (e.g. diagnosis of acute ischemic stroke). The purpose of this work was to develop a theoretical framework that quantitatively relates the quantification accuracy of parametric perfusion parameters with CTP acquisition and post-processing parameters. This goal was achieved with the help of a cascaded systems analysis for deconvolution-based CTP imaging systems. Based on the cascaded systems analysis, the quantitative relationship between regularization strength, source image noise, arterial input function, and the quantification accuracy of perfusion parameters was established. The theory could potentially be used to guide developments of CTP imaging technology for better quantification accuracy and lower radiation dose.

Analysis of simulated image sequences from sensors for restricted-visibility operations

NASA Technical Reports Server (NTRS)

Kasturi, Rangachar

1991-01-01

A real time model of the visible output from a 94 GHz sensor, based on a radiometric simulation of the sensor, was developed. A sequence of images as seen from an aircraft as it approaches for landing was simulated using this model. Thirty frames from this sequence of 200 x 200 pixel images were analyzed to identify and track objects in the image using the Cantata image processing package within the visual programming environment provided by the Khoros software system. The image analysis operations are described.

Autofluorescence endoscopy with "real-time" digital image processing in differential diagnostics of selected benign and malignant lesions in the oesophagus.

PubMed

Sieroń-Stołtny, Karolina; Kwiatek, Sebastian; Latos, Wojciech; Kawczyk-Krupka, Aleksandra; Cieślar, Grzegorz; Stanek, Agata; Ziaja, Damian; Bugaj, Andrzej M; Sieroń, Aleksander

2012-03-01

Oesophageal papilloma and Barrett's oesophagus are benign lesions known as risk factors of carcinoma in the oesophagus. Therefore, it is important to diagnose these early changes before neoplastic transformation. Autofluorescence endoscopy is a fast and non-invasive method of imaging of tissues based on the natural fluorescence of endogenous fluorophores. The aim of this study was to prove the diagnostic utility of autofluorescence endoscopy with digital image processing in histological diagnosis of endoscopic findings in the upper digestive tract, primarily in the imaging of oesophageal papilloma. During the retrospective analysis of about 200 endoscopic procedures in the upper digestive tract, 67 cases of benign, precancerous or cancerous changes were found. White light endoscopy (WLE) image, single-channel (red or green) autofluorescence images, as well as green and red fluorescence intensities in two modal fluorescence image and red-to-green (R/G) ratio (Numerical Colour Value, NCV) were correlated with histopathologic results. The NCV analysis in autofluorescence imaging (AFI) showed increased R/G ratio in cancerous changes in 96% vs. 85% in WLE. Simultaneous analysis with digital image processing allowed us to diagnose suspicious tissue as cancerous in all of cases. Barrett's metaplasia was confirmed in 90% vs. 79% (AFI vs. WLE), and 98% in imaging with digital image processing. In benign lesions, WLE allowed us to exclude tissue as malignant in 85%. Using autofluorescence endoscopy R/G ratio was increased in only 10% of benign changes causing the picture to be interpreted as suspicious, but when both methods were used together, 97.5% were cases excluded as malignancies. Mean R/G ratios were estimated to be 2.5 in cancers, 1.25 in Barrett's metaplasia and 0.75 in benign changes and were statistically significant (p=0.04). Autofluorescence imaging is a sensitive method to diagnose precancerous and cancerous early stages of the diseases located in oesophagus. Especially in two-modal imaging including white light endoscopy, autofluorescence imaging with digital image processing seems to be a useful modality of early diagnostics. Also in observation of papilloma changes, it facilitates differentiation between neoplastic and benign lesions and more accurate estimation of the risk of potential malignancy. Copyright © 2011 Elsevier B.V. All rights reserved.

IQM: An Extensible and Portable Open Source Application for Image and Signal Analysis in Java

PubMed Central

Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

2015-01-01

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM’s image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis. PMID:25612319

IQM: an extensible and portable open source application for image and signal analysis in Java.

PubMed

Kainz, Philipp; Mayrhofer-Reinhartshuber, Michael; Ahammer, Helmut

2015-01-01

Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM's image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

On-road anomaly detection by multimodal sensor analysis and multimedia processing

NASA Astrophysics Data System (ADS)

Orhan, Fatih; Eren, P. E.

2014-03-01

The use of smartphones in Intelligent Transportation Systems is gaining popularity, yet many challenges exist in developing functional applications. Due to the dynamic nature of transportation, vehicular social applications face complexities such as developing robust sensor management, performing signal and image processing tasks, and sharing information among users. This study utilizes a multimodal sensor analysis framework which enables the analysis of sensors in multimodal aspect. It also provides plugin-based analyzing interfaces to develop sensor and image processing based applications, and connects its users via a centralized application as well as to social networks to facilitate communication and socialization. With the usage of this framework, an on-road anomaly detector is being developed and tested. The detector utilizes the sensors of a mobile device and is able to identify anomalies such as hard brake, pothole crossing, and speed bump crossing. Upon such detection, the video portion containing the anomaly is automatically extracted in order to enable further image processing analysis. The detection results are shared on a central portal application for online traffic condition monitoring.

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.

Advances in medical image computing.

PubMed

Tolxdorff, T; Deserno, T M; Handels, H; Meinzer, H-P

2009-01-01

Medical image computing has become a key technology in high-tech applications in medicine and an ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on application level. Despite this progress there are still big challenges to meet in order to establish image processing routinely in health care. In this issue, selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing. The winners of scientific awards of the German Conference on Medical Image Processing (BVM) 2008 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, seven excellent papers were selected to describe important aspects of recent advances in the field of medical image processing. The selected papers give an impression of the breadth and heterogeneity of new developments. New methods for improved image segmentation, non-linear image registration and modeling of organs are presented together with applications of image analysis methods in different medical disciplines. Furthermore, state-of-the-art tools and techniques to support the development and evaluation of medical image processing systems in practice are described. The selected articles describe different aspects of the intense development in medical image computing. The image processing methods presented enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

MOPEX: a software package for astronomical image processing and visualization

NASA Astrophysics Data System (ADS)

Makovoz, David; Roby, Trey; Khan, Iffat; Booth, Hartley

2006-06-01

We present MOPEX - a software package for astronomical image processing and display. The package is a combination of command-line driven image processing software written in C/C++ with a Java-based GUI. The main image processing capabilities include creating mosaic images, image registration, background matching, point source extraction, as well as a number of minor image processing tasks. The combination of the image processing and display capabilities allows for much more intuitive and efficient way of performing image processing. The GUI allows for the control over the image processing and display to be closely intertwined. Parameter setting, validation, and specific processing options are entered by the user through a set of intuitive dialog boxes. Visualization feeds back into further processing by providing a prompt feedback of the processing results. The GUI also allows for further analysis by accessing and displaying data from existing image and catalog servers using a virtual observatory approach. Even though originally designed for the Spitzer Space Telescope mission, a lot of functionalities are of general usefulness and can be used for working with existing astronomical data and for new missions. The software used in the package has undergone intensive testing and benefited greatly from effective software reuse. The visualization part has been used for observation planning for both the Spitzer and Herschel Space Telescopes as part the tool Spot. The visualization capabilities of Spot have been enhanced and integrated with the image processing functionality of the command-line driven MOPEX. The image processing software is used in the Spitzer automated pipeline processing, which has been in operation for nearly 3 years. The image processing capabilities have also been tested in off-line processing by numerous astronomers at various institutions around the world. The package is multi-platform and includes automatic update capabilities. The software package has been developed by a small group of software developers and scientists at the Spitzer Science Center. It is available for distribution at the Spitzer Science Center web page.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery.

PubMed

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W; Chen, Zhuo Georgia; Fei, Baowei

2015-01-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

NASA Astrophysics Data System (ADS)

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma; Muller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

2015-12-01

Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

iSBatch: a batch-processing platform for data analysis and exploration of live-cell single-molecule microscopy images and other hierarchical datasets.

PubMed

Caldas, Victor E A; Punter, Christiaan M; Ghodke, Harshad; Robinson, Andrew; van Oijen, Antoine M

2015-10-01

Recent technical advances have made it possible to visualize single molecules inside live cells. Microscopes with single-molecule sensitivity enable the imaging of low-abundance proteins, allowing for a quantitative characterization of molecular properties. Such data sets contain information on a wide spectrum of important molecular properties, with different aspects highlighted in different imaging strategies. The time-lapsed acquisition of images provides information on protein dynamics over long time scales, giving insight into expression dynamics and localization properties. Rapid burst imaging reveals properties of individual molecules in real-time, informing on their diffusion characteristics, binding dynamics and stoichiometries within complexes. This richness of information, however, adds significant complexity to analysis protocols. In general, large datasets of images must be collected and processed in order to produce statistically robust results and identify rare events. More importantly, as live-cell single-molecule measurements remain on the cutting edge of imaging, few protocols for analysis have been established and thus analysis strategies often need to be explored for each individual scenario. Existing analysis packages are geared towards either single-cell imaging data or in vitro single-molecule data and typically operate with highly specific algorithms developed for particular situations. Our tool, iSBatch, instead allows users to exploit the inherent flexibility of the popular open-source package ImageJ, providing a hierarchical framework in which existing plugins or custom macros may be executed over entire datasets or portions thereof. This strategy affords users freedom to explore new analysis protocols within large imaging datasets, while maintaining hierarchical relationships between experiments, samples, fields of view, cells, and individual molecules.

Quantitative analysis of cardiovascular MR images.

PubMed

van der Geest, R J; de Roos, A; van der Wall, E E; Reiber, J H

1997-06-01

The diagnosis of cardiovascular disease requires the precise assessment of both morphology and function. Nearly all aspects of cardiovascular function and flow can be quantified nowadays with fast magnetic resonance (MR) imaging techniques. Conventional and breath-hold cine MR imaging allow the precise and highly reproducible assessment of global and regional left ventricular function. During the same examination, velocity encoded cine (VEC) MR imaging provides measurements of blood flow in the heart and great vessels. Quantitative image analysis often still relies on manual tracing of contours in the images. Reliable automated or semi-automated image analysis software would be very helpful to overcome the limitations associated with the manual and tedious processing of the images. Recent progress in MR imaging of the coronary arteries and myocardial perfusion imaging with contrast media, along with the further development of faster imaging sequences, suggest that MR imaging could evolve into a single technique ('one stop shop') for the evaluation of many aspects of heart disease. As a result, it is very likely that the need for automated image segmentation and analysis software algorithms will further increase. In this paper the developments directed towards the automated image analysis and semi-automated contour detection for cardiovascular MR imaging are presented.

Modeling fixation locations using spatial point processes.

PubMed

Barthelmé, Simon; Trukenbrod, Hans; Engbert, Ralf; Wichmann, Felix

2013-10-01

Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation.

MO-PIS-Exhibit Hall-01: Tools for TG-142 Linac Imaging QA I

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

Clements, M; Wiesmeyer, M

2014-06-15

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands-on” information about the equipment and software systems that we use in our clinics. The therapy topic this year is solutions for TG-142 recommendations for linear accelerator imaging QA. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors. Automated Imaging QA for TG-142 with RIT Presentation Time: 2:45 – 3:15 PM This presentation will discuss software tools for automated imaging QA and phantom analysis for TG-142.more » All modalities used in radiation oncology will be discussed, including CBCT, planar kV imaging, planar MV imaging, and imaging and treatment coordinate coincidence. Vendor supplied phantoms as well as a variety of third-party phantoms will be shown, along with appropriate analyses, proper phantom setup procedures and scanning settings, and a discussion of image quality metrics. Tools for process automation will be discussed which include: RIT Cognition (machine learning for phantom image identification), RIT Cerberus (automated file system monitoring and searching), and RunQueueC (batch processing of multiple images). In addition to phantom analysis, tools for statistical tracking, trending, and reporting will be discussed. This discussion will include an introduction to statistical process control, a valuable tool in analyzing data and determining appropriate tolerances. An Introduction to TG-142 Imaging QA Using Standard Imaging Products Presentation Time: 3:15 – 3:45 PM Medical Physicists want to understand the logic behind TG-142 Imaging QA. What is often missing is a firm understanding of the connections between the EPID and OBI phantom imaging, the software “algorithms” that calculate the QA metrics, the establishment of baselines, and the analysis and interpretation of the results. The goal of our brief presentation will be to establish and solidify these connections. Our talk will be motivated by the Standard Imaging, Inc. phantom and software solutions. We will present and explain each of the image quality metrics in TG-142 in terms of the theory, mathematics, and algorithms used to implement them in the Standard Imaging PIPSpro software. In the process, we will identify the regions of phantom images that are analyzed by each algorithm. We then will discuss the process of the creation of baselines and typical ranges of acceptable values for each imaging quality metric.« less

Software and Algorithms for Biomedical Image Data Processing and Visualization

NASA Technical Reports Server (NTRS)

Talukder, Ashit; Lambert, James; Lam, Raymond

2004-01-01

A new software equipped with novel image processing algorithms and graphical-user-interface (GUI) tools has been designed for automated analysis and processing of large amounts of biomedical image data. The software, called PlaqTrak, has been specifically used for analysis of plaque on teeth of patients. New algorithms have been developed and implemented to segment teeth of interest from surrounding gum, and a real-time image-based morphing procedure is used to automatically overlay a grid onto each segmented tooth. Pattern recognition methods are used to classify plaque from surrounding gum and enamel, while ignoring glare effects due to the reflection of camera light and ambient light from enamel regions. The PlaqTrak system integrates these components into a single software suite with an easy-to-use GUI (see Figure 1) that allows users to do an end-to-end run of a patient s record, including tooth segmentation of all teeth, grid morphing of each segmented tooth, and plaque classification of each tooth image. The automated and accurate processing of the captured images to segment each tooth [see Figure 2(a)] and then detect plaque on a tooth-by-tooth basis is a critical component of the PlaqTrak system to do clinical trials and analysis with minimal human intervention. These features offer distinct advantages over other competing systems that analyze groups of teeth or synthetic teeth. PlaqTrak divides each segmented tooth into eight regions using an advanced graphics morphing procedure [see results on a chipped tooth in Figure 2(b)], and a pattern recognition classifier is then used to locate plaque [red regions in Figure 2(d)] and enamel regions. The morphing allows analysis within regions of teeth, thereby facilitating detailed statistical analysis such as the amount of plaque present on the biting surfaces on teeth. This software system is applicable to a host of biomedical applications, such as cell analysis and life detection, or robotic applications, such as product inspection or assembly of parts in space and industry.

Reduce Fluid Experiment System: Flight data from the IML-1 Mission

NASA Technical Reports Server (NTRS)

Workman, Gary L.; Harper, Sabrina

1995-01-01

Processing and data reduction of holographic images from the International Microgravity Laboratory 1 (IML-1) presents some interesting challenges in determining the effects of microgravity on crystal growth processes. Use of several processing techniques, including the Computerized Holographic Image Processing System and the Software Development Package (SDP-151) will provide fundamental information for holographic and schlieren analysis of the space flight data.

Quantitative fractography by digital image processing: NIH Image macro tools for stereo pair analysis and 3-D reconstruction.

PubMed

Hein, L R

2001-10-01

A set of NIH Image macro programs was developed to make qualitative and quantitative analyses from digital stereo pictures produced by scanning electron microscopes. These tools were designed for image alignment, anaglyph representation, animation, reconstruction of true elevation surfaces, reconstruction of elevation profiles, true-scale elevation mapping and, for the quantitative approach, surface area and roughness calculations. Limitations on time processing, scanning techniques and programming concepts are also discussed.

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

Cancer.gov

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

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.

PubMed

Shirvaikar, Mukul; Lagadapati, Yamuna; Dong, Xuanliang

2017-03-01

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ ( h ), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h . Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O ( n 2 ) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor.

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures

PubMed Central

Shirvaikar, Mukul; Lagadapati, Yamuna; Dong, Xuanliang

2016-01-01

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O (n2) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor. PMID:28428829

Single molecule image formation, reconstruction and processing: introduction.

PubMed

Ashok, Amit; Piestun, Rafael; Stallinga, Sjoerd

2016-07-01

The ability to image at the single molecule scale has revolutionized research in molecular biology. This feature issue presents a collection of articles that provides new insights into the fundamental limits of single molecule imaging and reports novel techniques for image formation and analysis.

Comparison of existing digital image analysis systems for the analysis of Thematic Mapper data

NASA Technical Reports Server (NTRS)

Likens, W. C.; Wrigley, R. C.

1984-01-01

Most existing image analysis systems were designed with the Landsat Multi-Spectral Scanner in mind, leaving open the question of whether or not these systems could adequately process Thematic Mapper data. In this report, both hardware and software systems have been evaluated for compatibility with TM data. Lack of spectral analysis capability was not found to be a problem, though techniques for spatial filtering and texture varied. Computer processing speed and data storage of currently existing mini-computer based systems may be less than adequate. Upgrading to more powerful hardware may be required for many TM applications.

Chromosome Analysis

NASA Technical Reports Server (NTRS)

1998-01-01

Perceptive Scientific Instruments, Inc., provides the foundation for the Powergene line of chromosome analysis and molecular genetic instrumentation. This product employs image processing technology from NASA's Jet Propulsion Laboratory and image enhancement techniques from Johnson Space Center. Originally developed to send pictures back to earth from space probes, digital imaging techniques have been developed and refined for use in a variety of medical applications, including diagnosis of disease.

Image Analysis and Modeling

DTIC Science & Technology

1975-08-01

image analysis and processing tasks such as information extraction, image enhancement and restoration, coding, etc. The ultimate objective of this research is to form a basis for the development of technology relevant to military applications of machine extraction of information from aircraft and satellite imagery of the earth’s surface. This report discusses research activities during the three month period February 1 - April 30,

An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis.

PubMed

Stewart, Ethan L; Hagerty, Christina H; Mikaberidze, Alexey; Mundt, Christopher C; Zhong, Ziming; McDonald, Bruce A

2016-07-01

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

Assesment on the performance of electrode arrays using image processing technique

NASA Astrophysics Data System (ADS)

Usman, N.; Khiruddin, A.; Nawawi, Mohd

2017-08-01

Interpreting inverted resistivity section is time consuming, tedious and requires other sources of information to be relevant geologically. Image processing technique was used in order to perform post inversion processing which make geophysical data interpretation easier. The inverted data sets were imported into the PCI Geomatica 9.0.1 for further processing. The data sets were clipped and merged together in order to match the coordinates of the three layers and permit pixel to pixel analysis. Dipole-dipole array is more sensitive to resistivity variation with depth in comparison with Werner-Schlumberger and pole-dipole. Image processing serves as good post-inversion tool in geophysical data processing.

CT Dose Optimization in Pediatric Radiology: A Multiyear Effort to Preserve the Benefits of Imaging While Reducing the Risks.

PubMed

Greenwood, Taylor J; Lopez-Costa, Rodrigo I; Rhoades, Patrick D; Ramírez-Giraldo, Juan C; Starr, Matthew; Street, Mandie; Duncan, James; McKinstry, Robert C

2015-01-01

The marked increase in radiation exposure from medical imaging, especially in children, has caused considerable alarm and spurred efforts to preserve the benefits but reduce the risks of imaging. Applying the principles of the Image Gently campaign, data-driven process and quality improvement techniques such as process mapping and flowcharting, cause-and-effect diagrams, Pareto analysis, statistical process control (control charts), failure mode and effects analysis, "lean" or Six Sigma methodology, and closed feedback loops led to a multiyear program that has reduced overall computed tomographic (CT) examination volume by more than fourfold and concurrently decreased radiation exposure per CT study without compromising diagnostic utility. This systematic approach involving education, streamlining access to magnetic resonance imaging and ultrasonography, auditing with comparison with benchmarks, applying modern CT technology, and revising CT protocols has led to a more than twofold reduction in CT radiation exposure between 2005 and 2012 for patients at the authors' institution while maintaining diagnostic utility. (©)RSNA, 2015.

The Use of Multidimensional Image-Based Analysis to Accurately Monitor Cell Growth in 3D Bioreactor Culture

PubMed Central

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells. PMID:22028809

The use of multidimensional image-based analysis to accurately monitor cell growth in 3D bioreactor culture.

PubMed

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells.

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

PubMed

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

2009-04-01

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

Digital processing of Mariner 9 television data.

NASA Technical Reports Server (NTRS)

Green, W. B.; Seidman, J. B.

1973-01-01

The digital image processing performed by the Image Processing Laboratory (IPL) at JPL in support of the Mariner 9 mission is summarized. The support is divided into the general categories of image decalibration (the removal of photometric and geometric distortions from returned imagery), computer cartographic projections in support of mapping activities, and adaptive experimenter support (flexible support to provide qualitative digital enhancements and quantitative data reduction of returned imagery). Among the tasks performed were the production of maximum discriminability versions of several hundred frames to support generation of a geodetic control net for Mars, and special enhancements supporting analysis of Phobos and Deimos images.

Advances in interpretation of subsurface processes with time-lapse electrical imaging

USGS Publications Warehouse

Singha, Kaminit; Day-Lewis, Frederick D.; Johnson, Tim B.; Slater, Lee D.

2015-01-01

Electrical geophysical methods, including electrical resistivity, time-domain induced polarization, and complex resistivity, have become commonly used to image the near subsurface. Here, we outline their utility for time-lapse imaging of hydrological, geochemical, and biogeochemical processes, focusing on new instrumentation, processing, and analysis techniques specific to monitoring. We review data collection procedures, parameters measured, and petrophysical relationships and then outline the state of the science with respect to inversion methodologies, including coupled inversion. We conclude by highlighting recent research focused on innovative applications of time-lapse imaging in hydrology, biology, ecology, and geochemistry, among other areas of interest.

Advances in interpretation of subsurface processes with time-lapse electrical imaging

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

Singha, Kamini; Day-Lewis, Frederick D.; Johnson, Timothy C.

2015-03-15

Electrical geophysical methods, including electrical resistivity, time-domain induced polarization, and complex resistivity, have become commonly used to image the near subsurface. Here, we outline their utility for time-lapse imaging of hydrological, geochemical, and biogeochemical processes, focusing on new instrumentation, processing, and analysis techniques specific to monitoring. We review data collection procedures, parameters measured, and petrophysical relationships and then outline the state of the science with respect to inversion methodologies, including coupled inversion. We conclude by highlighting recent research focused on innovative applications of time-lapse imaging in hydrology, biology, ecology, and geochemistry, among other areas of interest.

An image analysis of TLC patterns for quality control of saffron based on soil salinity effect: A strategy for data (pre)-processing.

PubMed

Sereshti, Hassan; Poursorkh, Zahra; Aliakbarzadeh, Ghazaleh; Zarre, Shahin; Ataolahi, Sahar

2018-01-15

Quality of saffron, a valuable food additive, could considerably affect the consumers' health. In this work, a novel preprocessing strategy for image analysis of saffron thin layer chromatographic (TLC) patterns was introduced. This includes performing a series of image pre-processing techniques on TLC images such as compression, inversion, elimination of general baseline (using asymmetric least squares (AsLS)), removing spots shift and concavity (by correlation optimization warping (COW)), and finally conversion to RGB chromatograms. Subsequently, an unsupervised multivariate data analysis including principal component analysis (PCA) and k-means clustering was utilized to investigate the soil salinity effect, as a cultivation parameter, on saffron TLC patterns. This method was used as a rapid and simple technique to obtain the chemical fingerprints of saffron TLC images. Finally, the separated TLC spots were chemically identified using high-performance liquid chromatography-diode array detection (HPLC-DAD). Accordingly, the saffron quality from different areas of Iran was evaluated and classified. Copyright © 2017 Elsevier Ltd. All rights reserved.

Noise Gating Solar Images

NASA Astrophysics Data System (ADS)

DeForest, Craig; Seaton, Daniel B.; Darnell, John A.

2017-08-01

I present and demonstrate a new, general purpose post-processing technique, "3D noise gating", that can reduce image noise by an order of magnitude or more without effective loss of spatial or temporal resolution in typical solar applications.Nearly all scientific images are, ultimately, limited by noise. Noise can be direct Poisson "shot noise" from photon counting effects, or introduced by other means such as detector read noise. Noise is typically represented as a random variable (perhaps with location- or image-dependent characteristics) that is sampled once per pixel or once per resolution element of an image sequence. Noise limits many aspects of image analysis, including photometry, spatiotemporal resolution, feature identification, morphology extraction, and background modeling and separation.Identifying and separating noise from image signal is difficult. The common practice of blurring in space and/or time works because most image "signal" is concentrated in the low Fourier components of an image, while noise is evenly distributed. Blurring in space and/or time attenuates the high spatial and temporal frequencies, reducing noise at the expense of also attenuating image detail. Noise-gating exploits the same property -- "coherence" -- that we use to identify features in images, to separate image features from noise.Processing image sequences through 3-D noise gating results in spectacular (more than 10x) improvements in signal-to-noise ratio, while not blurring bright, resolved features in either space or time. This improves most types of image analysis, including feature identification, time sequence extraction, absolute and relative photometry (including differential emission measure analysis), feature tracking, computer vision, correlation tracking, background modeling, cross-scale analysis, visual display/presentation, and image compression.I will introduce noise gating, describe the method, and show examples from several instruments (including SDO/AIA , SDO/HMI, STEREO/SECCHI, and GOES-R/SUVI) that explore the benefits and limits of the technique.

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

PubMed

Perez-Sanz, Fernando; Navarro, Pedro J; Egea-Cortines, Marcos

2017-11-01

The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion. © The Author 2017. Published by Oxford University Press.

Determination of differential cross sections and kinetic energy release of co-products from central sliced images in photo-initiated dynamic processes.

PubMed

Chen, Kuo-mei; Chen, Yu-wei

2011-04-07

For photo-initiated inelastic and reactive collisions, dynamic information can be extracted from central sliced images of state-selected Newton spheres of product species. An analysis framework has been established to determine differential cross sections and the kinetic energy release of co-products from experimental images. When one of the reactants exhibits a high recoil speed in a photo-initiated dynamic process, the present theory can be employed to analyze central sliced images from ion imaging or three-dimensional sliced fluorescence imaging experiments. It is demonstrated that the differential cross section of a scattering process can be determined from the central sliced image by a double Legendre moment analysis, for either a fixed or continuously distributed recoil speeds in the center-of-mass reference frame. Simultaneous equations which lead to the determination of the kinetic energy release of co-products can be established from the second-order Legendre moment of the experimental image, as soon as the differential cross section is extracted. The intensity distribution of the central sliced image, along with its outer and inner ring sizes, provide all the clues to decipher the differential cross section and the kinetic energy release of co-products.

Impervious surfaces mapping using high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Shirmeen, Tahmina

In recent years, impervious surfaces have emerged not only as an indicator of the degree of urbanization, but also as an indicator of environmental quality. As impervious surface area increases, storm water runoff increases in velocity, quantity, temperature and pollution load. Any of these attributes can contribute to the degradation of natural hydrology and water quality. Various image processing techniques have been used to identify the impervious surfaces, however, most of the existing impervious surface mapping tools used moderate resolution imagery. In this project, the potential of standard image processing techniques to generate impervious surface data for change detection analysis using high-resolution satellite imagery was evaluated. The city of Oxford, MS was selected as the study site for this project. Standard image processing techniques, including Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA), a combination of NDVI and PCA, and image classification algorithms, were used to generate impervious surfaces from multispectral IKONOS and QuickBird imagery acquired in both leaf-on and leaf-off conditions. Accuracy assessments were performed, using truth data generated by manual classification, with Kappa statistics and Zonal statistics to select the most appropriate image processing techniques for impervious surface mapping. The performance of selected image processing techniques was enhanced by incorporating Soil Brightness Index (SBI) and Greenness Index (GI) derived from Tasseled Cap Transformed (TCT) IKONOS and QuickBird imagery. A time series of impervious surfaces for the time frame between 2001 and 2007 was made using the refined image processing techniques to analyze the changes in IS in Oxford. It was found that NDVI and the combined NDVI--PCA methods are the most suitable image processing techniques for mapping impervious surfaces in leaf-off and leaf-on conditions respectively, using high resolution multispectral imagery. It was also found that IS data generated by these techniques can be refined by removing the conflicting dry soil patches using SBI and GI obtained from TCT of the same imagery used for IS data generation. The change detection analysis of the IS time series shows that Oxford experienced the major changes in IS from the year 2001 to 2004 and 2006 to 2007.

Sequential processing of quantitative phase images for the study of cell behaviour in real-time digital holographic microscopy.

PubMed

Zikmund, T; Kvasnica, L; Týč, M; Křížová, A; Colláková, J; Chmelík, R

2014-11-01

Transmitted light holographic microscopy is particularly used for quantitative phase imaging of transparent microscopic objects such as living cells. The study of the cell is based on extraction of the dynamic data on cell behaviour from the time-lapse sequence of the phase images. However, the phase images are affected by the phase aberrations that make the analysis particularly difficult. This is because the phase deformation is prone to change during long-term experiments. Here, we present a novel algorithm for sequential processing of living cells phase images in a time-lapse sequence. The algorithm compensates for the deformation of a phase image using weighted least-squares surface fitting. Moreover, it identifies and segments the individual cells in the phase image. All these procedures are performed automatically and applied immediately after obtaining every single phase image. This property of the algorithm is important for real-time cell quantitative phase imaging and instantaneous control of the course of the experiment by playback of the recorded sequence up to actual time. Such operator's intervention is a forerunner of process automation derived from image analysis. The efficiency of the propounded algorithm is demonstrated on images of rat fibrosarcoma cells using an off-axis holographic microscope. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

SSME propellant path leak detection real-time

NASA Technical Reports Server (NTRS)

Crawford, R. A.; Smith, L. M.

1994-01-01

Included are four documents that outline the technical aspects of the research performed on NASA Grant NAG8-140: 'A System for Sequential Step Detection with Application to Video Image Processing'; 'Leak Detection from the SSME Using Sequential Image Processing'; 'Digital Image Processor Specifications for Real-Time SSME Leak Detection'; and 'A Color Change Detection System for Video Signals with Applications to Spectral Analysis of Rocket Engine Plumes'.

Image processing of angiograms: A pilot study

NASA Technical Reports Server (NTRS)

Larsen, L. E.; Evans, R. A.; Roehm, J. O., Jr.

1974-01-01

The technology transfer application this report describes is the result of a pilot study of image-processing methods applied to the image enhancement, coding, and analysis of arteriograms. Angiography is a subspecialty of radiology that employs the introduction of media with high X-ray absorption into arteries in order to study vessel pathology as well as to infer disease of the organs supplied by the vessel in question.

Dynamic feature analysis for Voyager at the Image Processing Laboratory

NASA Technical Reports Server (NTRS)

Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

1978-01-01

Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

Analysis of x-ray tomography data of an extruded low density styrenic foam: an image analysis study

NASA Astrophysics Data System (ADS)

Lin, Jui-Ching; Heeschen, William

2016-10-01

Extruded styrenic foams are low density foams that are widely used for thermal insulation. It is difficult to precisely characterize the structure of the cells in low density foams by traditional cross-section viewing due to the frailty of the walls of the cells. X-ray computed tomography (CT) is a non-destructive, three dimensional structure characterization technique that has great potential for structure characterization of styrenic foams. Unfortunately the intrinsic artifacts of the data and the artifacts generated during image reconstruction are often comparable in size and shape to the thin walls of the foam, making robust and reliable analysis of cell sizes challenging. We explored three different image processing methods to clean up artifacts in the reconstructed images, thus allowing quantitative three dimensional determination of cell size in a low density styrenic foam. Three image processing approaches - an intensity based approach, an intensity variance based approach, and a machine learning based approach - are explored in this study, and the machine learning image feature classification method was shown to be the best. Individual cells are segmented within the images after the images were cleaned up using the three different methods and the cell sizes are measured and compared in the study. Although the collected data with the image analysis methods together did not yield enough measurements for a good statistic of the measurement of cell sizes, the problem can be resolved by measuring multiple samples or increasing imaging field of view.

Low-level processing for real-time image analysis

NASA Technical Reports Server (NTRS)

Eskenazi, R.; Wilf, J. M.

1979-01-01

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given.

Traffic analysis and control using image processing

NASA Astrophysics Data System (ADS)

Senthilkumar, K.; Ellappan, Vijayan; Arun, A. R.

2017-11-01

This paper shows the work on traffic analysis and control till date. It shows an approach to regulate traffic the use of image processing and MATLAB systems. This concept uses computational images that are to be compared with original images of the street taken in order to determine the traffic level percentage and set the timing for the traffic signal accordingly which are used to reduce the traffic stoppage on traffic lights. They concept proposes to solve real life scenarios in the streets, thus enriching the traffic lights by adding image receivers like HD cameras and image processors. The input is then imported into MATLAB to be used. as a method for calculating the traffic on roads. Their results would be computed in order to adjust the traffic light timings on a particular street, and also with respect to other similar proposals but with the added value of solving a real, big instance.

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

PubMed Central

Fantuzzo, J. A.; Mirabella, V. R.; Zahn, J. D.

2017-01-01

Abstract Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process. PMID:29218324

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Optical information processing with transformation of the spatial coherence of light

NASA Astrophysics Data System (ADS)

Bykovskii, Yurii A.; Markilov, A. A.; Rodin, V. G.; Starikov, S. N.

1995-10-01

A description is given of systems with spatially incoherent illumination, intended for spectral and correlation analysis, and for the recording of Fourier holograms. These systems make use of transformation of the degree of the spatial coherence of light. The results are given of the processing of images and signals, including those transmitted by a bundle of fibre-optic waveguides both as monochromatic light and as quasimonochromatic radiation from a cathode-ray tube. The feasibility of spatial frequency filtering and of correlation analysis of images with a bipolar impulse response is considered for systems with spatially incoherent illumination where these tasks are performed by double transformation of the spatial coherence of light. A description is given of experimental systems and the results of image processing are reported.

Intershot Analysis of Flows in DIII-D

NASA Astrophysics Data System (ADS)

Meyer, W. H.; Allen, S. L.; Samuell, C. M.; Howard, J.

2016-10-01

Analysis of the DIII-D flow diagnostic data require demodulation of interference images, and inversion of the resultant line integrated emissivity and flow (phase) images. Four response matrices are pre-calculated: the emissivity line integral and the line integral of the scalar product of the lines-of-site with the orthogonal unit vectors of parallel flow. Equilibrium data determines the relative weight of the component matrices used in the final flow inversion matrix. Serial processing has been used for the lower divertor viewing flow camera 800x600 pixel image. The full cross section viewing camera will require parallel processing of the 2160x2560 pixel image. We will discuss using a Posix thread pool and a Tesla K40c GPU in the processing of this data. Prepared by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. DOE, Office of Science, Fusion Energy Sciences.

Quantitative analyses for elucidating mechanisms of cell fate commitment in the mouse blastocyst

NASA Astrophysics Data System (ADS)

Saiz, Néstor; Kang, Minjung; Puliafito, Alberto; Schrode, Nadine; Xenopoulos, Panagiotis; Lou, Xinghua; Di Talia, Stefano; Hadjantonakis, Anna-Katerina

2015-03-01

In recent years we have witnessed a shift from qualitative image analysis towards higher resolution, quantitative analyses of imaging data in developmental biology. This shift has been fueled by technological advances in both imaging and analysis software. We have recently developed a tool for accurate, semi-automated nuclear segmentation of imaging data from early mouse embryos and embryonic stem cells. We have applied this software to the study of the first lineage decisions that take place during mouse development and established analysis pipelines for both static and time-lapse imaging experiments. In this paper we summarize the conclusions from these studies to illustrate how quantitative, single-cell level analysis of imaging data can unveil biological processes that cannot be revealed by traditional qualitative studies.

Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes.

PubMed

Klukkert, Marten; Wu, Jian X; Rantanen, Jukka; Carstensen, Jens M; Rades, Thomas; Leopold, Claudia S

2016-07-30

Monitoring of tablet quality attributes in direct vicinity of the production process requires analytical techniques that allow fast, non-destructive, and accurate tablet characterization. The overall objective of this study was to investigate the applicability of multispectral UV imaging as a reliable, rapid technique for estimation of the tablet API content and tablet hardness, as well as determination of tablet intactness and the tablet surface density profile. One of the aims was to establish an image analysis approach based on multivariate image analysis and pattern recognition to evaluate the potential of UV imaging for automatized quality control of tablets with respect to their intactness and surface density profile. Various tablets of different composition and different quality regarding their API content, radial tensile strength, intactness, and surface density profile were prepared using an eccentric as well as a rotary tablet press at compression pressures from 20MPa up to 410MPa. It was found, that UV imaging can provide both, relevant information on chemical and physical tablet attributes. The tablet API content and radial tensile strength could be estimated by UV imaging combined with partial least squares analysis. Furthermore, an image analysis routine was developed and successfully applied to the UV images that provided qualitative information on physical tablet surface properties such as intactness and surface density profiles, as well as quantitative information on variations in the surface density. In conclusion, this study demonstrates that UV imaging combined with image analysis is an effective and non-destructive method to determine chemical and physical quality attributes of tablets and is a promising approach for (near) real-time monitoring of the tablet compaction process and formulation optimization purposes. Copyright © 2015 Elsevier B.V. All rights reserved.

On-line 3-dimensional confocal imaging in vivo.

PubMed

Li, J; Jester, J V; Cavanagh, H D; Black, T D; Petroll, W M

2000-09-01

In vivo confocal microscopy through focusing (CMTF) can provide a 3-D stack of high-resolution corneal images and allows objective measurements of corneal sublayer thickness and backscattering. However, current systems require time-consuming off-line image processing and analysis on multiple software platforms. Furthermore, there is a trade off between the CMTF speed and measurement precision. The purpose of this study was to develop a novel on-line system for in vivo corneal imaging and analysis that overcomes these limitations. A tandem scanning confocal microscope (TSCM) was used for corneal imaging. The TSCM video camera was interfaced directly to a PC image acquisition board to implement real-time digitization. Software was developed to allow in vivo 2-D imaging, CMTF image acquisition, interactive 3-D reconstruction, and analysis of CMTF data to be performed on line in a single user-friendly environment. A procedure was also incorporated to separate the odd/even video fields, thereby doubling the CMTF sampling rate and theoretically improving the precision of CMTF thickness measurements by a factor of two. In vivo corneal examinations of a normal human and a photorefractive keratectomy patient are presented to demonstrate the capabilities of the new system. Improvements in the convenience, speed, and functionality of in vivo CMTF image acquisition, display, and analysis are demonstrated. This is the first full-featured software package designed for in vivo TSCM imaging of the cornea, which performs both 2-D and 3-D image acquisition, display, and processing as well as CMTF analysis. The use of a PC platform and incorporation of easy to use, on line, and interactive features should help to improve the clinical utility of this technology.

Development of a user-friendly system for image processing of electron microscopy by integrating a web browser and PIONE with Eos.

PubMed

Tsukamoto, Takafumi; Yasunaga, Takuo

2014-11-01

Eos (Extensible object-oriented system) is one of the powerful applications for image processing of electron micrographs. In usual cases, Eos works with only character user interfaces (CUI) under the operating systems (OS) such as OS-X or Linux, not user-friendly. Thus, users of Eos need to be expert at image processing of electron micrographs, and have a little knowledge of computer science, as well. However, all the persons who require Eos does not an expert for CUI. Thus we extended Eos to a web system independent of OS with graphical user interfaces (GUI) by integrating web browser.Advantage to use web browser is not only to extend Eos with GUI, but also extend Eos to work under distributed computational environment. Using Ajax (Asynchronous JavaScript and XML) technology, we implemented more comfortable user-interface on web browser. Eos has more than 400 commands related to image processing for electron microscopy, and the usage of each command is different from each other. Since the beginning of development, Eos has managed their user-interface by using the interface definition file of "OptionControlFile" written in CSV (Comma-Separated Value) format, i.e., Each command has "OptionControlFile", which notes information for interface and its usage generation. Developed GUI system called "Zephyr" (Zone for Easy Processing of HYpermedia Resources) also accessed "OptionControlFIle" and produced a web user-interface automatically, because its mechanism is mature and convenient,The basic actions of client side system was implemented properly and can supply auto-generation of web-form, which has functions of execution, image preview, file-uploading to a web server. Thus the system can execute Eos commands with unique options for each commands, and process image analysis. There remain problems of image file format for visualization and workspace for analysis: The image file format information is useful to check whether the input/output file is correct and we also need to provide common workspace for analysis because the client is physically separated from a server. We solved the file format problem by extension of rules of OptionControlFile of Eos. Furthermore, to solve workspace problems, we have developed two type of system. The first system is to use only local environments. The user runs a web server provided by Eos, access to a web client through a web browser, and manipulate the local files with GUI on the web browser. The second system is employing PIONE (Process-rule for Input/Output Negotiation Environment), which is our developing platform that works under heterogenic distributed environment. The users can put their resources, such as microscopic images, text files and so on, into the server-side environment supported by PIONE, and so experts can write PIONE rule definition, which defines a workflow of image processing. PIONE run each image processing on suitable computers, following the defined rule. PIONE has the ability of interactive manipulation, and user is able to try a command with various setting values. In this situation, we contribute to auto-generation of GUI for a PIONE workflow.As advanced functions, we have developed a module to log user actions. The logs include information such as setting values in image processing, procedure of commands and so on. If we use the logs effectively, we can get a lot of advantages. For example, when an expert may discover some know-how of image processing, other users can also share logs including his know-hows and so we may obtain recommendation workflow of image analysis, if we analyze logs. To implement social platform of image processing for electron microscopists, we have developed system infrastructure, as well. © The Author 2014. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

UWGSP7: a real-time optical imaging workstation

NASA Astrophysics Data System (ADS)

Bush, John E.; Kim, Yongmin; Pennington, Stan D.; Alleman, Andrew P.

1995-04-01

With the development of UWGSP7, the University of Washington Image Computing Systems Laboratory has a real-time workstation for continuous-wave (cw) optical reflectance imaging. Recent discoveries in optical science and imaging research have suggested potential practical use of the technology as a medical imaging modality and identified the need for a machine to support these applications in real time. The UWGSP7 system was developed to provide researchers with a high-performance, versatile tool for use in optical imaging experiments with the eventual goal of bringing the technology into clinical use. One of several major applications of cw optical reflectance imaging is tumor imaging which uses a light-absorbing dye that preferentially sequesters in tumor tissue. This property could be used to locate tumors and to identify tumor margins intraoperatively. Cw optical reflectance imaging consists of illumination of a target with a band-limited light source and monitoring the light transmitted by or reflected from the target. While continuously illuminating the target, a control image is acquired and stored. A dye is injected into a subject and a sequence of data images are acquired and processed. The data images are aligned with the control image and then subtracted to obtain a signal representing the change in optical reflectance over time. This signal can be enhanced by digital image processing and displayed in pseudo-color. This type of emerging imaging technique requires a computer system that is versatile and adaptable. The UWGSP7 utilizes a VESA local bus PC as a host computer running the Windows NT operating system and includes ICSL developed add-on boards for image acquisition and processing. The image acquisition board is used to digitize and format the analog signal from the input device into digital frames and to the average frames into images. To accommodate different input devices, the camera interface circuitry is designed in a small mezzanine board that supports the RS-170 standard. The image acquisition board is connected to the image- processing board using a direct connect port which provides a 66 Mbytes/s channel independent of the system bus. The image processing board utilizes the Texas Instruments TMS320C80 Multimedia Video Processor chip. This chip is capable of 2 billion operations per second providing the UWGSP7 with the capability to perform real-time image processing functions like median filtering, convolution and contrast enhancement. This processing power allows interactive analysis of the experiments as compared to current practice of off-line processing and analysis. Due to its flexibility and programmability, the UWGSP7 can be adapted into various research needs in intraoperative optical imaging.

Quantitative Enzymatic and Immunologic Histophotometry of Diseased Human Kid-Ney Tissues Using Tv-Camera and Computer Assisted Image Processing Systems.

NASA Astrophysics Data System (ADS)

Heinert, G.; Mondorf, W.

1982-11-01

High speed image processing was used to analyse morphologic and metabolic characteristics of clinically relevant kidney tissue alterations.Qualitative computer-assisted histophotometry was performed to measure alterations in levels of the enzymes alkaline phosphatase (Ap),alanine aminopeptidase (AAP),g-glutamyltranspepti-dase (GGTP) and A-glucuronidase (B-G1) and AAP and GGTP immunologically determined in prepared renal and cancer tissue sections. A "Mioro-Videomat 2" image analysis system with a "Tessovar" macroscope,a computer-assisted "Axiomat" photomicroscope and an "Interactive Image Analysis System (IBAS)" were employed for analysing changes in enzyme activities determined by changes in absorbance or transmission.Diseased kidney as well as renal neoplastic tissues could be distinguished by significantly (wilcoxon test,p<0,05) decreased enzyme concentrations as compared to those found in normal human kidney tissues.This image analysis techniques might be of potential use in diagnostic and prognostic evaluation of renal cancer and diseased kidney tissues.

Image detection and compression for memory efficient system analysis

NASA Astrophysics Data System (ADS)

Bayraktar, Mustafa

2015-02-01

The advances in digital signal processing have been progressing towards efficient use of memory and processing. Both of these factors can be utilized efficiently by using feasible techniques of image storage by computing the minimum information of image which will enhance computation in later processes. Scale Invariant Feature Transform (SIFT) can be utilized to estimate and retrieve of an image. In computer vision, SIFT can be implemented to recognize the image by comparing its key features from SIFT saved key point descriptors. The main advantage of SIFT is that it doesn't only remove the redundant information from an image but also reduces the key points by matching their orientation and adding them together in different windows of image [1]. Another key property of this approach is that it works on highly contrasted images more efficiently because it`s design is based on collecting key points from the contrast shades of image.

An Optimal Partial Differential Equations-based Stopping Criterion for Medical Image Denoising.

PubMed

Khanian, Maryam; Feizi, Awat; Davari, Ali

2014-01-01

Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

Application of near-infrared image processing in agricultural engineering

NASA Astrophysics Data System (ADS)

Chen, Ming-hong; Zhang, Guo-ping; Xia, Hongxing

2009-07-01

Recently, with development of computer technology, the application field of near-infrared image processing becomes much wider. In this paper the technical characteristic and development of modern NIR imaging and NIR spectroscopy analysis were introduced. It is concluded application and studying of the NIR imaging processing technique in the agricultural engineering in recent years, base on the application principle and developing characteristic of near-infrared image. The NIR imaging would be very useful in the nondestructive external and internal quality inspecting of agricultural products. It is important to detect stored-grain insects by the application of near-infrared spectroscopy. Computer vision detection base on the NIR imaging would be help to manage food logistics. Application of NIR imaging promoted quality management of agricultural products. In the further application research fields of NIR image in the agricultural engineering, Some advices and prospect were put forward.

The application of a novel optical SPM in biomedicine

NASA Astrophysics Data System (ADS)

Li, Yinli; Chen, Haibo; Wu, Shifa; Song, Linfeng; Zhang, Jian

2005-01-01

As an analysis tool, SPM has been broadly used in biomedicine in recent years, such as AFM and SNOM; they are effective instruments in detecting life nanostructures at atomic level. Atomic force and photon scanning tunneling microscope (AF/PSTM) is one of member of SPM, it can be used to obtain sample" optical and atomic fore images at once scanning, these images include the transmissivity image, reflection index image and topography image. This report mainly introduces the application of AF/PSTM in red blood membrane and the effect of different sample dealt with processes on the experiment result. The materials for preparing red cells membrane samples are anticoagulant blood, isotonic phosphatic buffer solution (PBS) and new two times distilled water. The images of AF/PSTM give real expression to the biology samples" fact despite of different sample dealt with processes, which prove that AF/PSTM suits to biology sample imaging. At the same time, the optical images and the topography image of AF/PSTM of the same sample are complementary with each other; this will make AF/PSTM a facile tool to analysis biologic samples" nanostructure. As another sample, this paper gives the application of AF/PSTM in immunoassay, the result shows that AF/PSTM is suit to analysis biologic sample, and it will become a new tool for biomedicine test.

Realization of the FPGA-based reconfigurable computing environment by the example of morphological processing of a grayscale image

NASA Astrophysics Data System (ADS)

Shatravin, V.; Shashev, D. V.

2018-05-01

Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove the efficiency of vision systems in autonomous robotic devices. However, the use of these systems is limited because of the computational and energy resources available in the robot device. The paper describes the results of applying the original approach for image processing on reconfigurable computing environments by the example of morphological operations over grayscale images. This approach is prospective for realizing complex image processing algorithms and real-time image analysis in autonomous robotic devices.

Blind source separation of ex-vivo aorta tissue multispectral images

PubMed Central

Galeano, July; Perez, Sandra; Montoya, Yonatan; Botina, Deivid; Garzón, Johnson

2015-01-01

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method’s performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue. PMID:26137366

Selections from 2017: Image Processing with AstroImageJ

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2017-12-01

Editors note:In these last two weeks of 2017, well be looking at a few selections that we havent yet discussed on AAS Nova from among the most-downloaded paperspublished in AAS journals this year. The usual posting schedule will resume in January.AstroImageJ: Image Processing and Photometric Extraction for Ultra-Precise Astronomical Light CurvesPublished January2017The AIJ image display. A wide range of astronomy specific image display options and image analysis tools are available from the menus, quick access icons, and interactive histogram. [Collins et al. 2017]Main takeaway:AstroImageJ is a new integrated software package presented in a publication led byKaren Collins(Vanderbilt University,Fisk University, andUniversity of Louisville). Itenables new users even at the level of undergraduate student, high school student, or amateur astronomer to quickly start processing, modeling, and plotting astronomical image data.Why its interesting:Science doesnt just happen the momenta telescope captures a picture of a distantobject. Instead, astronomical images must firstbe carefully processed to clean up thedata, and this data must then be systematically analyzed to learn about the objects within it. AstroImageJ as a GUI-driven, easily installed, public-domain tool is a uniquelyaccessible tool for thisprocessing and analysis, allowing even non-specialist users to explore and visualizeastronomical data.Some features ofAstroImageJ:(as reported by Astrobites)Image calibration:generate master flat, dark, and bias framesImage arithmetic:combineimages viasubtraction, addition, division, multiplication, etc.Stack editing:easily perform operations on a series of imagesImage stabilization and image alignment featuresPrecise coordinate converters:calculate Heliocentric and Barycentric Julian DatesWCS coordinates:determine precisely where atelescope was pointed for an image by PlateSolving using Astronomy.netMacro and plugin support:write your own macrosMulti-aperture photometry with interactive light curve fitting:plot light curves of a star in real timeCitationKaren A. Collins et al 2017 AJ 153 77. doi:10.3847/1538-3881/153/2/77

Watch your step! A frustrated total internal reflection approach to forensic footwear imaging

NASA Astrophysics Data System (ADS)

Needham, J. A.; Sharp, J. S.

2016-02-01

Forensic image retrieval and processing are vital tools in the fight against crime e.g. during fingerprint capture. However, despite recent advances in machine vision technology and image processing techniques (and contrary to the claims of popular fiction) forensic image retrieval is still widely being performed using outdated practices involving inkpads and paper. Ongoing changes in government policy, increasing crime rates and the reduction of forensic service budgets increasingly require that evidence be gathered and processed more rapidly and efficiently. A consequence of this is that new, low-cost imaging technologies are required to simultaneously increase the quality and throughput of the processing of evidence. This is particularly true in the burgeoning field of forensic footwear analysis, where images of shoe prints are being used to link individuals to crime scenes. Here we describe one such approach based upon frustrated total internal reflection imaging that can be used to acquire images of regions where shoes contact rigid surfaces.

Watch your step! A frustrated total internal reflection approach to forensic footwear imaging.

PubMed

Needham, J A; Sharp, J S

2016-02-16

Forensic image retrieval and processing are vital tools in the fight against crime e.g. during fingerprint capture. However, despite recent advances in machine vision technology and image processing techniques (and contrary to the claims of popular fiction) forensic image retrieval is still widely being performed using outdated practices involving inkpads and paper. Ongoing changes in government policy, increasing crime rates and the reduction of forensic service budgets increasingly require that evidence be gathered and processed more rapidly and efficiently. A consequence of this is that new, low-cost imaging technologies are required to simultaneously increase the quality and throughput of the processing of evidence. This is particularly true in the burgeoning field of forensic footwear analysis, where images of shoe prints are being used to link individuals to crime scenes. Here we describe one such approach based upon frustrated total internal reflection imaging that can be used to acquire images of regions where shoes contact rigid surfaces.

Image processing and machine learning in the morphological analysis of blood cells.

PubMed

Rodellar, J; Alférez, S; Acevedo, A; Molina, A; Merino, A

2018-05-01

This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies. © 2018 John Wiley & Sons Ltd.

Computer-aided light sheet flow visualization using photogrammetry

NASA Technical Reports Server (NTRS)

Stacy, Kathryn; Severance, Kurt; Childers, Brooks A.

1994-01-01

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and a visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) results, was chosen to interactively display the reconstructed light sheet images with the numerical surface geometry for the model or aircraft under study. The photogrammetric reconstruction technique and the image processing and computer graphics techniques and equipment are described. Results of the computer-aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images with CFD solutions in the same graphics environment is also demonstrated.

Computer-Aided Light Sheet Flow Visualization

NASA Technical Reports Server (NTRS)

Stacy, Kathryn; Severance, Kurt; Childers, Brooks A.

1993-01-01

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) data sets, was chosen to interactively display the reconstructed light sheet images, along with the numerical surface geometry for the model or aircraft under study. A description is provided of the photogrammetric reconstruction technique, and the image processing and computer graphics techniques and equipment. Results of the computer aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images and CFD solutions in the same graphics environment is also demonstrated.

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

Computer-aided light sheet flow visualization

NASA Technical Reports Server (NTRS)

Stacy, Kathryn; Severance, Kurt; Childers, Brooks A.

1993-01-01

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) data sets, was chosen to interactively display the reconstructed light sheet images, along with the numerical surface geometry for the model or aircraft under study. A description is provided of the photogrammetric reconstruction technique, and the image processing and computer graphics techniques and equipment. Results of the computer aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images and CFD solutions in the same graphics environment is also demonstrated.

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

PubMed Central

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

2014-01-01

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

Imaging systems and algorithms to analyze biological samples in real-time using mobile phone microscopy.

PubMed

Shanmugam, Akshaya; Usmani, Mohammad; Mayberry, Addison; Perkins, David L; Holcomb, Daniel E

2018-01-01

Miniaturized imaging devices have pushed the boundaries of point-of-care imaging, but existing mobile-phone-based imaging systems do not exploit the full potential of smart phones. This work demonstrates the use of simple imaging configurations to deliver superior image quality and the ability to handle a wide range of biological samples. Results presented in this work are from analysis of fluorescent beads under fluorescence imaging, as well as helminth eggs and freshwater mussel larvae under white light imaging. To demonstrate versatility of the systems, real time analysis and post-processing results of the sample count and sample size are presented in both still images and videos of flowing samples.

Development of a Simple Image Processing Application that Makes Abdominopelvic Tumor Visible on Positron Emission Tomography/Computed Tomography Image.

PubMed

Pandey, Anil Kumar; Saroha, Kartik; Sharma, Param Dev; Patel, Chetan; Bal, Chandrashekhar; Kumar, Rakesh

2017-01-01

In this study, we have developed a simple image processing application in MATLAB that uses suprathreshold stochastic resonance (SSR) and helps the user to visualize abdominopelvic tumor on the exported prediuretic positron emission tomography/computed tomography (PET/CT) images. A brainstorming session was conducted for requirement analysis for the program. It was decided that program should load the screen captured PET/CT images and then produces output images in a window with a slider control that should enable the user to view the best image that visualizes the tumor, if present. The program was implemented on personal computer using Microsoft Windows and MATLAB R2013b. The program has option for the user to select the input image. For the selected image, it displays output images generated using SSR in a separate window having a slider control. The slider control enables the user to view images and select one which seems to provide the best visualization of the area(s) of interest. The developed application enables the user to select, process, and view output images in the process of utilizing SSR to detect the presence of abdominopelvic tumor on prediuretic PET/CT image.

Impact of remote sensing upon the planning, management and development of water resources. Summary of computers and computer growth trends for hydrologic modeling and the input of ERTS image data processing load

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Loats, H. L., Jr.

1975-01-01

An analysis of current computer usage by major water resources users was made to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era. The analysis showns significant impact due to the utilization and processing of ERTS CCT's data.

NDVI and Panchromatic Image Correlation Using Texture Analysis

DTIC Science & Technology

2010-03-01

6 Figure 5. Spectral reflectance of vegetation and soil from 0.4 to 1.1 mm (From Perry...should help the classification methods to be able to classify kelp. Figure 5. Spectral reflectance of vegetation and soil from 0.4 to 1.1 mm...1988). Image processing software for imaging spectrometry analysis. Remote Sensing of Enviroment , 24: 201–210. Perry, C., & Lautenschlager, L. F

Statistical Signal Models and Algorithms for Image Analysis

DTIC Science & Technology

1984-10-25

In this report, two-dimensional stochastic linear models are used in developing algorithms for image analysis such as classification, segmentation, and object detection in images characterized by textured backgrounds. These models generate two-dimensional random processes as outputs to which statistical inference procedures can naturally be applied. A common thread throughout our algorithms is the interpretation of the inference procedures in terms of linear prediction

Automated daily quality control analysis for mammography in a multi-unit imaging center.

PubMed

Sundell, Veli-Matti; Mäkelä, Teemu; Meaney, Alexander; Kaasalainen, Touko; Savolainen, Sauli

2018-01-01

Background The high requirements for mammography image quality necessitate a systematic quality assurance process. Digital imaging allows automation of the image quality analysis, which can potentially improve repeatability and objectivity compared to a visual evaluation made by the users. Purpose To develop an automatic image quality analysis software for daily mammography quality control in a multi-unit imaging center. Material and Methods An automated image quality analysis software using the discrete wavelet transform and multiresolution analysis was developed for the American College of Radiology accreditation phantom. The software was validated by analyzing 60 randomly selected phantom images from six mammography systems and 20 phantom images with different dose levels from one mammography system. The results were compared to a visual analysis made by four reviewers. Additionally, long-term image quality trends of a full-field digital mammography system and a computed radiography mammography system were investigated. Results The automated software produced feature detection levels comparable to visual analysis. The agreement was good in the case of fibers, while the software detected somewhat more microcalcifications and characteristic masses. Long-term follow-up via a quality assurance web portal demonstrated the feasibility of using the software for monitoring the performance of mammography systems in a multi-unit imaging center. Conclusion Automated image quality analysis enables monitoring the performance of digital mammography systems in an efficient, centralized manner.

Practical considerations of image analysis and quantification of signal transduction IHC staining.

PubMed

Grunkin, Michael; Raundahl, Jakob; Foged, Niels T

2011-01-01

The dramatic increase in computer processing power in combination with the availability of high-quality digital cameras during the last 10 years has fertilized the grounds for quantitative microscopy based on digital image analysis. With the present introduction of robust scanners for whole slide imaging in both research and routine, the benefits of automation and objectivity in the analysis of tissue sections will be even more obvious. For in situ studies of signal transduction, the combination of tissue microarrays, immunohistochemistry, digital imaging, and quantitative image analysis will be central operations. However, immunohistochemistry is a multistep procedure including a lot of technical pitfalls leading to intra- and interlaboratory variability of its outcome. The resulting variations in staining intensity and disruption of original morphology are an extra challenge for the image analysis software, which therefore preferably should be dedicated to the detection and quantification of histomorphometrical end points.

Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.

PubMed

Bashir, Usman; Siddique, Muhammad Musib; Mclean, Emma; Goh, Vicky; Cook, Gary J

2016-09-01

Texture analysis involves the mathematic processing of medical images to derive sets of numeric quantities that measure heterogeneity. Studies on lung cancer have shown that texture analysis may have a role in characterizing tumors and predicting patient outcome. This article outlines the mathematic basis of and the most recent literature on texture analysis in lung cancer imaging. We also describe the challenges facing the clinical implementation of texture analysis. Texture analysis of lung cancer images has been applied successfully to FDG PET and CT scans. Different texture parameters have been shown to be predictive of the nature of disease and of patient outcome. In general, it appears that more heterogeneous tumors on imaging tend to be more aggressive and to be associated with poorer outcomes and that tumor heterogeneity on imaging decreases with treatment. Despite these promising results, there is a large variation in the reported data and strengths of association.

Optimization of oncological {sup 18}F-FDG PET/CT imaging based on a multiparameter analysis

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

Menezes, Vinicius O., E-mail: vinicius@radtec.com.br; Machado, Marcos A. D.; Queiroz, Cleiton C.

2016-02-15

Purpose: This paper describes a method to achieve consistent clinical image quality in {sup 18}F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques. Methods: Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects. Results: The adoption of different schemes for three body mass ranges (<60 kg, 60–90 kg, >90 kg) allows improved image quality with both point spread functionmore » and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice. Conclusions: Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.« less

A forensic science perspective on the role of images in crime investigation and reconstruction.

PubMed

Milliet, Quentin; Delémont, Olivier; Margot, Pierre

2014-12-01

This article presents a global vision of images in forensic science. The proliferation of perspectives on the use of images throughout criminal investigations and the increasing demand for research on this topic seem to demand a forensic science-based analysis. In this study, the definitions of and concepts related to material traces are revisited and applied to images, and a structured approach is used to persuade the scientific community to extend and improve the use of images as traces in criminal investigations. Current research efforts focus on technical issues and evidence assessment. This article provides a sound foundation for rationalising and explaining the processes involved in the production of clues from trace images. For example, the mechanisms through which these visual traces become clues of presence or action are described. An extensive literature review of forensic image analysis emphasises the existing guidelines and knowledge available for answering investigative questions (who, what, where, when and how). However, complementary developments are still necessary to demystify many aspects of image analysis in forensic science, including how to review and select images or use them to reconstruct an event or assist intelligence efforts. The hypothetico-deductive reasoning pathway used to discover unknown elements of an event or crime can also help scientists understand the underlying processes involved in their decision making. An analysis of a single image in an investigative or probative context is used to demonstrate the highly informative potential of images as traces and/or clues. Research efforts should be directed toward formalising the extraction and combination of clues from images. An appropriate methodology is key to expanding the use of images in forensic science. Copyright © 2014 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.

Novel image processing approach to detect malaria

NASA Astrophysics Data System (ADS)

Mas, David; Ferrer, Belen; Cojoc, Dan; Finaurini, Sara; Mico, Vicente; Garcia, Javier; Zalevsky, Zeev

2015-09-01

In this paper we present a novel image processing algorithm providing good preliminary capabilities for in vitro detection of malaria. The proposed concept is based upon analysis of the temporal variation of each pixel. Changes in dark pixels mean that inter cellular activity happened, indicating the presence of the malaria parasite inside the cell. Preliminary experimental results involving analysis of red blood cells being either healthy or infected with malaria parasites, validated the potential benefit of the proposed numerical approach.

Automatic target recognition apparatus and method

DOEpatents

Baumgart, Chris W.; Ciarcia, Christopher A.

2000-01-01

An automatic target recognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.

Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability analysis.

PubMed

Hock, Sabrina; Hasenauer, Jan; Theis, Fabian J

2013-01-01

Diffusion is a key component of many biological processes such as chemotaxis, developmental differentiation and tissue morphogenesis. Since recently, the spatial gradients caused by diffusion can be assessed in-vitro and in-vivo using microscopy based imaging techniques. The resulting time-series of two dimensional, high-resolutions images in combination with mechanistic models enable the quantitative analysis of the underlying mechanisms. However, such a model-based analysis is still challenging due to measurement noise and sparse observations, which result in uncertainties of the model parameters. We introduce a likelihood function for image-based measurements with log-normal distributed noise. Based upon this likelihood function we formulate the maximum likelihood estimation problem, which is solved using PDE-constrained optimization methods. To assess the uncertainty and practical identifiability of the parameters we introduce profile likelihoods for diffusion processes. As proof of concept, we model certain aspects of the guidance of dendritic cells towards lymphatic vessels, an example for haptotaxis. Using a realistic set of artificial measurement data, we estimate the five kinetic parameters of this model and compute profile likelihoods. Our novel approach for the estimation of model parameters from image data as well as the proposed identifiability analysis approach is widely applicable to diffusion processes. The profile likelihood based method provides more rigorous uncertainty bounds in contrast to local approximation methods.

Real time quantitative imaging for semiconductor crystal growth, control and characterization

NASA Technical Reports Server (NTRS)

Wargo, Michael J.

1991-01-01

A quantitative real time image processing system has been developed which can be software-reconfigured for semiconductor processing and characterization tasks. In thermal imager mode, 2D temperature distributions of semiconductor melt surfaces (900-1600 C) can be obtained with temperature and spatial resolutions better than 0.5 C and 0.5 mm, respectively, as demonstrated by analysis of melt surface thermal distributions. Temporal and spatial image processing techniques and multitasking computational capabilities convert such thermal imaging into a multimode sensor for crystal growth control. A second configuration of the image processing engine in conjunction with bright and dark field transmission optics is used to nonintrusively determine the microdistribution of free charge carriers and submicron sized crystalline defects in semiconductors. The IR absorption characteristics of wafers are determined with 10-micron spatial resolution and, after calibration, are converted into charge carrier density.

Parallel Guessing: A Strategy for High-Speed Computation

DTIC Science & Technology

1984-09-19

for using additional hardware to obtain higher processing speed). In this paper we argue that parallel guessing for image analysis is a useful...from a true solution, or the correctness of a guess, can be readily checked. We review image - analysis algorithms having a parallel guessing or

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

Steganalysis based on reducing the differences of image statistical characteristics

NASA Astrophysics Data System (ADS)

Wang, Ran; Niu, Shaozhang; Ping, Xijian; Zhang, Tao

2018-04-01

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

Use of discrete chromatic space to tune the image tone in a color image mosaic

NASA Astrophysics Data System (ADS)

Zhang, Zuxun; Li, Zhijiang; Zhang, Jianqing; Zheng, Li

2003-09-01

Color image process is a very important problem. However, the main approach presently of them is to transfer RGB colour space into another colour space, such as HIS (Hue, Intensity and Saturation). YIQ, LUV and so on. Virutally, it may not be a valid way to process colour airborne image just in one colour space. Because the electromagnetic wave is physically altered in every wave band, while the color image is perceived based on psychology vision. Therefore, it's necessary to propose an approach accord with physical transformation and psychological perception. Then, an analysis on how to use relative colour spaces to process colour airborne photo is discussed and an application on how to tune the image tone in colour airborne image mosaic is introduced. As a practice, a complete approach to perform the mosaic on color airborne images via taking full advantage of relative color spaces is discussed in the application.

A Control System and Streaming DAQ Platform with Image-Based Trigger for X-ray Imaging

NASA Astrophysics Data System (ADS)

Stevanovic, Uros; Caselle, Michele; Cecilia, Angelica; Chilingaryan, Suren; Farago, Tomas; Gasilov, Sergey; Herth, Armin; Kopmann, Andreas; Vogelgesang, Matthias; Balzer, Matthias; Baumbach, Tilo; Weber, Marc

2015-06-01

High-speed X-ray imaging applications play a crucial role for non-destructive investigations of the dynamics in material science and biology. On-line data analysis is necessary for quality assurance and data-driven feedback, leading to a more efficient use of a beam time and increased data quality. In this article we present a smart camera platform with embedded Field Programmable Gate Array (FPGA) processing that is able to stream and process data continuously in real-time. The setup consists of a Complementary Metal-Oxide-Semiconductor (CMOS) sensor, an FPGA readout card, and a readout computer. It is seamlessly integrated in a new custom experiment control system called Concert that provides a more efficient way of operating a beamline by integrating device control, experiment process control, and data analysis. The potential of the embedded processing is demonstrated by implementing an image-based trigger. It records the temporal evolution of physical events with increased speed while maintaining the full field of view. The complete data acquisition system, with Concert and the smart camera platform was successfully integrated and used for fast X-ray imaging experiments at KIT's synchrotron radiation facility ANKA.

Spatial Statistics for Tumor Cell Counting and Classification

NASA Astrophysics Data System (ADS)

Wirjadi, Oliver; Kim, Yoo-Jin; Breuel, Thomas

To count and classify cells in histological sections is a standard task in histology. One example is the grading of meningiomas, benign tumors of the meninges, which requires to assess the fraction of proliferating cells in an image. As this process is very time consuming when performed manually, automation is required. To address such problems, we propose a novel application of Markov point process methods in computer vision, leading to algorithms for computing the locations of circular objects in images. In contrast to previous algorithms using such spatial statistics methods in image analysis, the present one is fully trainable. This is achieved by combining point process methods with statistical classifiers. Using simulated data, the method proposed in this paper will be shown to be more accurate and more robust to noise than standard image processing methods. On the publicly available SIMCEP benchmark for cell image analysis algorithms, the cell count performance of the present paper is significantly more accurate than results published elsewhere, especially when cells form dense clusters. Furthermore, the proposed system performs as well as a state-of-the-art algorithm for the computer-aided histological grading of meningiomas when combined with a simple k-nearest neighbor classifier for identifying proliferating cells.

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.

Normalized Temperature Contrast Processing in Infrared Flash Thermography

NASA Technical Reports Server (NTRS)

Koshti, Ajay M.

2016-01-01

The paper presents further development in normalized contrast processing used in flash infrared thermography method. Method of computing normalized image or pixel intensity contrast, and normalized temperature contrast are provided. Methods of converting image contrast to temperature contrast and vice versa are provided. Normalized contrast processing in flash thermography is useful in quantitative analysis of flash thermography data including flaw characterization and comparison of experimental results with simulation. Computation of normalized temperature contrast involves use of flash thermography data acquisition set-up with high reflectivity foil and high emissivity tape such that the foil, tape and test object are imaged simultaneously. Methods of assessing other quantitative parameters such as emissivity of object, afterglow heat flux, reflection temperature change and surface temperature during flash thermography are also provided. Temperature imaging and normalized temperature contrast processing provide certain advantages over normalized image contrast processing by reducing effect of reflected energy in images and measurements, therefore providing better quantitative data. Examples of incorporating afterglow heat-flux and reflection temperature evolution in flash thermography simulation are also discussed.

The influence of the microscope lamp filament colour temperature on the process of digital images of histological slides acquisition standardization.

PubMed

Korzynska, Anna; Roszkowiak, Lukasz; Pijanowska, Dorota; Kozlowski, Wojciech; Markiewicz, Tomasz

2014-01-01

The aim of this study is to compare the digital images of the tissue biopsy captured with optical microscope using bright field technique under various light conditions. The range of colour's variation in immunohistochemically stained with 3,3'-Diaminobenzidine and Haematoxylin tissue samples is immense and coming from various sources. One of them is inadequate setting of camera's white balance to microscope's light colour temperature. Although this type of error can be easily handled during the stage of image acquisition, it can be eliminated with use of colour adjustment algorithms. The examination of the dependence of colour variation from microscope's light temperature and settings of the camera is done as an introductory research to the process of automatic colour standardization. Six fields of view with empty space among the tissue samples have been selected for analysis. Each field of view has been acquired 225 times with various microscope light temperature and camera white balance settings. The fourteen randomly chosen images have been corrected and compared, with the reference image, by the following methods: Mean Square Error, Structural SIMilarity and visual assessment of viewer. For two types of backgrounds and two types of objects, the statistical image descriptors: range, median, mean and its standard deviation of chromaticity on a and b channels from CIELab colour space, and luminance L, and local colour variability for objects' specific area have been calculated. The results have been averaged for 6 images acquired in the same light conditions and camera settings for each sample. The analysis of the results leads to the following conclusions: (1) the images collected with white balance setting adjusted to light colour temperature clusters in certain area of chromatic space, (2) the process of white balance correction for images collected with white balance camera settings not matched to the light temperature moves image descriptors into proper chromatic space but simultaneously the value of luminance changes. So the process of the image unification in a sense of colour fidelity can be solved in separate introductory stage before the automatic image analysis.

Neural classifier in the estimation process of maturity of selected varieties of apples

NASA Astrophysics Data System (ADS)

Boniecki, P.; Piekarska-Boniecka, H.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Wojcieszak, D.; Zbytek, Z.; Ludwiczak, A.; Przybylak, A.; Lewicki, A.

2015-07-01

This paper seeks to present methods of neural image analysis aimed at estimating the maturity state of selected varieties of apples which are popular in Poland. An identification of the degree of maturity of selected varieties of apples has been conducted on the basis of information encoded in graphical form, presented in the digital photos. The above process involves the application of the BBCH scale, used to determine the maturity of apples. The aforementioned scale is widely used in the EU and has been developed for many species of monocotyledonous plants and dicotyledonous plants. It is also worth noticing that the given scale enables detailed determinations of development stage of a given plant. The purpose of this work is to identify maturity level of selected varieties of apples, which is supported by the use of image analysis methods and classification techniques represented by artificial neural networks. The analysis of graphical representative features based on image analysis method enabled the assessment of the maturity of apples. For the utilitarian purpose the "JabVis 1.1" neural IT system was created, in accordance with requirements of the software engineering dedicated to support the decision-making processes occurring in broadly understood production process and processing of apples.

Real-time feedback control of twin-screw wet granulation based on image analysis.

PubMed

Madarász, Lajos; Nagy, Zsombor Kristóf; Hoffer, István; Szabó, Barnabás; Csontos, István; Pataki, Hajnalka; Démuth, Balázs; Szabó, Bence; Csorba, Kristóf; Marosi, György

2018-06-04

The present paper reports the first dynamic image analysis-based feedback control of continuous twin-screw wet granulation process. Granulation of the blend of lactose and starch was selected as a model process. The size and size distribution of the obtained particles were successfully monitored by a process camera coupled with an image analysis software developed by the authors. The validation of the developed system showed that the particle size analysis tool can determine the size of the granules with an error of less than 5 µm. The next step was to implement real-time feedback control of the process by controlling the liquid feeding rate of the pump through a PC, based on the real-time determined particle size results. After the establishment of the feedback control, the system could correct different real-life disturbances, creating a Process Analytically Controlled Technology (PACT), which guarantees the real-time monitoring and controlling of the quality of the granules. In the event of changes or bad tendencies in the particle size, the system can automatically compensate the effect of disturbances, ensuring proper product quality. This kind of quality assurance approach is especially important in the case of continuous pharmaceutical technologies. Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Advanced Land Imager Assessment System

NASA Technical Reports Server (NTRS)

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

Performance assessment of multi-frequency processing of ICU chest images for enhanced visualization of tubes and catheters

NASA Astrophysics Data System (ADS)

Wang, Xiaohui; Couwenhoven, Mary E.; Foos, David H.; Doran, James; Yankelevitz, David F.; Henschke, Claudia I.

2008-03-01

An image-processing method has been developed to improve the visibility of tube and catheter features in portable chest x-ray (CXR) images captured in the intensive care unit (ICU). The image-processing method is based on a multi-frequency approach, wherein the input image is decomposed into different spatial frequency bands, and those bands that contain the tube and catheter signals are individually enhanced by nonlinear boosting functions. Using a random sampling strategy, 50 cases were retrospectively selected for the study from a large database of portable CXR images that had been collected from multiple institutions over a two-year period. All images used in the study were captured using photo-stimulable, storage phosphor computed radiography (CR) systems. Each image was processed two ways. The images were processed with default image processing parameters such as those used in clinical settings (control). The 50 images were then separately processed using the new tube and catheter enhancement algorithm (test). Three board-certified radiologists participated in a reader study to assess differences in both detection-confidence performance and diagnostic efficiency between the control and test images. Images were evaluated on a diagnostic-quality, 3-megapixel monochrome monitor. Two scenarios were studied: the baseline scenario, representative of today's workflow (a single-control image presented with the window/level adjustments enabled) vs. the test scenario (a control/test image pair presented with a toggle enabled and the window/level settings disabled). The radiologists were asked to read the images in each scenario as they normally would for clinical diagnosis. Trend analysis indicates that the test scenario offers improved reading efficiency while providing as good or better detection capability compared to the baseline scenario.

Experiments with recursive estimation in astronomical image processing

NASA Technical Reports Server (NTRS)

Busko, I.

1992-01-01

Recursive estimation concepts were applied to image enhancement problems since the 70's. However, very few applications in the particular area of astronomical image processing are known. These concepts were derived, for 2-dimensional images, from the well-known theory of Kalman filtering in one dimension. The historic reasons for application of these techniques to digital images are related to the images' scanned nature, in which the temporal output of a scanner device can be processed on-line by techniques borrowed directly from 1-dimensional recursive signal analysis. However, recursive estimation has particular properties that make it attractive even in modern days, when big computer memories make the full scanned image available to the processor at any given time. One particularly important aspect is the ability of recursive techniques to deal with non-stationary phenomena, that is, phenomena which have their statistical properties variable in time (or position in a 2-D image). Many image processing methods make underlying stationary assumptions either for the stochastic field being imaged, for the imaging system properties, or both. They will underperform, or even fail, when applied to images that deviate significantly from stationarity. Recursive methods, on the contrary, make it feasible to perform adaptive processing, that is, to process the image by a processor with properties tuned to the image's local statistical properties. Recursive estimation can be used to build estimates of images degraded by such phenomena as noise and blur. We show examples of recursive adaptive processing of astronomical images, using several local statistical properties to drive the adaptive processor, as average signal intensity, signal-to-noise and autocorrelation function. Software was developed under IRAF, and as such will be made available to interested users.

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Fluorescent screens and image processing for the APS linac test stand

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

Berg, W.; Ko, K.

A fluorescent screen was used to monitor relative beam position and spot size of a 56-MeV electron beam in the linac test stand. A chromium doped alumina ceramic screen inserted into the beam was monitored by a video camera. The resulting image was captured using a frame grabber and stored into memory. Reconstruction and analysis of the stored image was performed using PV-WAVE. This paper will discuss the hardware and software implementation of the fluorescent screen and imaging system. Proposed improvements for the APS linac fluorescent screens and image processing will also be discussed.

Radar image enhancement and simulation as an aid to interpretation and training

NASA Technical Reports Server (NTRS)

Frost, V. S.; Stiles, J. A.; Holtzman, J. C.; Dellwig, L. F.; Held, D. N.

1980-01-01

Greatly increased activity in the field of radar image applications in the coming years demands that techniques of radar image analysis, enhancement, and simulation be developed now. Since the statistical nature of radar imagery differs from that of photographic imagery, one finds that the required digital image processing algorithms (e.g., for improved viewing and feature extraction) differ from those currently existing. This paper addresses these problems and discusses work at the Remote Sensing Laboratory in image simulation and processing, especially for systems comparable to the formerly operational SEASAT synthetic aperture radar.

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

A new method of cardiographic image segmentation based on grammar

NASA Astrophysics Data System (ADS)

Hamdi, Salah; Ben Abdallah, Asma; Bedoui, Mohamed H.; Alimi, Adel M.

2011-10-01

The measurement of the most common ultrasound parameters, such as aortic area, mitral area and left ventricle (LV) volume, requires the delineation of the organ in order to estimate the area. In terms of medical image processing this translates into the need to segment the image and define the contours as accurately as possible. The aim of this work is to segment an image and make an automated area estimation based on grammar. The entity "language" will be projected to the entity "image" to perform structural analysis and parsing of the image. We will show how the idea of segmentation and grammar-based area estimation is applied to real problems of cardio-graphic image processing.

Processing infrared images of aircraft lapjoints

NASA Technical Reports Server (NTRS)

Syed, Hazari; Winfree, William P.; Cramer, K. E.

1992-01-01

Techniques for processing IR images of aging aircraft lapjoint data are discussed. Attention is given to a technique for detecting disbonds in aircraft lapjoints which clearly delineates the disbonded region from the bonded regions. The technique is weak on unpainted aircraft skin surfaces, but can be overridden by using a self-adhering contact sheet. Neural network analysis on raw temperature data has been shown to be an effective tool for visualization of images. Numerical simulation results show the above processing technique to be an effective tool in delineating the disbonds.

Image analysis and modeling in medical image computing. Recent developments and advances.

PubMed

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body. Hence, model-based image computing methods are important tools to improve medical diagnostics and patient treatment in future.

Overview of machine vision methods in x-ray imaging and microtomography

NASA Astrophysics Data System (ADS)

Buzmakov, Alexey; Zolotov, Denis; Chukalina, Marina; Nikolaev, Dmitry; Gladkov, Andrey; Ingacheva, Anastasia; Yakimchuk, Ivan; Asadchikov, Victor

2018-04-01

Digital X-ray imaging became widely used in science, medicine, non-destructive testing. This allows using modern digital images analysis for automatic information extraction and interpretation. We give short review of scientific applications of machine vision in scientific X-ray imaging and microtomography, including image processing, feature detection and extraction, images compression to increase camera throughput, microtomography reconstruction, visualization and setup adjustment.

Currently available methodologies for the processing of intravascular ultrasound and optical coherence tomography images.

PubMed

Athanasiou, Lambros; Sakellarios, Antonis I; Bourantas, Christos V; Tsirka, Georgia; Siogkas, Panagiotis; Exarchos, Themis P; Naka, Katerina K; Michalis, Lampros K; Fotiadis, Dimitrios I

2014-07-01

Optical coherence tomography and intravascular ultrasound are the most widely used methodologies in clinical practice as they provide high resolution cross-sectional images that allow comprehensive visualization of the lumen and plaque morphology. Several methods have been developed in recent years to process the output of these imaging modalities, which allow fast, reliable and reproducible detection of the luminal borders and characterization of plaque composition. These methods have proven useful in the study of the atherosclerotic process as they have facilitated analysis of a vast amount of data. This review presents currently available intravascular ultrasound and optical coherence tomography processing methodologies for segmenting and characterizing the plaque area, highlighting their advantages and disadvantages, and discusses the future trends in intravascular imaging.

Computational analysis of Pelton bucket tip erosion using digital image processing

NASA Astrophysics Data System (ADS)

Shrestha, Bim Prasad; Gautam, Bijaya; Bajracharya, Tri Ratna

2008-03-01

Erosion of hydro turbine components through sand laden river is one of the biggest problems in Himalayas. Even with sediment trapping systems, complete removal of fine sediment from water is impossible and uneconomical; hence most of the turbine components in Himalayan Rivers are exposed to sand laden water and subject to erode. Pelton bucket which are being wildly used in different hydropower generation plant undergoes erosion on the continuous presence of sand particles in water. The subsequent erosion causes increase in splitter thickness, which is supposed to be theoretically zero. This increase in splitter thickness gives rise to back hitting of water followed by decrease in turbine efficiency. This paper describes the process of measurement of sharp edges like bucket tip using digital image processing. Image of each bucket is captured and allowed to run for 72 hours; sand concentration in water hitting the bucket is closely controlled and monitored. Later, the image of the test bucket is taken in the same condition. The process is repeated for 10 times. In this paper digital image processing which encompasses processes that performs image enhancement in both spatial and frequency domain. In addition, the processes that extract attributes from images, up to and including the measurement of splitter's tip. Processing of image has been done in MATLAB 6.5 platform. The result shows that quantitative measurement of edge erosion of sharp edges could accurately be detected and the erosion profile could be generated using image processing technique.

Use of neural image analysis methods in the process to determine the dry matter content in the compost

NASA Astrophysics Data System (ADS)

Wojcieszak, D.; Przybył, J.; Lewicki, A.; Ludwiczak, A.; Przybylak, A.; Boniecki, P.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Witaszek, K.

2015-07-01

The aim of this research was investigate the possibility of using methods of computer image analysis and artificial neural networks for to assess the amount of dry matter in the tested compost samples. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in the compost. Generated neural model may be the beginning of research into the use of neural image analysis assess the content of dry matter and other constituents of compost. The presented model RBF 19:19-2-1:1 characterized by test error 0.092189 may be more efficient.

Extended depth of field imaging for high speed object analysis

NASA Technical Reports Server (NTRS)

Frost, Keith (Inventor); Ortyn, William (Inventor); Basiji, David (Inventor); Bauer, Richard (Inventor); Liang, Luchuan (Inventor); Hall, Brian (Inventor); Perry, David (Inventor)

2011-01-01

A high speed, high-resolution flow imaging system is modified to achieve extended depth of field imaging. An optical distortion element is introduced into the flow imaging system. Light from an object, such as a cell, is distorted by the distortion element, such that a point spread function (PSF) of the imaging system is invariant across an extended depth of field. The distorted light is spectrally dispersed, and the dispersed light is used to simultaneously generate a plurality of images. The images are detected, and image processing is used to enhance the detected images by compensating for the distortion, to achieve extended depth of field images of the object. The post image processing preferably involves de-convolution, and requires knowledge of the PSF of the imaging system, as modified by the optical distortion element.

Successful implementation of image-guided radiation therapy quality assurance in the Trans Tasman Radiation Oncology Group 08.01 PROFIT Study.

PubMed

Middleton, Mark; Frantzis, Jim; Healy, Brendan; Jones, Mark; Murry, Rebecca; Kron, Tomas; Plank, Ashley; Catton, Charles; Martin, Jarad

2011-12-01

The quality assurance (QA) of image-guided radiation therapy (IGRT) within clinical trials is in its infancy, but its importance will continue to grow as IGRT becomes the standard of care. The purpose of this study was to demonstrate the feasibility of IGRT QA as part of the credentialing process for a clinical trial. As part of the accreditation process for a randomized trial in prostate cancer hypofraction, IGRT benchmarking across multiple sites was incorporated. Each participating site underwent IGRT credentialing via a site visit. In all centers, intraprostatic fiducials were used. A real-time assessment of analysis of IGRT was performed using Varian's Offline Review image analysis package. Two-dimensional (2D) kV and MV electronic portal imaging prostate patient datasets were used, consisting of 39 treatment verification images for 2D/2D comparison with the digitally reconstructed radiograph derived from the planning scan. The influence of differing sites, image modality, and observer experience on IGRT was then assessed. Statistical analysis of the mean mismatch errors showed that IGRT analysis was performed uniformly regardless of institution, therapist seniority, or imaging modality across the three orthogonal planes. The IGRT component of clinical trials that include sophisticated planning and treatment protocols must undergo stringent QA. The IGRT technique of intraprostatic fiducials has been shown in the context of this trial to be undertaken in a uniform manner across Australia. Extending this concept to many sites with different equipment and IGRT experience will require a robust remote credentialing process. Crown Copyright © 2011. Published by Elsevier Inc. All rights reserved.

Managing Institutional Image.

ERIC Educational Resources Information Center

Melchiori, Gerlinda S.

1990-01-01

A managerial process for enhancing the image and public reputation of a higher education institution is outlined. It consists of five stages: market research; data analysis and market positioning; communication of results and recommendations to the administration; development of a global image program; and impact evaluation. (MSE)

Platform for Postprocessing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Don

2008-01-01

Taking advantage of the similarities that exist among all waveform-based non-destructive evaluation (NDE) methods, a common software platform has been developed containing multiple- signal and image-processing techniques for waveforms and images. The NASA NDE Signal and Image Processing software has been developed using the latest versions of LabVIEW, and its associated Advanced Signal Processing and Vision Toolkits. The software is useable on a PC with Windows XP and Windows Vista. The software has been designed with a commercial grade interface in which two main windows, Waveform Window and Image Window, are displayed if the user chooses a waveform file to display. Within these two main windows, most actions are chosen through logically conceived run-time menus. The Waveform Window has plots for both the raw time-domain waves and their frequency- domain transformations (fast Fourier transform and power spectral density). The Image Window shows the C-scan image formed from information of the time-domain waveform (such as peak amplitude) or its frequency-domain transformation at each scan location. The user also has the ability to open an image, or series of images, or a simple set of X-Y paired data set in text format. Each of the Waveform and Image Windows contains menus from which to perform many user actions. An option exists to use raw waves obtained directly from scan, or waves after deconvolution if system wave response is provided. Two types of deconvolution, time-based subtraction or inverse-filter, can be performed to arrive at a deconvolved wave set. Additionally, the menu on the Waveform Window allows preprocessing of waveforms prior to image formation, scaling and display of waveforms, formation of different types of images (including non-standard types such as velocity), gating of portions of waves prior to image formation, and several other miscellaneous and specialized operations. The menu available on the Image Window allows many further image processing and analysis operations, some of which are found in commercially-available image-processing software programs (such as Adobe Photoshop), and some that are not (removing outliers, Bscan information, region-of-interest analysis, line profiles, and precision feature measurements).

Process perspective on image quality evaluation

NASA Astrophysics Data System (ADS)

Leisti, Tuomas; Halonen, Raisa; Kokkonen, Anna; Weckman, Hanna; Mettänen, Marja; Lensu, Lasse; Ritala, Risto; Oittinen, Pirkko; Nyman, Göte

2008-01-01

The psychological complexity of multivariate image quality evaluation makes it difficult to develop general image quality metrics. Quality evaluation includes several mental processes and ignoring these processes and the use of a few test images can lead to biased results. By using a qualitative/quantitative (Interpretation Based Quality, IBQ) methodology, we examined the process of pair-wise comparison in a setting, where the quality of the images printed by laser printer on different paper grades was evaluated. Test image consisted of a picture of a table covered with several objects. Three other images were also used, photographs of a woman, cityscape and countryside. In addition to the pair-wise comparisons, observers (N=10) were interviewed about the subjective quality attributes they used in making their quality decisions. An examination of the individual pair-wise comparisons revealed serious inconsistencies in observers' evaluations on the test image content, but not on other contexts. The qualitative analysis showed that this inconsistency was due to the observers' focus of attention. The lack of easily recognizable context in the test image may have contributed to this inconsistency. To obtain reliable knowledge of the effect of image context or attention on subjective image quality, a qualitative methodology is needed.

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.

Neural analysis of bovine ovaries ultrasound images in the identification process of the corpus luteum

NASA Astrophysics Data System (ADS)

Górna, K.; Jaśkowski, B. M.; Okoń, P.; Czechlowski, M.; Koszela, K.; Zaborowicz, M.; Idziaszek, P.

2017-07-01

The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.

Influence of Averaging Preprocessing on Image Analysis with a Markov Random Field Model

NASA Astrophysics Data System (ADS)

Sakamoto, Hirotaka; Nakanishi-Ohno, Yoshinori; Okada, Masato

2018-02-01

This paper describes our investigations into the influence of averaging preprocessing on the performance of image analysis. Averaging preprocessing involves a trade-off: image averaging is often undertaken to reduce noise while the number of image data available for image analysis is decreased. We formulated a process of generating image data by using a Markov random field (MRF) model to achieve image analysis tasks such as image restoration and hyper-parameter estimation by a Bayesian approach. According to the notions of Bayesian inference, posterior distributions were analyzed to evaluate the influence of averaging. There are three main results. First, we found that the performance of image restoration with a predetermined value for hyper-parameters is invariant regardless of whether averaging is conducted. We then found that the performance of hyper-parameter estimation deteriorates due to averaging. Our analysis of the negative logarithm of the posterior probability, which is called the free energy based on an analogy with statistical mechanics, indicated that the confidence of hyper-parameter estimation remains higher without averaging. Finally, we found that when the hyper-parameters are estimated from the data, the performance of image restoration worsens as averaging is undertaken. We conclude that averaging adversely influences the performance of image analysis through hyper-parameter estimation.

Applications of satellite image processing to the analysis of Amazonian cultural ecology

NASA Technical Reports Server (NTRS)

Behrens, Clifford A.

1991-01-01

This paper examines the application of satellite image processing towards identifying and comparing resource exploitation among indigenous Amazonian peoples. The use of statistical and heuristic procedures for developing land cover/land use classifications from Thematic Mapper satellite imagery will be discussed along with actual results from studies of relatively small (100 - 200 people) settlements. Preliminary research indicates that analysis of satellite imagery holds great potential for measuring agricultural intensification, comparing rates of tropical deforestation, and detecting changes in resource utilization patterns over time.

Processing and analysis of commercial satellite image data of the nuclear accident near Chernobyl, U.S.S.R.

USGS Publications Warehouse

Sadowski, Franklin G.; Covington, Steven J.

1987-01-01

Advanced digital processing techniques were applied to Landsat-5 Thematic Mapper (TM) data and SPOT highresolution visible (HRV) panchromatic data to maximize the utility of images of a nuclear powerplant emergency at Chernobyl in the Soviet Ukraine. The images demonstrate the unique interpretive capabilities provided by the numerous spectral bands of the Thematic Mapper and the high spatial resolution of the SPOT HRV sensor.

Display management subsystem, version 1: A user's eye view

NASA Technical Reports Server (NTRS)

Parker, Dolores

1986-01-01

The structure and application functions of the Display Management Subsystem (DMS) are described. The DMS, a subsystem of the Transportable Applications Executive (TAE), was designed to provide a device-independent interface for an image processing and display environment. The system is callable by C and FORTRAN applications, portable to accommodate different image analysis terminals, and easily expandable to meet local needs. Generic applications are also available for performing many image processing tasks.

Image Analysis Based on Soft Computing and Applied on Space Shuttle During the Liftoff Process

NASA Technical Reports Server (NTRS)

Dominquez, Jesus A.; Klinko, Steve J.

2007-01-01

Imaging techniques based on Soft Computing (SC) and developed at Kennedy Space Center (KSC) have been implemented on a variety of prototype applications related to the safety operation of the Space Shuttle during the liftoff process. These SC-based prototype applications include detection and tracking of moving Foreign Objects Debris (FOD) during the Space Shuttle liftoff, visual anomaly detection on slidewires used in the emergency egress system for the Space Shuttle at the laJlIlch pad, and visual detection of distant birds approaching the Space Shuttle launch pad. This SC-based image analysis capability developed at KSC was also used to analyze images acquired during the accident of the Space Shuttle Columbia and estimate the trajectory and velocity of the foam that caused the accident.

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

PubMed

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

2018-04-12

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

Sub-band denoising and spline curve fitting method for hemodynamic measurement in perfusion MRI

NASA Astrophysics Data System (ADS)

Lin, Hong-Dun; Huang, Hsiao-Ling; Hsu, Yuan-Yu; Chen, Chi-Chen; Chen, Ing-Yi; Wu, Liang-Chi; Liu, Ren-Shyan; Lin, Kang-Ping

2003-05-01

In clinical research, non-invasive MR perfusion imaging is capable of investigating brain perfusion phenomenon via various hemodynamic measurements, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean trasnit time (MTT). These hemodynamic parameters are useful in diagnosing brain disorders such as stroke, infarction and periinfarct ischemia by further semi-quantitative analysis. However, the accuracy of quantitative analysis is usually affected by poor signal-to-noise ratio image quality. In this paper, we propose a hemodynamic measurement method based upon sub-band denoising and spline curve fitting processes to improve image quality for better hemodynamic quantitative analysis results. Ten sets of perfusion MRI data and corresponding PET images were used to validate the performance. For quantitative comparison, we evaluate gray/white matter CBF ratio. As a result, the hemodynamic semi-quantitative analysis result of mean gray to white matter CBF ratio is 2.10 +/- 0.34. The evaluated ratio of brain tissues in perfusion MRI is comparable to PET technique is less than 1-% difference in average. Furthermore, the method features excellent noise reduction and boundary preserving in image processing, and short hemodynamic measurement time.

Imaging systems and algorithms to analyze biological samples in real-time using mobile phone microscopy

PubMed Central

Mayberry, Addison; Perkins, David L.; Holcomb, Daniel E.

2018-01-01

Miniaturized imaging devices have pushed the boundaries of point-of-care imaging, but existing mobile-phone-based imaging systems do not exploit the full potential of smart phones. This work demonstrates the use of simple imaging configurations to deliver superior image quality and the ability to handle a wide range of biological samples. Results presented in this work are from analysis of fluorescent beads under fluorescence imaging, as well as helminth eggs and freshwater mussel larvae under white light imaging. To demonstrate versatility of the systems, real time analysis and post-processing results of the sample count and sample size are presented in both still images and videos of flowing samples. PMID:29509786

Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

PubMed

Orbán, Levente L; Chartier, Sylvain

2015-01-01

Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

High resolution imaging of latent fingerprints by localized corrosion on brass surfaces.

PubMed

Goddard, Alex J; Hillman, A Robert; Bond, John W

2010-01-01

The Atomic Force Microscope (AFM) is capable of imaging fingerprint ridges on polished brass substrates at an unprecedented level of detail. While exposure to elevated humidity at ambient or slightly raised temperatures does not change the image appreciably, subsequent brief heating in a flame results in complete loss of the sweat deposit and the appearance of pits and trenches. Localized elemental analysis (using EDAX, coupled with SEM imaging) shows the presence of the constituents of salt in the initial deposits. Together with water and atmospheric oxygen--and with thermal enhancement--these are capable of driving a surface corrosion process. This process is sufficiently localized that it has the potential to generate a durable negative topographical image of the fingerprint. AFM examination of surface regions between ridges revealed small deposits (probably microscopic "spatter" of sweat components or transferred particulates) that may ultimately limit the level of ridge detail analysis.

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

PubMed Central

2013-01-01

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

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

PubMed

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

2013-10-11

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

Research for Key Techniques of Geophysical Recognition System of Hydrocarbon-induced Magnetic Anomalies Based on Hydrocarbon Seepage Theory

NASA Astrophysics Data System (ADS)

Zhang, L.; Hao, T.; Zhao, B.

2009-12-01

Hydrocarbon seepage effects can cause magnetic alteration zones in near surface, and the magnetic anomalies induced by the alteration zones can thus be used to locate oil-gas potential regions. In order to reduce the inaccuracy and multi-resolution of the hydrocarbon anomalies recognized only by magnetic data, and to meet the requirement of integrated management and sythetic analysis of multi-source geoscientfic data, it is necessary to construct a recognition system that integrates the functions of data management, real-time processing, synthetic evaluation, and geologic mapping. In this paper research for the key techniques of the system is discussed. Image processing methods can be applied to potential field images so as to make it easier for visual interpretation and geological understanding. For gravity or magnetic images, the anomalies with identical frequency-domain characteristics but different spatial distribution will reflect differently in texture and relevant textural statistics. Texture is a description of structural arrangements and spatial variation of a dataset or an image, and has been applied in many research fields. Textural analysis is a procedure that extracts textural features by image processing methods and thus obtains a quantitative or qualitative description of texture. When the two kinds of anomalies have no distinct difference in amplitude or overlap in frequency spectrum, they may be distinguishable due to their texture, which can be considered as textural contrast. Therefore, for the recognition system we propose a new “magnetic spots” recognition method based on image processing techniques. The method can be divided into 3 major steps: firstly, separate local anomalies caused by shallow, relatively small sources from the total magnetic field, and then pre-process the local magnetic anomaly data by image processing methods such that magnetic anomalies can be expressed as points, lines and polygons with spatial correlation, which includes histogram-equalization based image display, object recognition and extraction; then, mine the spatial characteristics and correlations of the magnetic anomalies using textural statistics and analysis, and study the features of known anomalous objects (closures, hydrocarbon-bearing structures, igneous rocks, etc.) in the same research area; finally, classify the anomalies, cluster them according to their similarity, and predict hydrocarbon induced “magnetic spots” combined with geologic, drilling and rock core data. The system uses the ArcGIS as the secondary development platform, inherits the basic functions of the ArcGIS, and develops two main sepecial functional modules, the module for conventional potential-field data processing methods and the module for feature extraction and enhancement based on image processing and analysis techniques. The system can be applied to realize the geophysical detection and recognition of near-surface hydrocarbon seepage anomalies, provide technical support for locating oil-gas potential regions, and promote geophysical data processing and interpretation to advance more efficiently.

Benefit from NASA

NASA Image and Video Library

1985-01-01

The NASA imaging processing technology, an advanced computer technique to enhance images sent to Earth in digital form by distant spacecraft, helped develop a new vision screening process. The Ocular Vision Screening system, an important step in preventing vision impairment, is a portable device designed especially to detect eye problems in children through the analysis of retinal reflexes.

MTI science, data products, and ground-data processing overview

NASA Astrophysics Data System (ADS)

Szymanski, John J.; Atkins, William H.; Balick, Lee K.; Borel, Christoph C.; Clodius, William B.; Christensen, R. Wynn; Davis, Anthony B.; Echohawk, J. C.; Galbraith, Amy E.; Hirsch, Karen L.; Krone, James B.; Little, Cynthia K.; McLachlan, Peter M.; Morrison, Aaron; Pollock, Kimberly A.; Pope, Paul A.; Novak, Curtis; Ramsey, Keri A.; Riddle, Emily E.; Rohde, Charles A.; Roussel-Dupre, Diane C.; Smith, Barham W.; Smith, Kathy; Starkovich, Kim; Theiler, James P.; Weber, Paul G.

2001-08-01

The mission of the Multispectral Thermal Imager (MTI) satellite is to demonstrate the efficacy of highly accurate multispectral imaging for passive characterization of urban and industrial areas, as well as sites of environmental interest. The satellite makes top-of-atmosphere radiance measurements that are subsequently processed into estimates of surface properties such as vegetation health, temperatures, material composition and others. The MTI satellite also provides simultaneous data for atmospheric characterization at high spatial resolution. To utilize these data the MTI science program has several coordinated components, including modeling, comprehensive ground-truth measurements, image acquisition planning, data processing and data interpretation and analysis. Algorithms have been developed to retrieve a multitude of physical quantities and these algorithms are integrated in a processing pipeline architecture that emphasizes automation, flexibility and programmability. In addition, the MTI science team has produced detailed site, system and atmospheric models to aid in system design and data analysis. This paper provides an overview of the MTI research objectives, data products and ground data processing.

Quantitative analysis of brain magnetic resonance imaging for hepatic encephalopathy

NASA Astrophysics Data System (ADS)

Syh, Hon-Wei; Chu, Wei-Kom; Ong, Chin-Sing

1992-06-01

High intensity lesions around ventricles have recently been observed in T1-weighted brain magnetic resonance images for patients suffering hepatic encephalopathy. The exact etiology that causes magnetic resonance imaging (MRI) gray scale changes has not been totally understood. The objective of our study was to investigate, through quantitative means, (1) the amount of changes to brain white matter due to the disease process, and (2) the extent and distribution of these high intensity lesions, since it is believed that the abnormality may not be entirely limited to the white matter only. Eleven patients with proven haptic encephalopathy and three normal persons without any evidence of liver abnormality constituted our current data base. Trans-axial, sagittal, and coronal brain MRI were obtained on a 1.5 Tesla scanner. All processing was carried out on a microcomputer-based image analysis system in an off-line manner. Histograms were decomposed into regular brain tissues and lesions. Gray scale ranges coded as lesion were then brought back to original images to identify distribution of abnormality. Our results indicated the disease process involved pallidus, mesencephalon, and subthalamic regions.

Color image analysis of contaminants and bacteria transport in porous media

NASA Astrophysics Data System (ADS)

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

1997-10-01

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

Superresolution intrinsic fluorescence imaging of chromatin utilizing native, unmodified nucleic acids for contrast

PubMed Central

Dong, Biqin; Almassalha, Luay M.; Stypula-Cyrus, Yolanda; Urban, Ben E.; Chandler, John E.; Nguyen, The-Quyen; Sun, Cheng; Zhang, Hao F.; Backman, Vadim

2016-01-01

Visualizing the nanoscale intracellular structures formed by nucleic acids, such as chromatin, in nonperturbed, structurally and dynamically complex cellular systems, will help expand our understanding of biological processes and open the next frontier for biological discovery. Traditional superresolution techniques to visualize subdiffractional macromolecular structures formed by nucleic acids require exogenous labels that may perturb cell function and change the very molecular processes they intend to study, especially at the extremely high label densities required for superresolution. However, despite tremendous interest and demonstrated need, label-free optical superresolution imaging of nucleotide topology under native nonperturbing conditions has never been possible. Here we investigate a photoswitching process of native nucleotides and present the demonstration of subdiffraction-resolution imaging of cellular structures using intrinsic contrast from unmodified DNA based on the principle of single-molecule photon localization microscopy (PLM). Using DNA-PLM, we achieved nanoscopic imaging of interphase nuclei and mitotic chromosomes, allowing a quantitative analysis of the DNA occupancy level and a subdiffractional analysis of the chromosomal organization. This study may pave a new way for label-free superresolution nanoscopic imaging of macromolecular structures with nucleotide topologies and could contribute to the development of new DNA-based contrast agents for superresolution imaging. PMID:27535934

Automatic analysis of microscopic images of red blood cell aggregates

NASA Astrophysics Data System (ADS)

Menichini, Pablo A.; Larese, Mónica G.; Riquelme, Bibiana D.

2015-06-01

Red blood cell aggregation is one of the most important factors in blood viscosity at stasis or at very low rates of flow. The basic structure of aggregates is a linear array of cell commonly termed as rouleaux. Enhanced or abnormal aggregation is seen in clinical conditions, such as diabetes and hypertension, producing alterations in the microcirculation, some of which can be analyzed through the characterization of aggregated cells. Frequently, image processing and analysis for the characterization of RBC aggregation were done manually or semi-automatically using interactive tools. We propose a system that processes images of RBC aggregation and automatically obtains the characterization and quantification of the different types of RBC aggregates. Present technique could be interesting to perform the adaptation as a routine used in hemorheological and Clinical Biochemistry Laboratories because this automatic method is rapid, efficient and economical, and at the same time independent of the user performing the analysis (repeatability of the analysis).

Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis.

PubMed

Ibrahim, Reham S; Fathy, Hoda

2018-03-30

Tracking the impact of commonly applied post-harvesting and industrial processing practices on the compositional integrity of ginger rhizome was implemented in this work. Untargeted metabolite profiling was performed using digitally-enhanced HPTLC method where the chromatographic fingerprints were extracted using ImageJ software then analysed with multivariate Principal Component Analysis (PCA) for pattern recognition. A targeted approach was applied using a new, validated, simple and fast HPTLC image analysis method for simultaneous quantification of the officially recognized markers 6-, 8-, 10-gingerol and 6-shogaol in conjunction with chemometric Hierarchical Clustering Analysis (HCA). The results of both targeted and untargeted metabolite profiling revealed that peeling, drying in addition to storage employed during processing have a great influence on ginger chemo-profile, the different forms of processed ginger shouldn't be used interchangeably. Moreover, it deemed necessary to consider the holistic metabolic profile for comprehensive evaluation of ginger during processing. Copyright © 2018. Published by Elsevier B.V.

Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery.

PubMed

Loizou, Christos P; Theofanous, Charoula; Pantziaris, Marios; Kasparis, Takis

2014-04-01

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB(®) a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert's assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Automated Diatom Analysis Applied to Traditional Light Microscopy: A Proof-of-Concept Study

NASA Astrophysics Data System (ADS)

Little, Z. H. L.; Bishop, I.; Spaulding, S. A.; Nelson, H.; Mahoney, C.

2017-12-01

Diatom identification and enumeration by high resolution light microscopy is required for many areas of research and water quality assessment. Such analyses, however, are both expertise and labor-intensive. These challenges motivate the need for an automated process to efficiently and accurately identify and enumerate diatoms. Improvements in particle analysis software have increased the likelihood that diatom enumeration can be automated. VisualSpreadsheet software provides a possible solution for automated particle analysis of high-resolution light microscope diatom images. We applied the software, independent of its complementary FlowCam hardware, to automated analysis of light microscope images containing diatoms. Through numerous trials, we arrived at threshold settings to correctly segment 67% of the total possible diatom valves and fragments from broad fields of view. (183 light microscope images were examined containing 255 diatom particles. Of the 255 diatom particles present, 216 diatoms valves and fragments of valves were processed, with 170 properly analyzed and focused upon by the software). Manual analysis of the images yielded 255 particles in 400 seconds, whereas the software yielded a total of 216 particles in 68 seconds, thus highlighting that the software has an approximate five-fold efficiency advantage in particle analysis time. As in past efforts, incomplete or incorrect recognition was found for images with multiple valves in contact or valves with little contrast. The software has potential to be an effective tool in assisting taxonomists with diatom enumeration by completing a large portion of analyses. Benefits and limitations of the approach are presented to allow for development of future work in image analysis and automated enumeration of traditional light microscope images containing diatoms.

Associative architecture for image processing

NASA Astrophysics Data System (ADS)

Adar, Rutie; Akerib, Avidan

1997-09-01

This article presents a new generation in parallel processing architecture for real-time image processing. The approach is implemented in a real time image processor chip, called the XiumTM-2, based on combining a fully associative array which provides the parallel engine with a serial RISC core on the same die. The architecture is fully programmable and can be programmed to implement a wide range of color image processing, computer vision and media processing functions in real time. The associative part of the chip is based on patented pending methodology of Associative Computing Ltd. (ACL), which condenses 2048 associative processors, each of 128 'intelligent' bits. Each bit can be a processing bit or a memory bit. At only 33 MHz and 0.6 micron manufacturing technology process, the chip has a computational power of 3 billion ALU operations per second and 66 billion string search operations per second. The fully programmable nature of the XiumTM-2 chip enables developers to use ACL tools to write their own proprietary algorithms combined with existing image processing and analysis functions from ACL's extended set of libraries.

3D Image Analysis of Geomaterials using Confocal Microscopy

NASA Astrophysics Data System (ADS)

Mulukutla, G.; Proussevitch, A.; Sahagian, D.

2009-05-01

Confocal microscopy is one of the most significant advances in optical microscopy of the last century. It is widely used in biological sciences but its application to geomaterials lingers due to a number of technical problems. Potentially the technique can perform non-invasive testing on a laser illuminated sample that fluoresces using a unique optical sectioning capability that rejects out-of-focus light reaching the confocal aperture. Fluorescence in geomaterials is commonly induced using epoxy doped with a fluorochrome that is impregnated into the sample to enable discrimination of various features such as void space or material boundaries. However, for many geomaterials, this method cannot be used because they do not naturally fluoresce and because epoxy cannot be impregnated into inaccessible parts of the sample due to lack of permeability. As a result, the confocal images of most geomaterials that have not been pre-processed with extensive sample preparation techniques are of poor quality and lack the necessary image and edge contrast necessary to apply any commonly used segmentation techniques to conduct any quantitative study of its features such as vesicularity, internal structure, etc. In our present work, we are developing a methodology to conduct a quantitative 3D analysis of images of geomaterials collected using a confocal microscope with minimal amount of prior sample preparation and no addition of fluorescence. Two sample geomaterials, a volcanic melt sample and a crystal chip containing fluid inclusions are used to assess the feasibility of the method. A step-by-step process of image analysis includes application of image filtration to enhance the edges or material interfaces and is based on two segmentation techniques: geodesic active contours and region competition. Both techniques have been applied extensively to the analysis of medical MRI images to segment anatomical structures. Preliminary analysis suggests that there is distortion in the shapes of the segmented vesicles, vapor bubbles, and void spaces due to the optical measurements, so corrective actions are being explored. This will establish a practical and reliable framework for an adaptive 3D image processing technique for the analysis of geomaterials using confocal microscopy.

IMAGE 100: The interactive multispectral image processing system

NASA Technical Reports Server (NTRS)

Schaller, E. S.; Towles, R. W.

1975-01-01

The need for rapid, cost-effective extraction of useful information from vast quantities of multispectral imagery available from aircraft or spacecraft has resulted in the design, implementation and application of a state-of-the-art processing system known as IMAGE 100. Operating on the general principle that all objects or materials possess unique spectral characteristics or signatures, the system uses this signature uniqueness to identify similar features in an image by simultaneously analyzing signatures in multiple frequency bands. Pseudo-colors, or themes, are assigned to features having identical spectral characteristics. These themes are displayed on a color CRT, and may be recorded on tape, film, or other media. The system was designed to incorporate key features such as interactive operation, user-oriented displays and controls, and rapid-response machine processing. Owing to these features, the user can readily control and/or modify the analysis process based on his knowledge of the input imagery. Effective use can be made of conventional photographic interpretation skills and state-of-the-art machine analysis techniques in the extraction of useful information from multispectral imagery. This approach results in highly accurate multitheme classification of imagery in seconds or minutes rather than the hours often involved in processing using other means.

StreakDet data processing and analysis pipeline for space debris optical observations

NASA Astrophysics Data System (ADS)

Virtanen, Jenni; Flohrer, Tim; Muinonen, Karri; Granvik, Mikael; Torppa, Johanna; Poikonen, Jonne; Lehti, Jussi; Santti, Tero; Komulainen, Tuomo; Naranen, Jyri

We describe a novel data processing and analysis pipeline for optical observations of space debris. The monitoring of space object populations requires reliable acquisition of observational data, to support the development and validation of space debris environment models, the build-up and maintenance of a catalogue of orbital elements. In addition, data is needed for the assessment of conjunction events and for the support of contingency situations or launches. The currently available, mature image processing algorithms for detection and astrometric reduction of optical data cover objects that cross the sensor field-of-view comparably slowly, and within a rather narrow, predefined range of angular velocities. By applying specific tracking techniques, the objects appear point-like or as short trails in the exposures. However, the general survey scenario is always a “track before detect” problem, resulting in streaks, i.e., object trails of arbitrary lengths, in the images. The scope of the ESA-funded StreakDet (Streak detection and astrometric reduction) project is to investigate solutions for detecting and reducing streaks from optical images, particularly in the low signal-to-noise ratio (SNR) domain, where algorithms are not readily available yet. For long streaks, the challenge is to extract precise position information and related registered epochs with sufficient precision. Although some considerations for low-SNR processing of streak-like features are available in the current image processing and computer vision literature, there is a need to discuss and compare these approaches for space debris analysis, in order to develop and evaluate prototype implementations. In the StreakDet project, we develop algorithms applicable to single images (as compared to consecutive frames of the same field) obtained with any observing scenario, including space-based surveys and both low- and high-altitude populations. The proposed processing pipeline starts from the segmentation of the acquired image (i.e., the extraction of all sources), followed by the astrometric and photometric characterization of the candidate streaks, and ends with orbital validation of the detected streaks. A central concept of the pipeline is streak classification which guides the actual characterization process by aiming to identify the interesting sources and to filter out the uninteresting ones, as well as by allowing the tailoring of algorithms for specific streak classes (e.g. point-like vs. long, disintegrated streaks). To validate the single-image detections, the processing is finalized by orbital analysis, resulting in preliminary orbital classification (Earth-bound vs. non-Earth-bound orbit) for the detected streaks.

A simplified and powerful image processing methods to separate Thai jasmine rice and sticky rice varieties

NASA Astrophysics Data System (ADS)

Khondok, Piyoros; Sakulkalavek, Aparporn; Suwansukho, Kajpanya

2018-03-01

A simplified and powerful image processing procedures to separate the paddy of KHAW DOK MALI 105 or Thai jasmine rice and the paddy of sticky rice RD6 varieties were proposed. The procedures consist of image thresholding, image chain coding and curve fitting using polynomial function. From the fitting, three parameters of each variety, perimeters, area, and eccentricity, were calculated. Finally, the overall parameters were determined by using principal component analysis. The result shown that these procedures can be significantly separate both varieties.

Color image processing and vision system for an automated laser paint-stripping system

NASA Astrophysics Data System (ADS)

Hickey, John M., III; Hise, Lawson

1994-10-01

Color image processing in machine vision systems has not gained general acceptance. Most machine vision systems use images that are shades of gray. The Laser Automated Decoating System (LADS) required a vision system which could discriminate between substrates of various colors and textures and paints ranging from semi-gloss grays to high gloss red, white and blue (Air Force Thunderbirds). The changing lighting levels produced by the pulsed CO2 laser mandated a vision system that did not require a constant color temperature lighting for reliable image analysis.

magHD: a new approach to multi-dimensional data storage, analysis, display and exploitation

NASA Astrophysics Data System (ADS)

Angleraud, Christophe

2014-06-01

The ever increasing amount of data and processing capabilities - following the well- known Moore's law - is challenging the way scientists and engineers are currently exploiting large datasets. The scientific visualization tools, although quite powerful, are often too generic and provide abstract views of phenomena, thus preventing cross disciplines fertilization. On the other end, Geographic information Systems allow nice and visually appealing maps to be built but they often get very confused as more layers are added. Moreover, the introduction of time as a fourth analysis dimension to allow analysis of time dependent phenomena such as meteorological or climate models, is encouraging real-time data exploration techniques that allow spatial-temporal points of interests to be detected by integration of moving images by the human brain. Magellium is involved in high performance image processing chains for satellite image processing as well as scientific signal analysis and geographic information management since its creation (2003). We believe that recent work on big data, GPU and peer-to-peer collaborative processing can open a new breakthrough in data analysis and display that will serve many new applications in collaborative scientific computing, environment mapping and understanding. The magHD (for Magellium Hyper-Dimension) project aims at developing software solutions that will bring highly interactive tools for complex datasets analysis and exploration commodity hardware, targeting small to medium scale clusters with expansion capabilities to large cloud based clusters.

Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy

PubMed Central

Yoon, Woong Bae; Kim, Hyunjin; Kim, Kwang Gi; Choi, Yongdoo; Chang, Hee Jin

2016-01-01

Objectives We produced hematoxylin and eosin (H&E) staining-like color images by using confocal laser scanning microscopy (CLSM), which can obtain the same or more information in comparison to conventional tissue staining. Methods We improved images by using several image converting techniques, including morphological methods, color space conversion methods, and segmentation methods. Results An image obtained after image processing showed coloring very similar to that in images produced by H&E staining, and it is advantageous to conduct analysis through fluorescent dye imaging and microscopy rather than analysis based on single microscopic imaging. Conclusions The colors used in CLSM are different from those seen in H&E staining, which is the method most widely used for pathologic diagnosis and is familiar to pathologists. Computer technology can facilitate the conversion of images by CLSM to be very similar to H&E staining images. We believe that the technique used in this study has great potential for application in clinical tissue analysis. PMID:27525165

Methods of Hematoxylin and Erosin Image Information Acquisition and Optimization in Confocal Microscopy.

PubMed

Yoon, Woong Bae; Kim, Hyunjin; Kim, Kwang Gi; Choi, Yongdoo; Chang, Hee Jin; Sohn, Dae Kyung

2016-07-01

We produced hematoxylin and eosin (H&E) staining-like color images by using confocal laser scanning microscopy (CLSM), which can obtain the same or more information in comparison to conventional tissue staining. We improved images by using several image converting techniques, including morphological methods, color space conversion methods, and segmentation methods. An image obtained after image processing showed coloring very similar to that in images produced by H&E staining, and it is advantageous to conduct analysis through fluorescent dye imaging and microscopy rather than analysis based on single microscopic imaging. The colors used in CLSM are different from those seen in H&E staining, which is the method most widely used for pathologic diagnosis and is familiar to pathologists. Computer technology can facilitate the conversion of images by CLSM to be very similar to H&E staining images. We believe that the technique used in this study has great potential for application in clinical tissue analysis.

Visual analysis of trash bin processing on garbage trucks in low resolution video

NASA Astrophysics Data System (ADS)

Sidla, Oliver; Loibner, Gernot

2015-03-01

We present a system for trash can detection and counting from a camera which is mounted on a garbage collection truck. A working prototype has been successfully implemented and tested with several hours of real-world video. The detection pipeline consists of HOG detectors for two trash can sizes, and meanshift tracking and low level image processing for the analysis of the garbage disposal process. Considering the harsh environment and unfavorable imaging conditions, the process works already good enough so that very useful measurements from video data can be extracted. The false positive/false negative rate of the full processing pipeline is about 5-6% at fully automatic operation. Video data of a full day (about 8 hrs) can be processed in about 30 minutes on a standard PC.

Automated analysis of hot spot X-ray images at the National Ignition Facility

NASA Astrophysics Data System (ADS)

Khan, S. F.; Izumi, N.; Glenn, S.; Tommasini, R.; Benedetti, L. R.; Ma, T.; Pak, A.; Kyrala, G. A.; Springer, P.; Bradley, D. K.; Town, R. P. J.

2016-11-01

At the National Ignition Facility, the symmetry of the hot spot of imploding capsules is diagnosed by imaging the emitted x-rays using gated cameras and image plates. The symmetry of an implosion is an important factor in the yield generated from the resulting fusion process. The x-ray images are analyzed by decomposing the image intensity contours into Fourier and Legendre modes. This paper focuses on the additional protocols for the time-integrated shape analysis from image plates. For implosions with temperatures above ˜4 keV, the hard x-ray background can be utilized to infer the temperature of the hot spot.

Automated analysis of hot spot X-ray images at the National Ignition Facility

DOE PAGES

Khan, S. F.; Izumi, N.; Glenn, S.; ...

2016-09-02

At the National Ignition Facility, the symmetry of the hot spot of imploding capsules is diagnosed by imaging the emitted x-rays using gated cameras and image plates. The symmetry of an implosion is an important factor in the yield generated from the resulting fusion process. The x-ray images are analyzed by decomposing the image intensity contours into Fourier and Legendre modes. This paper focuses on the additional protocols for the time-integrated shape analysis from image plates. Here, for implosions with temperatures above ~4keV, the hard x-ray background can be utilized to infer the temperature of the hot spot.

Automated analysis of hot spot X-ray images at the National Ignition Facility

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

Khan, S. F., E-mail: khan9@llnl.gov; Izumi, N.; Glenn, S.

At the National Ignition Facility, the symmetry of the hot spot of imploding capsules is diagnosed by imaging the emitted x-rays using gated cameras and image plates. The symmetry of an implosion is an important factor in the yield generated from the resulting fusion process. The x-ray images are analyzed by decomposing the image intensity contours into Fourier and Legendre modes. This paper focuses on the additional protocols for the time-integrated shape analysis from image plates. For implosions with temperatures above ∼4 keV, the hard x-ray background can be utilized to infer the temperature of the hot spot.

Automated analysis of hot spot X-ray images at the National Ignition Facility.

PubMed

Khan, S F; Izumi, N; Glenn, S; Tommasini, R; Benedetti, L R; Ma, T; Pak, A; Kyrala, G A; Springer, P; Bradley, D K; Town, R P J

2016-11-01

At the National Ignition Facility, the symmetry of the hot spot of imploding capsules is diagnosed by imaging the emitted x-rays using gated cameras and image plates. The symmetry of an implosion is an important factor in the yield generated from the resulting fusion process. The x-ray images are analyzed by decomposing the image intensity contours into Fourier and Legendre modes. This paper focuses on the additional protocols for the time-integrated shape analysis from image plates. For implosions with temperatures above ∼4 keV, the hard x-ray background can be utilized to infer the temperature of the hot spot.

Quantification of fibre polymerization through Fourier space image analysis

PubMed Central

Nekouzadeh, Ali; Genin, Guy M.

2011-01-01

Quantification of changes in the total length of randomly oriented and possibly curved lines appearing in an image is a necessity in a wide variety of biological applications. Here, we present an automated approach based upon Fourier space analysis. Scaled, band-pass filtered power spectral densities of greyscale images are integrated to provide a quantitative measurement of the total length of lines of a particular range of thicknesses appearing in an image. A procedure is presented to correct for changes in image intensity. The method is most accurate for two-dimensional processes with fibres that do not occlude one another. PMID:24959096

Implementation Analysis of Cutting Tool Carbide with Cast Iron Material S45 C on Universal Lathe

NASA Astrophysics Data System (ADS)

Junaidi; hestukoro, Soni; yanie, Ahmad; Jumadi; Eddy

2017-12-01

Cutting tool is the tools lathe. Cutting process tool CARBIDE with Cast Iron Material Universal Lathe which is commonly found at Analysiscutting Process by some aspects numely Cutting force, Cutting Speed, Cutting Power, Cutting Indication Power, Temperature Zone 1 and Temperatur Zone 2. Purpose of this Study was to determine how big the cutting Speed, Cutting Power, electromotor Power,Temperatur Zone 1 and Temperatur Zone 2 that drives the chisel cutting CARBIDE in the Process of tur ning Cast Iron Material. Cutting force obtained from image analysis relationship between the recommended Component Cuting Force with plane of the cut and Cutting Speed obtained from image analysis of relationships between the recommended Cutting Speed Feed rate.

Distortion correction and cross-talk compensation algorithm for use with an imaging spectrometer based spatially resolved diffuse reflectance system

NASA Astrophysics Data System (ADS)

Cappon, Derek J.; Farrell, Thomas J.; Fang, Qiyin; Hayward, Joseph E.

2016-12-01

Optical spectroscopy of human tissue has been widely applied within the field of biomedical optics to allow rapid, in vivo characterization and analysis of the tissue. When designing an instrument of this type, an imaging spectrometer is often employed to allow for simultaneous analysis of distinct signals. This is especially important when performing spatially resolved diffuse reflectance spectroscopy. In this article, an algorithm is presented that allows for the automated processing of 2-dimensional images acquired from an imaging spectrometer. The algorithm automatically defines distinct spectrometer tracks and adaptively compensates for distortion introduced by optical components in the imaging chain. Crosstalk resulting from the overlap of adjacent spectrometer tracks in the image is detected and subtracted from each signal. The algorithm's performance is demonstrated in the processing of spatially resolved diffuse reflectance spectra recovered from an Intralipid and ink liquid phantom and is shown to increase the range of wavelengths over which usable data can be recovered.

Poster - Thur Eve - 05: Safety systems and failure modes and effects analysis for a magnetic resonance image guided radiation therapy system.

PubMed

Lamey, M; Carlone, M; Alasti, H; Bissonnette, J P; Borg, J; Breen, S; Coolens, C; Heaton, R; Islam, M; van Proojen, M; Sharpe, M; Stanescu, T; Jaffray, D

2012-07-01

An online Magnetic Resonance guided Radiation Therapy (MRgRT) system is under development. The system is comprised of an MRI with the capability of travel between and into HDR brachytherapy and external beam radiation therapy vaults. The system will provide on-line MR images immediately prior to radiation therapy. The MR images will be registered to a planning image and used for image guidance. With the intention of system safety we have performed a failure modes and effects analysis. A process tree of the facility function was developed. Using the process tree as well as an initial design of the facility as guidelines possible failure modes were identified, for each of these failure modes root causes were identified. For each possible failure the assignment of severity, detectability and occurrence scores was performed. Finally suggestions were developed to reduce the possibility of an event. The process tree consists of nine main inputs and each of these main inputs consisted of 5 - 10 sub inputs and tertiary inputs were also defined. The process tree ensures that the overall safety of the system has been considered. Several possible failure modes were identified and were relevant to the design, construction, commissioning and operating phases of the facility. The utility of the analysis can be seen in that it has spawned projects prior to installation and has lead to suggestions in the design of the facility. © 2012 American Association of Physicists in Medicine.

Semi-automation of Doppler Spectrum Image Analysis for Grading Aortic Valve Stenosis Severity.

PubMed

Niakšu, O; Balčiunaitė, G; Kizlaitis, R J; Treigys, P

2016-01-01

Doppler echocardiography analysis has become a golden standard in the modern diagnosis of heart diseases. In this paper, we propose a set of techniques for semi-automated parameter extraction for aortic valve stenosis severity grading. The main objectives of the study is to create echocardiography image processing techniques, which minimize manual image processing work of clinicians and leads to reduced human error rates. Aortic valve and left ventricle output tract spectrogram images have been processed and analyzed. A novel method was developed to trace systoles and to extract diagnostic relevant features. The results of the introduced method have been compared to the findings of the participating cardiologists. The experimental results showed the accuracy of the proposed method is comparable to the manual measurement performed by medical professionals. Linear regression analysis of the calculated parameters and the measurements manually obtained by the cardiologists resulted in the strongly correlated values: peak systolic velocity's and mean pressure gradient's R2 both equal to 0.99, their means' differences equal to 0.02 m/s and 4.09 mmHg, respectively, and aortic valve area's R2 of 0.89 with the two methods means' difference of 0.19 mm. The introduced Doppler echocardiography images processing method can be used as a computer-aided assistance in the aortic valve stenosis diagnostics. In our future work, we intend to improve precision of left ventricular outflow tract spectrogram measurements and apply data mining methods to propose a clinical decision support system for diagnosing aortic valve stenosis.

Efficient analysis of three dimensional EUV mask induced imaging artifacts using the waveguide decomposition method

NASA Astrophysics Data System (ADS)

Shao, Feng; Evanschitzky, Peter; Fühner, Tim; Erdmann, Andreas

2009-10-01

This paper employs the Waveguide decomposition method as an efficient rigorous electromagnetic field (EMF) solver to investigate three dimensional mask-induced imaging artifacts in EUV lithography. The major mask diffraction induced imaging artifacts are first identified by applying the Zernike analysis of the mask nearfield spectrum of 2D lines/spaces. Three dimensional mask features like 22nm semidense/dense contacts/posts, isolated elbows and line-ends are then investigated in terms of lithographic results. After that, the 3D mask-induced imaging artifacts such as feature orientation dependent best focus shift, process window asymmetries, and other aberration-like phenomena are explored for the studied mask features. The simulation results can help lithographers to understand the reasons of EUV-specific imaging artifacts and to devise illumination and feature dependent strategies for their compensation in the optical proximity correction (OPC) for EUV masks. At last, an efficient approach using the Zernike analysis together with the Waveguide decomposition technique is proposed to characterize the impact of mask properties for the future OPC process.

Predicting the amount of coke deposition on catalyst pellets through image analysis and soft computing

NASA Astrophysics Data System (ADS)

Zhang, Jingqiong; Zhang, Wenbiao; He, Yuting; Yan, Yong

2016-11-01

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis.

Study of talcum charging status in parallel plate electrostatic separator based on particle trajectory analysis

NASA Astrophysics Data System (ADS)

Yunxiao, CAO; Zhiqiang, WANG; Jinjun, WANG; Guofeng, LI

2018-05-01

Electrostatic separation has been extensively used in mineral processing, and has the potential to separate gangue minerals from raw talcum ore. As for electrostatic separation, the particle charging status is one of important influence factors. To describe the talcum particle charging status in a parallel plate electrostatic separator accurately, this paper proposes a modern images processing method. Based on the actual trajectories obtained from sequence images of particle movement and the analysis of physical forces applied on a charged particle, a numerical model is built, which could calculate the charge-to-mass ratios represented as the charging status of particle and simulate the particle trajectories. The simulated trajectories agree well with the experimental results obtained by images processing. In addition, chemical composition analysis is employed to reveal the relationship between ferrum gangue mineral content and charge-to-mass ratios. Research results show that the proposed method is effective for describing the particle charging status in electrostatic separation.

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

PubMed

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

2007-01-01

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

Machine processing of remotely sensed data - quantifying global process: Models, sensor systems, and analytical methods; Proceedings of the Eleventh International Symposium, Purdue University, West Lafayette, IN, June 25-27, 1985

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

Mengel, S.K.; Morrison, D.B.

1985-01-01

Consideration is given to global biogeochemical issues, image processing, remote sensing of tropical environments, global processes, geology, landcover hydrology, and ecosystems modeling. Topics discussed include multisensor remote sensing strategies, geographic information systems, radars, and agricultural remote sensing. Papers are presented on fast feature extraction; a computational approach for adjusting TM imagery terrain distortions; the segmentation of a textured image by a maximum likelihood classifier; analysis of MSS Landsat data; sun angle and background effects on spectral response of simulated forest canopies; an integrated approach for vegetation/landcover mapping with digital Landsat images; geological and geomorphological studies using an image processing technique;more » and wavelength intensity indices in relation to tree conditions and leaf-nutrient content.« less

A new method of SC image processing for confluence estimation.

PubMed

Soleimani, Sajjad; Mirzaei, Mohsen; Toncu, Dana-Cristina

2017-10-01

Stem cells images are a strong instrument in the estimation of confluency during their culturing for therapeutic processes. Various laboratory conditions, such as lighting, cell container support and image acquisition equipment, effect on the image quality, subsequently on the estimation efficiency. This paper describes an efficient image processing method for cell pattern recognition and morphological analysis of images that were affected by uneven background. The proposed algorithm for enhancing the image is based on coupling a novel image denoising method through BM3D filter with an adaptive thresholding technique for improving the uneven background. This algorithm works well to provide a faster, easier, and more reliable method than manual measurement for the confluency assessment of stem cell cultures. The present scheme proves to be valid for the prediction of the confluency and growth of stem cells at early stages for tissue engineering in reparatory clinical surgery. The method used in this paper is capable of processing the image of the cells, which have already contained various defects due to either personnel mishandling or microscope limitations. Therefore, it provides proper information even out of the worst original images available. Copyright © 2017 Elsevier Ltd. All rights reserved.

Imaging energy landscapes with concentrated diffusing colloidal probes

NASA Astrophysics Data System (ADS)

Bahukudumbi, Pradipkumar; Bevan, Michael A.

2007-06-01

The ability to locally interrogate interactions between particles and energetically patterned surfaces provides essential information to design, control, and optimize template directed self-assembly processes. Although numerous techniques are capable of characterizing local physicochemical surface properties, no current method resolves interactions between colloids and patterned surfaces on the order of the thermal energy kT, which is the inherent energy scale of equilibrium self-assembly processes. Here, the authors describe video microscopy measurements and an inverse Monte Carlo analysis of diffusing colloidal probes as a means to image three dimensional free energy and potential energy landscapes due to physically patterned surfaces. In addition, they also develop a consistent analysis of self-diffusion in inhomogeneous fluids of concentrated diffusing probes on energy landscapes, which is important to the temporal imaging process and to self-assembly kinetics. Extension of the concepts developed in this work suggests a general strategy to image multidimensional and multiscale physical, chemical, and biological surfaces using a variety of diffusing probes (i.e., molecules, macromolecules, nanoparticles, and colloids).

Analysis of x-ray hand images for bone age assessment

NASA Astrophysics Data System (ADS)

Serrat, Joan; Vitria, Jordi M.; Villanueva, Juan J.

1990-09-01

In this paper we describe a model-based system for the assessment of skeletal maturity on hand radiographs by the TW2 method. The problem consists in classiflying a set of bones appearing in an image in one of several stages described in an atlas. A first approach consisting in pre-processing segmentation and classification independent phases is also presented. However it is only well suited for well contrasted low noise images without superimposed bones were the edge detection by zero crossing of second directional derivatives is able to extract all bone contours maybe with little gaps and few false edges on the background. Hence the use of all available knowledge about the problem domain is needed to build a rather general system. We have designed a rule-based system for narrow down the rank of possible stages for each bone and guide the analysis process. It calls procedures written in conventional languages for matching stage models against the image and getting features needed in the classification process.

Automated processing for proton spectroscopic imaging using water reference deconvolution.

PubMed

Maudsley, A A; Wu, Z; Meyerhoff, D J; Weiner, M W

1994-06-01

Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

Image processing developments and applications for water quality monitoring and trophic state determination

NASA Technical Reports Server (NTRS)

Blackwell, R. J.

1982-01-01

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics.

Combined Acquisition/Processing For Data Reduction

NASA Astrophysics Data System (ADS)

Kruger, Robert A.

1982-01-01

Digital image processing systems necessarily consist of three components: acquisition, storage/retrieval and processing. The acquisition component requires the greatest data handling rates. By coupling together the acquisition witn some online hardwired processing, data rates and capacities for short term storage can be reduced. Furthermore, long term storage requirements can be reduced further by appropriate processing and editing of image data contained in short term memory. The net result could be reduced performance requirements for mass storage, processing and communication systems. Reduced amounts of data also snouid speed later data analysis and diagnostic decision making.

Failure mode and effects analysis and fault tree analysis of surface image guided cranial radiosurgery.

PubMed

Manger, Ryan P; Paxton, Adam B; Pawlicki, Todd; Kim, Gwe-Ya

2015-05-01

Surface image guided, Linac-based radiosurgery (SIG-RS) is a modern approach for delivering radiosurgery that utilizes optical stereoscopic imaging to monitor the surface of the patient during treatment in lieu of using a head frame for patient immobilization. Considering the novelty of the SIG-RS approach and the severity of errors associated with delivery of large doses per fraction, a risk assessment should be conducted to identify potential hazards, determine their causes, and formulate mitigation strategies. The purpose of this work is to investigate SIG-RS using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA), report on the effort required to complete the analysis, and evaluate the use of FTA in conjunction with FMEA. A multidisciplinary team was assembled to conduct the FMEA on the SIG-RS process. A process map detailing the steps of the SIG-RS was created to guide the FMEA. Failure modes were determined for each step in the SIG-RS process, and risk priority numbers (RPNs) were estimated for each failure mode to facilitate risk stratification. The failure modes were ranked by RPN, and FTA was used to determine the root factors contributing to the riskiest failure modes. Using the FTA, mitigation strategies were formulated to address the root factors and reduce the risk of the process. The RPNs were re-estimated based on the mitigation strategies to determine the margin of risk reduction. The FMEA and FTAs for the top two failure modes required an effort of 36 person-hours (30 person-hours for the FMEA and 6 person-hours for two FTAs). The SIG-RS process consisted of 13 major subprocesses and 91 steps, which amounted to 167 failure modes. Of the 91 steps, 16 were directly related to surface imaging. Twenty-five failure modes resulted in a RPN of 100 or greater. Only one of these top 25 failure modes was specific to surface imaging. The riskiest surface imaging failure mode had an overall RPN-rank of eighth. Mitigation strategies for the top failure mode decreased the RPN from 288 to 72. Based on the FMEA performed in this work, the use of surface imaging for monitoring intrafraction position in Linac-based stereotactic radiosurgery (SRS) did not greatly increase the risk of the Linac-based SRS process. In some cases, SIG helped to reduce the risk of Linac-based RS. The FMEA was augmented by the use of FTA since it divided the failure modes into their fundamental components, which simplified the task of developing mitigation strategies.

The image enhancement and region of interest extraction of lobster-eye X-ray dangerous material inspection system

NASA Astrophysics Data System (ADS)

Zhan, Qi; Wang, Xin; Mu, Baozhong; Xu, Jie; Xie, Qing; Li, Yaran; Chen, Yifan; He, Yanan

2016-10-01

Dangerous materials inspection is an important technique to confirm dangerous materials crimes. It has significant impact on the prohibition of dangerous materials-related crimes and the spread of dangerous materials. Lobster-Eye Optical Imaging System is a kind of dangerous materials detection device which mainly takes advantage of backscatter X-ray. The strength of the system is its applicability to access only one side of an object, and to detect dangerous materials without disturbing the surroundings of the target material. The device uses Compton scattered x-rays to create computerized outlines of suspected objects during security detection process. Due to the grid structure of the bionic object glass, which imitate the eye of a lobster, grids contribute to the main image noise during the imaging process. At the same time, when used to inspect structured or dense materials, the image is plagued by superposition artifacts and limited by attenuation and noise. With the goal of achieving high quality images which could be used for dangerous materials detection and further analysis, we developed effective image process methods applied to the system. The first aspect of the image process is the denoising and enhancing edge contrast process, during the process, we apply deconvolution algorithm to remove the grids and other noises. After image processing, we achieve high signal-to-noise ratio image. The second part is to reconstruct image from low dose X-ray exposure condition. We developed a kind of interpolation method to achieve the goal. The last aspect is the region of interest (ROI) extraction process, which could be used to help identifying dangerous materials mixed with complex backgrounds. The methods demonstrated in the paper have the potential to improve the sensitivity and quality of x-ray backscatter system imaging.

Evaluation of aortic contractility based on analysis of CT images of the heart

NASA Astrophysics Data System (ADS)

DzierŻak, RóŻa; Maciejewski, Ryszard; Uhlig, Sebastian

2017-08-01

The paper presents a method to assess the aortic contractility based on the analysis of CT images of the heart. This is an alternative method that can be used for patients who cannot be examined by using echocardiography. Usage of medical imaging application for DICOM file processing allows to evaluate the aortic cross section during systole and diastole. It makes possible to assess the level of aortic contractility.

Real-time processing of interferograms for monitoring protein crystal growth on the Space Station

NASA Technical Reports Server (NTRS)

Choudry, A.; Dupuis, N.

1988-01-01

The possibility of using microscopic interferometric techniques to monitor the growth of protein crystals on the Space Station is studied. Digital image processing techniques are used to develop a system for the real-time analysis of microscopic interferograms of nucleation sites during protein crystal growth. Features of the optical setup and the image processing system are discussed and experimental results are presented.

Cervical screening programmes: can automation help? Evidence from systematic reviews, an economic analysis and a simulation modelling exercise applied to the UK.

PubMed

Willis, B H; Barton, P; Pearmain, P; Bryan, S; Hyde, C

2005-03-01

To assess the effectiveness and cost-effectiveness of adding automated image analysis to cervical screening programmes. Searching of all major electronic databases to the end of 2000 was supplemented by a detailed survey for unpublished UK literature. Four systematic reviews were conducted according to recognised guidance. The review of 'clinical effectiveness' included studies assessing reproducibility and impact on health outcomes and processes in addition to evaluations of test accuracy. A discrete event simulation model was developed, although the economic evaluation ultimately relied on a cost-minimisation analysis. The predominant finding from the systematic reviews was the very limited amount of rigorous primary research. None of the included studies refers to the only commercially available automated image analysis device in 2002, the AutoPap Guided Screening (GS) System. The results of the included studies were debatably most compatible with automated image analysis being equivalent in test performance to manual screening. Concerning process, there was evidence that automation does lead to reductions in average slide processing times. In the PRISMATIC trial this was reduced from 10.4 to 3.9 minutes, a statistically significant and practically important difference. The economic evaluation tentatively suggested that the AutoPap GS System may be efficient. The key proviso is that credible data become available to support that the AutoPap GS System has test performance and processing times equivalent to those obtained for PAPNET. The available evidence is still insufficient to recommend implementation of automated image analysis systems. The priority for action remains further research, particularly the 'clinical effectiveness' of the AutoPap GS System. Assessing the cost-effectiveness of introducing automation alongside other approaches is also a priority.

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID:29095927

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.

Status analysis of keyhole bottom in laser-MAG hybrid welding process.

PubMed

Wang, Lin; Gao, Xiangdong; Chen, Ziqin

2018-01-08

The keyhole status is a determining factor of weld quality in laser-metal active gas arc (MAG) hybrid welding process. For a better evaluation of the hybrid welding process, three different penetration welding experiments: partial penetration, normal penetration (or full penetration), and excessive penetration were conducted in this work. The instantaneous visual phenomena including metallic vapor, spatters and keyhole of bottom surface were used to evaluate the keyhole status by a double high-speed camera system. The Fourier transform was applied on the bottom weld pool image for removing the image noise around the keyhole, and then the bottom weld pool image was reconstructed through the inverse Fourier transform. Lastly, the keyhole bottom was extracted from the de-noised bottom weld pool image. By analyzing the visual features of the laser-MAG hybrid welding process, mechanism of the closed and opened keyhole bottom were revealed. The results show that the stable opened or closed status of keyhole bottom is directly affected by the MAG droplet transition in the normal penetration welding process, and the unstable opened or closed status of keyhole bottom would appear in excessive penetration welding and partial penetration welding. The analysis method proposed in this paper could be used to monitor the keyhole stability in laser-MAG hybrid welding process.

Computer analysis of arteriograms

NASA Technical Reports Server (NTRS)

Selzer, R. H.; Armstrong, J. H.; Beckenbach, E. B.; Blankenhorn, D. H.; Crawford, D. W.; Brooks, S. H.; Sanmarco, M. E.

1977-01-01

A computer system has been developed to quantify the degree of atherosclerosis in the human femoral artery. The analysis involves first scanning and digitizing angiographic film, then tracking the outline of the arterial image and finally computing the relative amount of roughness or irregularity in the vessel wall. The image processing system and method are described.

Optical disk processing of solar images.

NASA Astrophysics Data System (ADS)

Title, A.; Tarbell, T.

The current generation of space and ground-based experiments in solar physics produces many megabyte-sized image data arrays. Optical disk technology is the leading candidate for convenient analysis, distribution, and archiving of these data. The authors have been developing data analysis procedures which use both analog and digital optical disks for the study of solar phenomena.

Quantitative real-time analysis of collective cancer invasion and dissemination

NASA Astrophysics Data System (ADS)

Ewald, Andrew J.

2015-05-01

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

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

PubMed

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

1997-01-01

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

Real-time optical image processing techniques

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang

1988-01-01

Nonlinear real-time optical processing on spatial pulse frequency modulation has been pursued through the analysis, design, and fabrication of pulse frequency modulated halftone screens and the modification of micro-channel spatial light modulators (MSLMs). Micro-channel spatial light modulators are modified via the Fabry-Perot method to achieve the high gamma operation required for non-linear operation. Real-time nonlinear processing was performed using the halftone screen and MSLM. The experiments showed the effectiveness of the thresholding and also showed the needs of higher SBP for image processing. The Hughes LCLV has been characterized and found to yield high gamma (about 1.7) when operated in low frequency and low bias mode. Cascading of two LCLVs should also provide enough gamma for nonlinear processing. In this case, the SBP of the LCLV is sufficient but the uniformity of the LCLV needs improvement. These include image correlation, computer generation of holograms, pseudo-color image encoding for image enhancement, and associative-retrieval in neural processing. The discovery of the only known optical method for dynamic range compression of an input image in real-time by using GaAs photorefractive crystals is reported. Finally, a new architecture for non-linear multiple sensory, neural processing has been suggested.

Comparison of binary mask defect printability analysis using virtual stepper system and aerial image microscope system

NASA Astrophysics Data System (ADS)

Phan, Khoi A.; Spence, Chris A.; Dakshina-Murthy, S.; Bala, Vidya; Williams, Alvina M.; Strener, Steve; Eandi, Richard D.; Li, Junling; Karklin, Linard

1999-12-01

As advanced process technologies in the wafer fabs push the patterning processes toward lower k1 factor for sub-wavelength resolution printing, reticles are required to use optical proximity correction (OPC) and phase-shifted mask (PSM) for resolution enhancement. For OPC/PSM mask technology, defect printability is one of the major concerns. Current reticle inspection tools available on the market sometimes are not capable of consistently differentiating between an OPC feature and a true random defect. Due to the process complexity and high cost associated with the making of OPC/PSM reticles, it is important for both mask shops and lithography engineers to understand the impact of different defect types and sizes to the printability. Aerial Image Measurement System (AIMS) has been used in the mask shops for a number of years for reticle applications such as aerial image simulation and transmission measurement of repaired defects. The Virtual Stepper System (VSS) provides an alternative method to do defect printability simulation and analysis using reticle images captured by an optical inspection or review system. In this paper, pre- programmed defects and repairs from a Defect Sensitivity Monitor (DSM) reticle with 200 nm minimum features (at 1x) will be studied for printability. The simulated resist lines by AIMS and VSS are both compared to SEM images of resist wafers qualitatively and quantitatively using CD verification.Process window comparison between unrepaired and repaired defects for both good and bad repair cases will be shown. The effect of mask repairs to resist pattern images for the binary mask case will be discussed. AIMS simulation was done at the International Sematech, Virtual stepper simulation at Zygo and resist wafers were processed at AMD-Submicron Development Center using a DUV lithographic process for 0.18 micrometer Logic process technology.

Improvement of Speckle Contrast Image Processing by an Efficient Algorithm.

PubMed

Steimers, A; Farnung, W; Kohl-Bareis, M

2016-01-01

We demonstrate an efficient algorithm for the temporal and spatial based calculation of speckle contrast for the imaging of blood flow by laser speckle contrast analysis (LASCA). It reduces the numerical complexity of necessary calculations, facilitates a multi-core and many-core implementation of the speckle analysis and enables an independence of temporal or spatial resolution and SNR. The new algorithm was evaluated for both spatial and temporal based analysis of speckle patterns with different image sizes and amounts of recruited pixels as sequential, multi-core and many-core code.

Bubble structure evaluation method of sponge cake by using image morphology

NASA Astrophysics Data System (ADS)

Kato, Kunihito; Yamamoto, Kazuhiko; Nonaka, Masahiko; Katsuta, Yukiyo; Kasamatsu, Chinatsu

2007-01-01

Nowadays, many evaluation methods for food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that have been used for the quality evaluation recently. The goal of our research is structure evaluation of sponge cake by using the image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner, because the depth of field of this type scanner is very shallow. Therefore the bubble region of the surface has low gray scale value, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. The input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

PubMed

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Quasi real-time analysis of mixed-phase clouds using interferometric out-of-focus imaging: development of an algorithm to assess liquid and ice water content

NASA Astrophysics Data System (ADS)

Lemaitre, P.; Brunel, M.; Rondeau, A.; Porcheron, E.; Gréhan, G.

2015-12-01

According to changes in aircraft certifications rules, instrumentation has to be developed to alert the flight crews of potential icing conditions. The technique developed needs to measure in real time the amount of ice and liquid water encountered by the plane. Interferometric imaging offers an interesting solution: It is currently used to measure the size of regular droplets, and it can further measure the size of irregular particles from the analysis of their speckle-like out-of-focus images. However, conventional image processing needs to be speeded up to be compatible with the real-time detection of icing conditions. This article presents the development of an optimised algorithm to accelerate image processing. The algorithm proposed is based on the detection of each interferogram with the use of the gradient pair vector method. This method is shown to be 13 times faster than the conventional Hough transform. The algorithm is validated on synthetic images of mixed phase clouds, and finally tested and validated in laboratory conditions. This algorithm should have important applications in the size measurement of droplets and ice particles for aircraft safety, cloud microphysics investigation, and more generally in the real-time analysis of triphasic flows using interferometric particle imaging.

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.

Thermal image analysis using the serpentine method

NASA Astrophysics Data System (ADS)

Koprowski, Robert; Wilczyński, Sławomir

2018-03-01

Thermal imaging is an increasingly widespread alternative to other imaging methods. As a supplementary method in diagnostics, it can be used both statically and with dynamic temperature changes. The paper proposes a new image analysis method that allows for the acquisition of new diagnostic information as well as object segmentation. The proposed serpentine analysis uses known and new methods of image analysis and processing proposed by the authors. Affine transformations of an image and subsequent Fourier analysis provide a new diagnostic quality. The method is fully repeatable and automatic and independent of inter-individual variability in patients. The segmentation results are by 10% better than those obtained from the watershed method and the hybrid segmentation method based on the Canny detector. The first and second harmonics of serpentine analysis enable to determine the type of temperature changes in the region of interest (gradient, number of heat sources etc.). The presented serpentine method provides new quantitative information on thermal imaging and more. Since it allows for image segmentation and designation of contact points of two and more heat sources (local minimum), it can be used to support medical diagnostics in many areas of medicine.

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

PubMed

Kawel-Boehm, Nadine; Bluemke, David A

2017-07-01

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

Process simulation in digital camera system

NASA Astrophysics Data System (ADS)

Toadere, Florin

2012-06-01

The goal of this paper is to simulate the functionality of a digital camera system. The simulations cover the conversion from light to numerical signal and the color processing and rendering. We consider the image acquisition system to be linear shift invariant and axial. The light propagation is orthogonal to the system. We use a spectral image processing algorithm in order to simulate the radiometric properties of a digital camera. In the algorithm we take into consideration the transmittances of the: light source, lenses, filters and the quantum efficiency of a CMOS (complementary metal oxide semiconductor) sensor. The optical part is characterized by a multiple convolution between the different points spread functions of the optical components. We use a Cooke triplet, the aperture, the light fall off and the optical part of the CMOS sensor. The electrical part consists of the: Bayer sampling, interpolation, signal to noise ratio, dynamic range, analog to digital conversion and JPG compression. We reconstruct the noisy blurred image by blending different light exposed images in order to reduce the photon shot noise, also we filter the fixed pattern noise and we sharpen the image. Then we have the color processing blocks: white balancing, color correction, gamma correction, and conversion from XYZ color space to RGB color space. For the reproduction of color we use an OLED (organic light emitting diode) monitor. The analysis can be useful to assist students and engineers in image quality evaluation and imaging system design. Many other configurations of blocks can be used in our analysis.

White Light Optical Processing and Holography.

DTIC Science & Technology

1982-10-01

of the object beam. The major problem in image deblurring is noise in the dclurred image. There are two kinds of noise : S (a) False images. The...reducing the noise this work is described in Sec. 3. 2. We addressed the bias buildup and SNR in incoherent optical processing, making an analysis that...system is generally better than the coherent for SNR. Thus, if we have a sensitive, low- noise detector at the output of an incoherent system, we should

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

PubMed

Markiewicz, Tomasz

2011-03-30

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