Expression and localization of ionotropic glutamate receptor subunits in the goldfish retina--an in situ hybridization and immunocytochemical study

NARCIS (Netherlands)

Vandenbranden, C. A.; Kamphuis, W.; Nunes Cardozo, B.; Kamermans, M.

2000-01-01

The expression and distribution of AMPA, kainate and NMDA glutamate receptor subunits was studied in the goldfish retina. For the immunocytochemical localization of the AMPA receptor antisera against GluR2, GluR2/3 and GluR4 were used, and for in situ hybridization rat specific probes for GluR1 and

Immunocytochemical characterization of primary cell culture in canine transmissible venereal tumor

Directory of Open Access Journals (Sweden)

Luis M.M. Flórez

Full Text Available Abstract: Immunochemistry with anti-vimentin, anti-lysozyme, anti-alpha 1 antitrypsin, anti-CD3 and anti-CD79α antibodies has been used for characterization of primary cell culture in the transmissible venereal tumor (TVT. Samples for primary cell culture and immunohistochemistry assays were taken from eight dogs with cytological and clinical diagnosis of TVT. To validate the immunochemical results in the primary cell culture of TVT, a chromosome count was performed. For the statistical analysis, the Mann-Whitney test with p<0.05 was used. TVT tissues and culture cells showed intense anti-vimentin immunoreactivity, lightly to moderate immunoreactivity for anti-lysozyme, and mild for anti-alpha-antitrypsin. No marking was achieved for CD3 and CD79α. All culture cells showed chromosomes variable number of 56 to 68. This is the first report on the use of immunocytochemical characterization in cell culture of TVT. Significant statistic difference between immunochemistry in tissue and culture cell was not established, what suggests that the use of this technique may provide greater certainty for the confirmation of tumors in the primary culture. This fact is particularly important because in vitro culture of tumor tissues has been increasingly used to provide quick access to drug efficacy and presents relevant information to identify potential response to anticancer medicine; so it is possible to understand the behavior of the tumor.

Study of osteoporosis through the measurement of bone density, trace elements, biomechanical properties and immunocytochemicals

International Nuclear Information System (INIS)

Aras, N.K.; Korkusuz, F.; Akkas, N.; Laleli, Y.; Kuscu, L.; Gunel, U.

1996-01-01

Osteoporosis is defined as an absolute decrease in the amount of bone to a level below required for mechanical support. It is an important bone disease in elderly people in many countries. Unfortunately, there is no reliable statistical data in Turkey for the incidence of osteoporosis. A decrease in bone mass is the important cause in fractures in osteoporosis. Therefore, we intend to study both bone density and other variables such as trace elements, biomechanical properties and other immunocytochemicals in bone, all combined might give an information about the cause and prevention of osteoporosis. (author)

Retinal Imaging and Image Analysis

Science.gov (United States)

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

Immunocytochemical localization of the [3H]estradiol-binding protein in rat pancreatic acinar cells

International Nuclear Information System (INIS)

Grossman, A.; Oppenheim, J.; Grondin, G.; St Jean, P.; Beaudoin, A.R.

1989-01-01

Significant amounts of an estradiol-binding protein (EBP) are present in pancreatic acinar cells. This protein differs from the one found in female reproductive tissues and secondary sex organs (which is commonly referred to as estrogen receptor). EBP has now been purified from rat pancreas and was used as an antigen to induce polyclonal antibodies in rabbits. The antiserum obtained was purified initially by ammonium sulfate fractionation and then still further by interaction with a protein fraction from pancreas that was devoid of estradiol-binding activity. The latter procedure was used to precipitate nonspecific immunoglobulin Gs. Western blot analysis demonstrated that the anti-EBP antibody reacted specifically with a doublet of protein bands having mol wt of 64K and 66K. When this purified antibody was used as an immunocytochemical probe in conjunction with protein-A-gold, acinar cells were labeled on the surface of the endoplasmic reticulum, on the plasma membrane, and in mitochondria. This specific labeling pattern was not observed when preimmune serum was used. No labeling was observed over the nucleus, Golgi apparatus, or zymogen granules with purified anti-EBP antibodies. The unexpected distribution of EBP in both the endoplasmic reticulum and mitochondria is discussed

Image analysis

International Nuclear Information System (INIS)

Berman, M.; Bischof, L.M.; Breen, E.J.; Peden, G.M.

1994-01-01

This paper provides an overview of modern image analysis techniques pertinent to materials science. The usual approach in image analysis contains two basic steps: first, the image is segmented into its constituent components (e.g. individual grains), and second, measurement and quantitative analysis are performed. Usually, the segmentation part of the process is the harder of the two. Consequently, much of the paper concentrates on this aspect, reviewing both fundamental segmentation tools (commonly found in commercial image analysis packages) and more advanced segmentation tools. There is also a review of the most widely used quantitative analysis methods for measuring the size, shape and spatial arrangements of objects. Many of the segmentation and analysis methods are demonstrated using complex real-world examples. Finally, there is a discussion of hardware and software issues. 42 refs., 17 figs

Ultrasonic image analysis and image-guided interventions.

Science.gov (United States)

Noble, J Alison; Navab, Nassir; Becher, H

2011-08-06

The fields of medical image analysis and computer-aided interventions deal with reducing the large volume of digital images (X-ray, computed tomography, magnetic resonance imaging (MRI), positron emission tomography and ultrasound (US)) to more meaningful clinical information using software algorithms. US is a core imaging modality employed in these areas, both in its own right and used in conjunction with the other imaging modalities. It is receiving increased interest owing to the recent introduction of three-dimensional US, significant improvements in US image quality, and better understanding of how to design algorithms which exploit the unique strengths and properties of this real-time imaging modality. This article reviews the current state of art in US image analysis and its application in image-guided interventions. The article concludes by giving a perspective from clinical cardiology which is one of the most advanced areas of clinical application of US image analysis and describing some probable future trends in this important area of ultrasonic imaging research.

Image formation and image analysis in electron microscopy

International Nuclear Information System (INIS)

Heel, M. van.

1981-01-01

This thesis covers various aspects of image formation and image analysis in electron microscopy. The imaging of relatively strong objects in partially coherent illumination, the coherence properties of thermionic emission sources and the detection of objects in quantum noise limited images are considered. IMAGIC, a fast, flexible and friendly image analysis software package is described. Intelligent averaging of molecular images is discussed. (C.F.)

Utility of an immunocytochemical assay using aqueous humor in the diagnosis of feline infectious peritonitis.

Science.gov (United States)

Felten, Sandra; Matiasek, Kaspar; Gruendl, Stefanie; Sangl, Laura; Hartmann, Katrin

2018-01-01

In cats suffering from feline infectious peritonitis (FIP) without effusion, antemortem diagnosis is challenging. Uveitis is common in these cats. It was the aim of this study to evaluate sensitivity and specificity of an immunocytochemical assay (ICC) in aqueous humor of cats suspected of having FIP. The study included 26 cats with immunohistochemically confirmed FIP and 12 control cats for which FIP was suspected due to similar clinical or laboratory changes, but which suffered from other diseases confirmed via histopathology. All aqueous humor samples were collected postmortem by paracentesis. ICC was carried out as avidin-biotin complex method. Sensitivity, specificity, and the overall accuracy including 95% confidence intervals (95% CI) were calculated. Immunocytochemistry was positive in 16 of 25 cats with FIP and 2 of 11 control cats (one cat with lymphoma, one with pulmonary adenocarcinoma). Aqueous humor samples of one cat with FIP and of one control cat were excluded from statistical analysis. Sensitivity was 64.0% (95% CI: 42.5-82.0); specificity 81.8% (95% CI: 48.2-97.7); and overall accuracy 69.4% (95% CI: 51.9-83.7). As false-positive results occurred and specificity is most important in the diagnosis of FIP, the diagnostic utility of ICC in aqueous humor is limited. Further studies are required to clarify the origin of false-positive ICC results. © 2017 American College of Veterinary Ophthalmologists.

Spinal imaging and image analysis

CERN Document Server

Yao, Jianhua

2015-01-01

This book is instrumental to building a bridge between scientists and clinicians in the field of spine imaging by introducing state-of-the-art computational methods in the context of clinical applications.  Spine imaging via computed tomography, magnetic resonance imaging, and other radiologic imaging modalities, is essential for noninvasively visualizing and assessing spinal pathology. Computational methods support and enhance the physician’s ability to utilize these imaging techniques for diagnosis, non-invasive treatment, and intervention in clinical practice. Chapters cover a broad range of topics encompassing radiological imaging modalities, clinical imaging applications for common spine diseases, image processing, computer-aided diagnosis, quantitative analysis, data reconstruction and visualization, statistical modeling, image-guided spine intervention, and robotic surgery. This volume serves a broad audience as  contributions were written by both clinicians and researchers, which reflects the inte...

Image Analysis for X-ray Imaging of Food

DEFF Research Database (Denmark)

Einarsdottir, Hildur

for quality and safety evaluation of food products. In this effort the fields of statistics, image analysis and statistical learning are combined, to provide analytical tools for determining the aforementioned food traits. The work demonstrated includes a quantitative analysis of heat induced changes......X-ray imaging systems are increasingly used for quality and safety evaluation both within food science and production. They offer non-invasive and nondestructive penetration capabilities to image the inside of food. This thesis presents applications of a novel grating-based X-ray imaging technique...... and defect detection in food. Compared to the complex three dimensional analysis of microstructure, here two dimensional images are considered, making the method applicable for an industrial setting. The advantages obtained by grating-based imaging are compared to conventional X-ray imaging, for both foreign...

Shape analysis in medical image analysis

CERN Document Server

Tavares, João

2014-01-01

This book contains thirteen contributions from invited experts of international recognition addressing important issues in shape analysis in medical image analysis, including techniques for image segmentation, registration, modelling and classification, and applications in biology, as well as in cardiac, brain, spine, chest, lung and clinical practice. This volume treats topics such as, anatomic and functional shape representation and matching; shape-based medical image segmentation; shape registration; statistical shape analysis; shape deformation; shape-based abnormity detection; shape tracking and longitudinal shape analysis; machine learning for shape modeling and analysis; shape-based computer-aided-diagnosis; shape-based medical navigation; benchmark and validation of shape representation, analysis and modeling algorithms. This work will be of interest to researchers, students, and manufacturers in the fields of artificial intelligence, bioengineering, biomechanics, computational mechanics, computationa...

Retinal imaging and image analysis

NARCIS (Netherlands)

Abramoff, M.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

Hyperspectral image analysis. A tutorial

International Nuclear Information System (INIS)

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

2015-01-01

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares – Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case. - Highlights: • Comprehensive tutorial of Hyperspectral Image analysis. • Hierarchical discrimination of six classes of plastics containing flame retardant. • Step by step guidelines to perform class-modeling on hyperspectral images. • Fusion of multivariate data analysis and digital image processing methods. • Promising methodology for real-time detection of plastics containing flame retardant.

Medical image registration for analysis

International Nuclear Information System (INIS)

Petrovic, V.

2006-01-01

Full text: Image registration techniques represent a rich family of image processing and analysis tools that aim to provide spatial correspondences across sets of medical images of similar and disparate anatomies and modalities. Image registration is a fundamental and usually the first step in medical image analysis and this paper presents a number of advanced techniques as well as demonstrates some of the advanced medical image analysis techniques they make possible. A number of both rigid and non-rigid medical image alignment algorithms of equivalent and merely consistent anatomical structures respectively are presented. The algorithms are compared in terms of their practical aims, inputs, computational complexity and level of operator (e.g. diagnostician) interaction. In particular, the focus of the methods discussion is placed on the applications and practical benefits of medical image registration. Results of medical image registration on a number of different imaging modalities and anatomies are presented demonstrating the accuracy and robustness of their application. Medical image registration is quickly becoming ubiquitous in medical imaging departments with the results of such algorithms increasingly used in complex medical image analysis and diagnostics. This paper aims to demonstrate at least part of the reason why

The Digital Image Processing And Quantitative Analysis In Microscopic Image Characterization

International Nuclear Information System (INIS)

Ardisasmita, M. Syamsa

2000-01-01

Many electron microscopes although have produced digital images, but not all of them are equipped with a supporting unit to process and analyse image data quantitatively. Generally the analysis of image has to be made visually and the measurement is realized manually. The development of mathematical method for geometric analysis and pattern recognition, allows automatic microscopic image analysis with computer. Image processing program can be used for image texture and structure periodic analysis by the application of Fourier transform. Because the development of composite materials. Fourier analysis in frequency domain become important for measure the crystallography orientation. The periodic structure analysis and crystal orientation are the key to understand many material properties like mechanical strength. stress, heat conductivity, resistance, capacitance and other material electric and magnetic properties. In this paper will be shown the application of digital image processing in microscopic image characterization and analysis in microscopic image

Fine-needle aspiration cytology of postirradiation sarcomas, including angiosarcoma, with immunocytochemical confirmation

Energy Technology Data Exchange (ETDEWEB)

Silverman, J.F.; Lannin, D.L.; Larkin, E.W.; Feldman, P.; Frable, W.J. (East Carolina Univ. School of Medicine, Greenville, NC (USA))

1989-01-01

Postirradiation sarcomas are an unusual but well-recognized late effect of cancer therapy. In this article, a fine-needle aspiration (FNA) series of four cases is presented. There were three female patients and one male patient, with an age range of 28-55 yr (mean, 41). Two of the patients were irradiated for uterine cervical carcinoma while the other two received irradiation for malignant lymphoma. The time interval to the development of the postirradiation sarcoma ranged from 10 to greater than 20 yr. There were a postirradiation synovial sarcoma of the buttock region, malignant fibrous histiocytoma of the bone (femur), and rhabdomyosarcoma and angiosarcoma of the retroperitoneum. A spectrum of cytologic findings was encountered, reflecting the specific types of sarcomas. Immunocytochemical studies performed on the aspirated material from the angiosarcoma demonstrated the utility of immunoperoxidase stains for ULEX europaeus agglutinin-1 (UEA-1) and, to a lesser degree, factor VIII-related antigen antibody, confirming the vascular nature of this malignancy. The FNA findings from all four cases demonstrated cytologic features that allowed recognition of this unusual complication of irradiation treatment. This article confirms the utility of FNA cytology in following patients with previous malignancies and differentiating a postirradiation sarcoma from recurrent carcinoma.

Fine-needle aspiration cytology of postirradiation sarcomas, including angiosarcoma, with immunocytochemical confirmation

International Nuclear Information System (INIS)

Silverman, J.F.; Lannin, D.L.; Larkin, E.W.; Feldman, P.; Frable, W.J.

1989-01-01

Postirradiation sarcomas are an unusual but well-recognized late effect of cancer therapy. In this article, a fine-needle aspiration (FNA) series of four cases is presented. There were three female patients and one male patient, with an age range of 28-55 yr (mean, 41). Two of the patients were irradiated for uterine cervical carcinoma while the other two received irradiation for malignant lymphoma. The time interval to the development of the postirradiation sarcoma ranged from 10 to greater than 20 yr. There were a postirradiation synovial sarcoma of the buttock region, malignant fibrous histiocytoma of the bone (femur), and rhabdomyosarcoma and angiosarcoma of the retroperitoneum. A spectrum of cytologic findings was encountered, reflecting the specific types of sarcomas. Immunocytochemical studies performed on the aspirated material from the angiosarcoma demonstrated the utility of immunoperoxidase stains for ULEX europaeus agglutinin-1 (UEA-1) and, to a lesser degree, factor VIII-related antigen antibody, confirming the vascular nature of this malignancy. The FNA findings from all four cases demonstrated cytologic features that allowed recognition of this unusual complication of irradiation treatment. This article confirms the utility of FNA cytology in following patients with previous malignancies and differentiating a postirradiation sarcoma from recurrent carcinoma

Oncological image analysis.

Science.gov (United States)

Brady, Sir Michael; Highnam, Ralph; Irving, Benjamin; Schnabel, Julia A

2016-10-01

Cancer is one of the world's major healthcare challenges and, as such, an important application of medical image analysis. After a brief introduction to cancer, we summarise some of the major developments in oncological image analysis over the past 20 years, but concentrating those in the authors' laboratories, and then outline opportunities and challenges for the next decade. Copyright © 2016 Elsevier B.V. All rights reserved.

Gabor Analysis for Imaging

DEFF Research Database (Denmark)

Christensen, Ole; Feichtinger, Hans G.; Paukner, Stephan

2015-01-01

, it characterizes a function by its transform over phase space, which is the time–frequency plane (TF-plane) in a musical context or the location–wave-number domain in the context of image processing. Since the transition from the signal domain to the phase space domain introduces an enormous amount of data...... of the generalities relevant for an understanding of Gabor analysis of functions on Rd. We pay special attention to the case d = 2, which is the most important case for image processing and image analysis applications. The chapter is organized as follows. Section 2 presents central tools from functional analysis......, the application of Gabor expansions to image representation is considered in Sect. 6....

Artificial intelligence and medical imaging. Expert systems and image analysis

International Nuclear Information System (INIS)

Wackenheim, A.; Zoellner, G.; Horviller, S.; Jacqmain, T.

1987-01-01

This paper gives an overview on the existing systems for automated image analysis and interpretation in medical imaging, especially in radiology. The example of ORFEVRE, the system for the analysis of CAT-scan images of the cervical triplet (c3-c5) by image analysis and subsequent expert-system is given and discussed in detail. Possible extensions are described [fr

Immunocytochemical identification of adenohypophyseal cells in the pirarucu (Arapaima gigas), an Amazonian basal teleost.

Science.gov (United States)

Borella, M I; Venturieri, R; Mancera, J M

2009-03-01

The adenohypophysis (AH) of juvenile pirarucu (Arapaima gigas), a representative species of the Osteoglossomorpha (bonytongue fishes, one of the oldest living groups of the teleosts), was studied using histochemical and immunocytochemical methods. The AH is comprised of the pars distalis (PD), without a clear distinction between rostral pars distalis (RPD) and proximal pars distalis (PPD), and the pars intermedia (PI). The neurohypophysis (NH) is positioned on top of the PD and penetrates and branches into the PI. In the most rostral dorsal portion of the PD, adrenocorticotropic cells and fusiform gonadotropic cells were found. In the central PD, scarce prolactin-producing cells and growth-hormone-producing cells were located mainly in the dorsal part, whereas round gonadotropic cells were abundant in the ventral portion of this region. Human thyrotropin immunoreactive cells were not found in the entire AH. In the PI, melanotropic, some adrenocorticotropic, and somatolactin-producing cells were located intermingled surrounding the neurohypophyseal branches. Our results showed that the A. gigas pituitary has some basal characteristics between the ancient Actinopterygii and the more derived teleosts.

Microscopy image segmentation tool: Robust image data analysis

Energy Technology Data Exchange (ETDEWEB)

Valmianski, Ilya, E-mail: ivalmian@ucsd.edu; Monton, Carlos; Schuller, Ivan K. [Department of Physics and Center for Advanced Nanoscience, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093 (United States)

2014-03-15

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

Microscopy image segmentation tool: Robust image data analysis

Science.gov (United States)

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

2014-03-01

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

Microscopy image segmentation tool: Robust image data analysis

International Nuclear Information System (INIS)

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

2014-01-01

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

Hyperspectral image analysis. A tutorial

DEFF Research Database (Denmark)

Amigo Rubio, Jose Manuel; Babamoradi, Hamid; Elcoroaristizabal Martin, Saioa

2015-01-01

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

Imaging mass spectrometry statistical analysis.

Science.gov (United States)

Jones, Emrys A; Deininger, Sören-Oliver; Hogendoorn, Pancras C W; Deelder, André M; McDonnell, Liam A

2012-08-30

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research. Copyright © 2012 Elsevier B.V. All rights reserved.

Image analysis and modeling in medical image computing. Recent developments and advances.

Science.gov (United States)

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

Quantitative image analysis of synovial tissue

NARCIS (Netherlands)

van der Hall, Pascal O.; Kraan, Maarten C.; Tak, Paul Peter

2007-01-01

Quantitative image analysis is a form of imaging that includes microscopic histological quantification, video microscopy, image analysis, and image processing. Hallmarks are the generation of reliable, reproducible, and efficient measurements via strict calibration and step-by-step control of the

[Immunocytochemical studies on the phase of differentiation of hatching gland cells in brine shrimp, Artemia salina].

Science.gov (United States)

Li, Ling; Fan, Ting Jun; Wang, Xiao Feng; Cong, Ri Shan; Yu, Qiu Tao; Zhong, Qi Wang

2004-04-01

Hatching enzyme (HE), synthesized in hatching gland cells (HGCs), plays vital roles in animal hatching. Immunocytochemical techniques employing anti-GST-UVS.2 antiserum, prepared from Xenopus HE and with specificity to brine shrimp HE, were first used to investigate the differentiation and variability of hatching gland cells (HGCs) in the hatching process of embryos of brine shrimp, Artemia salina, in this study. HGCs with immunoreactivity to anti-GST-UVS.2 antiserum were identified, for the first time, in brine shrimp embryos during hatching process. Immunocytochemical staining results showed that, (1) HE-positive immunoreactivity is really specific to Artemia HE, and its appearance and disappearance are closely correlated with the hatching process of Artemia salina. (2) Artemia HGCs, first appeared in embryos 5 hours before hatching and disappeared 4 hours after hatching, were also a transient type of cells, with an existence period of 9 hours. (3) The head portion of Artemia embryo is probably the initial position of HE secretion, and likely to be the main position of HE secretion as well. The detailed process and mechanism need to be studied. (4) The appearance of HGCs is in a synchronous mode from places all over the embryos, and their disappearance is also in a synchronous mode. (5) The number of HGCs increased gradually along with embryo development process and reached a maximum number at hatching. Contrarily, the number of HGCs decreased gradually after hatching, and HGCs disappeared 5 hours after hatching. However, the intensity of HE-positive reaction was almost at the same level at the period of HGCs'presence. (6) Artemia HGCs were distributed throughout the body of embryos at all time during their presence. Therefore, it can concluded that Artemia HGCs, as a transient type of cells, first appeared in embryos 4 hours before hatching and disappeared in embryos 5 hours after hatching, and with distinguished patterns of appearance, disappearance and

Stochastic geometry for image analysis

CERN Document Server

Descombes, Xavier

2013-01-01

This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are  described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed.  Numerous applications, covering remote sensing images, biological and medical imaging, are detailed.  This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.

Transfer function analysis of radiographic imaging systems

International Nuclear Information System (INIS)

Metz, C.E.; Doi, K.

1979-01-01

The theoretical and experimental aspects of the techniques of transfer function analysis used in radiographic imaging systems are reviewed. The mathematical principles of transfer function analysis are developed for linear, shift-invariant imaging systems, for the relation between object and image and for the image due to a sinusoidal plane wave object. The other basic mathematical principle discussed is 'Fourier analysis' and its application to an input function. Other aspects of transfer function analysis included are alternative expressions for the 'optical transfer function' of imaging systems and expressions are derived for both serial and parallel transfer image sub-systems. The applications of transfer function analysis to radiographic imaging systems are discussed in relation to the linearisation of the radiographic imaging system, the object, the geometrical unsharpness, the screen-film system unsharpness, other unsharpness effects and finally noise analysis. It is concluded that extensive theoretical, computer simulation and experimental studies have demonstrated that the techniques of transfer function analysis provide an accurate and reliable means for predicting and understanding the effects of various radiographic imaging system components in most practical diagnostic medical imaging situations. (U.K.)

Image Analysis

DEFF Research Database (Denmark)

The 19th Scandinavian Conference on Image Analysis was held at the IT University of Copenhagen in Denmark during June 15-17, 2015. The SCIA conference series has been an ongoing biannual event for more than 30 years and over the years it has nurtured a world-class regional research and development...... area within the four participating Nordic countries. It is a regional meeting of the International Association for Pattern Recognition (IAPR). We would like to thank all authors who submitted works to this year’s SCIA, the invited speakers, and our Program Committee. In total 67 papers were submitted....... The topics of the accepted papers range from novel applications of vision systems, pattern recognition, machine learning, feature extraction, segmentation, 3D vision, to medical and biomedical image analysis. The papers originate from all the Scandinavian countries and several other European countries...

Digital image analysis

DEFF Research Database (Denmark)

Riber-Hansen, Rikke; Vainer, Ben; Steiniche, Torben

2012-01-01

Digital image analysis (DIA) is increasingly implemented in histopathological research to facilitate truly quantitative measurements, decrease inter-observer variation and reduce hands-on time. Originally, efforts were made to enable DIA to reproduce manually obtained results on histological slides...... reproducibility, application of stereology-based quantitative measurements, time consumption, optimization of histological slides, regions of interest selection and recent developments in staining and imaging techniques....

Image sequence analysis

CERN Document Server

1981-01-01

The processing of image sequences has a broad spectrum of important applica­ tions including target tracking, robot navigation, bandwidth compression of TV conferencing video signals, studying the motion of biological cells using microcinematography, cloud tracking, and highway traffic monitoring. Image sequence processing involves a large amount of data. However, because of the progress in computer, LSI, and VLSI technologies, we have now reached a stage when many useful processing tasks can be done in a reasonable amount of time. As a result, research and development activities in image sequence analysis have recently been growing at a rapid pace. An IEEE Computer Society Workshop on Computer Analysis of Time-Varying Imagery was held in Philadelphia, April 5-6, 1979. A related special issue of the IEEE Transactions on Pattern Anal­ ysis and Machine Intelligence was published in November 1980. The IEEE Com­ puter magazine has also published a special issue on the subject in 1981. The purpose of this book ...

Immunocytochemical localization of estrogen receptors in the normal male and female canine urinary tract and prostate

International Nuclear Information System (INIS)

Schulze, H.; Barrack, E.R.

1987-01-01

We have used the monoclonal estrogen receptor (ER) antibody H222Sp gamma to localize ER by immunocytochemistry in frozen sections of the normal canine urinary tract of both sexes and of the normal prostate of the male. Striking regional heterogeneity of ER location was observed. In the urinary tract, specific ER staining was confined to nuclei of the transitional epithelium (mucosa) and subjacent stroma (submucosa) of the prostatic urethra in the male dog and of the proximal urethra in the female dog. In both sexes there was a gradient of ER staining intensity along these urethral segments. In the male, ER staining intensity was highest in the region of the verumontanum. The pattern and intensity of staining were similar in the male prostatic urethra and female proximal urethra, indicating a similar concentration of ER in these tissues, which have the same embryological origin. No specific staining was found in the kidney, ureter, bladder, or distal urethra of either sex. In the normal prostate, specific immunocytochemical ER staining was confined to nuclei of the prostatic stroma and prostatic ductal epithelium. Specific staining intensity appeared to be higher in the periurethral region of the prostate than in the periphery. No specific staining was found in the acinar epithelium of the prostate. Based on overall staining intensity there appeared to be a higher concentration of ER in the urethra than in the prostate. Scatchard analysis of [ 3 H]estradiol binding confirmed a similar ER content in the urethra of male and female dogs and a higher ER content in the prostatic urethra than in the prostate itself (P less than 0.001)

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

Science.gov (United States)

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.

Introduction to Medical Image Analysis

DEFF Research Database (Denmark)

Paulsen, Rasmus Reinhold; Moeslund, Thomas B.

of the book is to present the fascinating world of medical image analysis in an easy and interesting way. Compared to many standard books on image analysis, the approach we have chosen is less mathematical and more casual. Some of the key algorithms are exemplified in C-code. Please note that the code...

Wavefront analysis for plenoptic camera imaging

International Nuclear Information System (INIS)

Luan Yin-Sen; Xu Bing; Yang Ping; Tang Guo-Mao

2017-01-01

The plenoptic camera is a single lens stereo camera which can retrieve the direction of light rays while detecting their intensity distribution. In this paper, to reveal more truths of plenoptic camera imaging, we present the wavefront analysis for the plenoptic camera imaging from the angle of physical optics but not from the ray tracing model of geometric optics. Specifically, the wavefront imaging model of a plenoptic camera is analyzed and simulated by scalar diffraction theory and the depth estimation is redescribed based on physical optics. We simulate a set of raw plenoptic images of an object scene, thereby validating the analysis and derivations and the difference between the imaging analysis methods based on geometric optics and physical optics are also shown in simulations. (paper)

Multimodality image analysis work station

International Nuclear Information System (INIS)

Ratib, O.; Huang, H.K.

1989-01-01

The goal of this project is to design and implement a PACS (picture archiving and communication system) workstation for quantitative analysis of multimodality images. The Macintosh II personal computer was selected for its friendly user interface, its popularity among the academic and medical community, and its low cost. The Macintosh operates as a stand alone workstation where images are imported from a central PACS server through a standard Ethernet network and saved on a local magnetic or optical disk. A video digitizer board allows for direct acquisition of images from sonograms or from digitized cine angiograms. The authors have focused their project on the exploration of new means of communicating quantitative data and information through the use of an interactive and symbolic user interface. The software developed includes a variety of image analysis, algorithms for digitized angiograms, sonograms, scintigraphic images, MR images, and CT scans

Immunocytochemical characterization of lung tumors in fine-needle aspiration. The use of cytokeratin monoclonal antibodies for the differential diagnosis of squamous cell carcinoma and adenocarcinoma.

Science.gov (United States)

Bruderman, I; Cohen, R; Leitner, O; Ronah, R; Guber, A; Griffel, B; Geiger, B

1990-10-15

In the current study, immunocytochemical typing of intermediate filaments was used for a differential diagnosis of human lung tumors from transthoracic fine-needle aspiration biopsies (TFNAB). The authors have compared the cytologic diagnosis of 53 lung cancer cases with the immunofluorescence patterns obtained using a panel of monoclonal antibodies, five of which (KG 8.13, KM 4.62, Ks B.17, KS 8.12, KK 8.60) react with specific cytokeratin polypeptides and one with vimentin (VIM 13.2). Only in six of 23 samples cytologically diagnosed as squamous cell carcinoma did the immunocytochemical typing of cytokeratins (ICTC) confirm the cytologic diagnosis. In seven cases some of the tumor cells stained positively with antibody Ks B.17 specific for simple epithelial keratin (No: 18), suggesting the presence of some cells of glandular origin. In ten additional cases the ICTC was in conflict with the cytologic diagnosis of squamous cell carcinoma (i.e., antibodies Ks 8.12 and KK 8.60 were negative, and antibody Ks B.17, positive) supporting a diagnosis of adenocarcinoma. In 14 of 18 cases cytologically diagnosed as adenocarcinoma, the ICTC confirmed the diagnosis whereas in four cases additional presence of some squamous cells was noticed. The ICTC labeling of cases cytologically diagnosed as undifferentiated and large cell carcinomas was similar to that of the group of adenocarcinomas. Thus, the application of cytokeratin typing for TFNAB samples seems to provide a vital complementation to routine cytologic study, especially for cases cytologically diagnosed as squamous carcinoma.

Rapid Analysis and Exploration of Fluorescence Microscopy Images

OpenAIRE

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

2014-01-01

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard.

Mesh Processing in Medical Image Analysis

DEFF Research Database (Denmark)

The following topics are dealt with: mesh processing; medical image analysis; interactive freeform modeling; statistical shape analysis; clinical CT images; statistical surface recovery; automated segmentation; cerebral aneurysms; and real-time particle-based representation....

Immunocytochemical expression of monocarboxylate transporters in the human visual cortex at midgestation.

Science.gov (United States)

Fayol, Laurence; Baud, Olivier; Monier, Anne; Pellerin, Luc; Magistretti, Pierre; Evrard, Philippe; Verney, Catherine

2004-01-31

Lactate and the other monocarboxylates are a major energy source for the developing brain. We investigated the immunocytochemical expression of two monocarboxylate transporters, MCT1 and MCT2, in the human visual cortex between 13 and 26 post-ovulatory weeks. We used immunoperoxidase and immunofluorescence techniques to determine whether these transporters co-localized with markers for blood vessels (CD34), neurons (microtubule-associated protein 2 [MAP2], SMI 311), radial glia (vimentin), or astrocytes (glial fibrillary acidic protein [GFAP], S100beta protein). MCT1 immunoreactivity was visible in blood vessel walls as early as the 13th week of gestation mainly in the cortical plate and subplate. At this stage, less than 10% of vessels in the ventricular layer expressed MCT1, whereas all blood vessels walls showed this immunoreactivity at the 26th gestational week. Starting at the 19th week of gestation, sparse MCT1 positive cell bodies were detected, some of them co-localized with MAP2 immunoreactivity. MCT2 immunoreactivity was noted in astrocytic cell bodies from week 19 and spread subsequently to the astrocyte end-feet in contact with blood vessels. MCTs immunoreactivities were most marked in the subplate and deep cortical plate, where the most differentiated neurons were located. Our findings suggest that monocarboxylate trafficking between vessels (MCT1), astrocytes (MCT2) and some postmitotic neurons (MCT1) could develop gradually toward 20 gestational weeks (g.w.). These data suggest that lactate or other monocarboxylates could represent a significant energy source for the human visual cortex at this early stage.

Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

DEFF Research Database (Denmark)

Jensen, P O; Larsen, J K; Christensen, I J

1994-01-01

Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogona...

Quantitative analysis of receptor imaging

International Nuclear Information System (INIS)

Fu Zhanli; Wang Rongfu

2004-01-01

Model-based methods for quantitative analysis of receptor imaging, including kinetic, graphical and equilibrium methods, are introduced in detail. Some technical problem facing quantitative analysis of receptor imaging, such as the correction for in vivo metabolism of the tracer and the radioactivity contribution from blood volume within ROI, and the estimation of the nondisplaceable ligand concentration, is also reviewed briefly

Information granules in image histogram analysis.

Science.gov (United States)

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.

Introduction to Medical Image Analysis

DEFF Research Database (Denmark)

Paulsen, Rasmus Reinhold; Moeslund, Thomas B.

This book is a result of a collaboration between DTU Informatics at the Technical University of Denmark and the Laboratory of Computer Vision and Media Technology at Aalborg University. It is partly based on the book ”Image and Video Processing”, second edition by Thomas Moeslund. The aim...... of the book is to present the fascinating world of medical image analysis in an easy and interesting way. Compared to many standard books on image analysis, the approach we have chosen is less mathematical and more casual. Some of the key algorithms are exemplified in C-code. Please note that the code...

Multispectral analysis of multimodal images

Energy Technology Data Exchange (ETDEWEB)

Kvinnsland, Yngve; Brekke, Njaal (Dept. of Surgical Sciences, Univ. of Bergen, Bergen (Norway)); Taxt, Torfinn M.; Gruener, Renate (Dept. of Biomedicine, Univ. of Bergen, Bergen (Norway))

2009-02-15

An increasing number of multimodal images represent a valuable increase in available image information, but at the same time it complicates the extraction of diagnostic information across the images. Multispectral analysis (MSA) has the potential to simplify this problem substantially as unlimited number of images can be combined, and tissue properties across the images can be extracted automatically. Materials and methods. We have developed a software solution for MSA containing two algorithms for unsupervised classification, an EM-algorithm finding multinormal class descriptions and the k-means clustering algorithm, and two for supervised classification, a Bayesian classifier using multinormal class descriptions and a kNN-algorithm. The software has an efficient user interface for the creation and manipulation of class descriptions, and it has proper tools for displaying the results. Results. The software has been tested on different sets of images. One application is to segment cross-sectional images of brain tissue (T1- and T2-weighted MR images) into its main normal tissues and brain tumors. Another interesting set of images are the perfusion maps and diffusion maps, derived images from raw MR images. The software returns segmentation that seem to be sensible. Discussion. The MSA software appears to be a valuable tool for image analysis with multimodal images at hand. It readily gives a segmentation of image volumes that visually seems to be sensible. However, to really learn how to use MSA, it will be necessary to gain more insight into what tissues the different segments contain, and the upcoming work will therefore be focused on examining the tissues through for example histological sections.

An Imaging And Graphics Workstation For Image Sequence Analysis

Science.gov (United States)

Mostafavi, Hassan

1990-01-01

This paper describes an application-specific engineering workstation designed and developed to analyze imagery sequences from a variety of sources. The system combines the software and hardware environment of the modern graphic-oriented workstations with the digital image acquisition, processing and display techniques. The objective is to achieve automation and high throughput for many data reduction tasks involving metric studies of image sequences. The applications of such an automated data reduction tool include analysis of the trajectory and attitude of aircraft, missile, stores and other flying objects in various flight regimes including launch and separation as well as regular flight maneuvers. The workstation can also be used in an on-line or off-line mode to study three-dimensional motion of aircraft models in simulated flight conditions such as wind tunnels. The system's key features are: 1) Acquisition and storage of image sequences by digitizing real-time video or frames from a film strip; 2) computer-controlled movie loop playback, slow motion and freeze frame display combined with digital image sharpening, noise reduction, contrast enhancement and interactive image magnification; 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) automatic and manual field-of-view and spatial calibration; 5) image sequence data base generation and management, including the measurement data products; 6) off-line analysis software for trajectory plotting and statistical analysis; 7) model-based estimation and tracking of object attitude angles; and 8) interface to a variety of video players and film transport sub-systems.

Brain-inspired algorithms for retinal image analysis

NARCIS (Netherlands)

ter Haar Romeny, B.M.; Bekkers, E.J.; Zhang, J.; Abbasi-Sureshjani, S.; Huang, F.; Duits, R.; Dasht Bozorg, Behdad; Berendschot, T.T.J.M.; Smit-Ockeloen, I.; Eppenhof, K.A.J.; Feng, J.; Hannink, J.; Schouten, J.; Tong, M.; Wu, H.; van Triest, J.W.; Zhu, S.; Chen, D.; He, W.; Xu, L.; Han, P.; Kang, Y.

2016-01-01

Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck

The cumulative verification image analysis tool for offline evaluation of portal images

International Nuclear Information System (INIS)

Wong, John; Yan Di; Michalski, Jeff; Graham, Mary; Halverson, Karen; Harms, William; Purdy, James

1995-01-01

Purpose: Daily portal images acquired using electronic portal imaging devices contain important information about the setup variation of the individual patient. The data can be used to evaluate the treatment and to derive correction for the individual patient. The large volume of images also require software tools for efficient analysis. This article describes the approach of cumulative verification image analysis (CVIA) specifically designed as an offline tool to extract quantitative information from daily portal images. Methods and Materials: The user interface, image and graphics display, and algorithms of the CVIA tool have been implemented in ANSCI C using the X Window graphics standards. The tool consists of three major components: (a) definition of treatment geometry and anatomical information; (b) registration of portal images with a reference image to determine setup variation; and (c) quantitative analysis of all setup variation measurements. The CVIA tool is not automated. User interaction is required and preferred. Successful alignment of anatomies on portal images at present remains mostly dependent on clinical judgment. Predefined templates of block shapes and anatomies are used for image registration to enhance efficiency, taking advantage of the fact that much of the tool's operation is repeated in the analysis of daily portal images. Results: The CVIA tool is portable and has been implemented on workstations with different operating systems. Analysis of 20 sequential daily portal images can be completed in less than 1 h. The temporal information is used to characterize setup variation in terms of its systematic, random and time-dependent components. The cumulative information is used to derive block overlap isofrequency distributions (BOIDs), which quantify the effective coverage of the prescribed treatment area throughout the course of treatment. Finally, a set of software utilities is available to facilitate feedback of the information for

Image sequence analysis workstation for multipoint motion analysis

Science.gov (United States)

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.

Image analysis enhancement and interpretation

International Nuclear Information System (INIS)

Glauert, A.M.

1978-01-01

The necessary practical and mathematical background are provided for the analysis of an electron microscope image in order to extract the maximum amount of structural information. Instrumental methods of image enhancement are described, including the use of the energy-selecting electron microscope and the scanning transmission electron microscope. The problems of image interpretation are considered with particular reference to the limitations imposed by radiation damage and specimen thickness. A brief survey is given of the methods for producing a three-dimensional structure from a series of two-dimensional projections, although emphasis is really given on the analysis, processing and interpretation of the two-dimensional projection of a structure. (Auth.)

Data Analysis Strategies in Medical Imaging.

Science.gov (United States)

Parmar, Chintan; Barry, Joseph D; Hosny, Ahmed; Quackenbush, John; Aerts, Hugo Jwl

2018-03-26

Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence (AI) has allowed for detailed quantification of radiographic characteristics of tissues using predefined engineered algorithms or deep learning methods. Precedents in radiology as well as a wealth of research studies hint at the clinical relevance of these characteristics. However, there are critical challenges associated with the analysis of medical imaging data. While some of these challenges are specific to the imaging field, many others like reproducibility and batch effects are generic and have already been addressed in other quantitative fields such as genomics. Here, we identify these pitfalls and provide recommendations for analysis strategies of medical imaging data including data normalization, development of robust models, and rigorous statistical analyses. Adhering to these recommendations will not only improve analysis quality, but will also enhance precision medicine by allowing better integration of imaging data with other biomedical data sources. Copyright ©2018, American Association for Cancer Research.

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

Science.gov (United States)

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.

Optimization of shearography image quality analysis

International Nuclear Information System (INIS)

Rafhayudi Jamro

2005-01-01

Shearography is an optical technique based on speckle pattern to measure the deformation of the object surface in which the fringe pattern is obtained through the correlation analysis from the speckle pattern. Analysis of fringe pattern for engineering application is limited for qualitative measurement. Therefore, for further analysis that lead to qualitative data, series of image processing mechanism are involved. In this paper, the fringe pattern for qualitative analysis is discussed. In principal field of applications is qualitative non-destructive testing such as detecting discontinuity, defect in the material structure, locating fatigue zones and etc and all these required image processing application. In order to performed image optimisation successfully, the noise in the fringe pattern must be minimised and the fringe pattern itself must be maximise. This can be achieved by applying a filtering method with a kernel size ranging from 2 X 2 to 7 X 7 pixels size and also applying equalizer in the image processing. (Author)

Metastasis of colon cancer to the thyroid gland: a case diagnosed on fine-needle aspirate by a combined cytological, immunocytochemical, and molecular approach.

Science.gov (United States)

Cozzolino, Immacolata; Malapelle, Umberto; Carlomagno, Chiara; Palombini, Lucio; Troncone, Giancarlo

2010-12-01

Fine-needle aspiration (FNA) with cytological evaluation reliably diagnoses primary and secondary thyroid neoplasms. However, identifying the primary origin of a metastatic process involving the thyroid gland is challenging. In particular, metastasis of colon cancer to the thyroid gland is very rare. In this case report, a right lobe solid thyroid nodule in a 66-year-old male was aspirated. FNA cytology showed necrosis and atypical tall columnar cells; since, the patient at age 60 had undergone surgery for a sigmoid-rectal cancer metastasizing to the liver and subsequently to the lung, a suspicion of metastasis from colon cancer was raised. This was corroborated by cell-block immunocytochemistry showing a cytokeratin (CK) 7 negative/CK20-positive staining pattern; thyreoglobulin and TTF-1 were both negative. Since KRAS codon 12/13 mutations frequently occur in colon cancer, whereas they are extremely uncommon in primary thyroid tumors, DNA was extracted from the aspirated cells, and KRAS mutational analysis was carried out. The codon 12 G12D mutation was found; the same mutation was evident in the primary cancer of the colon and in its liver and lung metastasis. Thus, a combined cytological, immunocytochemical and molecular approach unquestionably correlated metastatic adenocarcinoma cells aspirated from the thyroid to a colo-rectal origin. © 2010 Wiley-Liss, Inc.

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

Directory of Open Access Journals (Sweden)

Kiuru Aaro

2003-01-01

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

Some developments in multivariate image analysis

DEFF Research Database (Denmark)

Kucheryavskiy, Sergey

be up to several million. The main MIA tool for exploratory analysis is score density plot – all pixels are projected into principal component space and on the corresponding scores plots are colorized according to their density (how many pixels are crowded in the unit area of the plot). Looking...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection......Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...

Rapid analysis and exploration of fluorescence microscopy images.

Science.gov (United States)

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

2014-03-19

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

UV imaging in pharmaceutical analysis

DEFF Research Database (Denmark)

Østergaard, Jesper

2018-01-01

UV imaging provides spatially and temporally resolved absorbance measurements, which are highly useful in pharmaceutical analysis. Commercial UV imaging instrumentation was originally developed as a detector for separation sciences, but the main use is in the area of in vitro dissolution...

Image Analysis of Eccentric Photorefraction

Directory of Open Access Journals (Sweden)

J. Dušek

2004-01-01

Full Text Available This article deals with image and data analysis of the recorded video-sequences of strabistic infants. It describes a unique noninvasive measuring system based on two measuring methods (position of I. Purkynje image with relation to the centre of the lens and eccentric photorefraction for infants. The whole process is divided into three steps. The aim of the first step is to obtain video sequences on our special system (Eye Movement Analyser. Image analysis of the recorded sequences is performed in order to obtain curves of basic eye reactions (accommodation and convergence. The last step is to calibrate of these curves to corresponding units (diopter and degrees of movement.

Knowledge-based image analysis: some aspects on the analysis of images using other types of information

Energy Technology Data Exchange (ETDEWEB)

Eklundh, J O

1982-01-01

The computer vision approach to image analysis is discussed from two aspects. First, this approach is constrasted to the pattern recognition approach. Second, how external knowledge and information and models from other fields of science and engineering can be used for image and scene analysis is discussed. In particular, the connections between computer vision and computer graphics are pointed out.

Malware analysis using visualized image matrices.

Science.gov (United States)

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

2014-01-01

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

Malware Analysis Using Visualized Image Matrices

Directory of Open Access Journals (Sweden)

KyoungSoo Han

2014-01-01

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

Rapid, low-cost, image analysis through video processing

International Nuclear Information System (INIS)

Levinson, R.A.; Marrs, R.W.; Grantham, D.G.

1976-01-01

Remote Sensing now provides the data necessary to solve many resource problems. However, many of the complex image processing and analysis functions used in analysis of remotely-sensed data are accomplished using sophisticated image analysis equipment. High cost of this equipment places many of these techniques beyond the means of most users. A new, more economical, video system capable of performing complex image analysis has now been developed. This report describes the functions, components, and operation of that system. Processing capability of the new video image analysis system includes many of the tasks previously accomplished with optical projectors and digital computers. Video capabilities include: color separation, color addition/subtraction, contrast stretch, dark level adjustment, density analysis, edge enhancement, scale matching, image mixing (addition and subtraction), image ratioing, and construction of false-color composite images. Rapid input of non-digital image data, instantaneous processing and display, relatively low initial cost, and low operating cost gives the video system a competitive advantage over digital equipment. Complex pre-processing, pattern recognition, and statistical analyses must still be handled through digital computer systems. The video system at the University of Wyoming has undergone extensive testing, comparison to other systems, and has been used successfully in practical applications ranging from analysis of x-rays and thin sections to production of color composite ratios of multispectral imagery. Potential applications are discussed including uranium exploration, petroleum exploration, tectonic studies, geologic mapping, hydrology sedimentology and petrography, anthropology, and studies on vegetation and wildlife habitat

Automated image analysis of atomic force microscopy images of rotavirus particles

International Nuclear Information System (INIS)

Venkataraman, S.; Allison, D.P.; Qi, H.; Morrell-Falvey, J.L.; Kallewaard, N.L.; Crowe, J.E.; Doktycz, M.J.

2006-01-01

A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM

Automated image analysis of atomic force microscopy images of rotavirus particles

Energy Technology Data Exchange (ETDEWEB)

Venkataraman, S. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Allison, D.P. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Department of Biochemistry, Cellular, and Molecular Biology, University of Tennessee, Knoxville, TN 37996 (United States); Molecular Imaging Inc. Tempe, AZ, 85282 (United States); Qi, H. [Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996 (United States); Morrell-Falvey, J.L. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States); Kallewaard, N.L. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Crowe, J.E. [Vanderbilt University Medical Center, Nashville, TN 37232-2905 (United States); Doktycz, M.J. [Life Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States)]. E-mail: doktyczmj@ornl.gov

2006-06-15

A variety of biological samples can be imaged by the atomic force microscope (AFM) under environments that range from vacuum to ambient to liquid. Generally imaging is pursued to evaluate structural features of the sample or perhaps identify some structural changes in the sample that are induced by the investigator. In many cases, AFM images of sample features and induced structural changes are interpreted in general qualitative terms such as markedly smaller or larger, rougher, highly irregular, or smooth. Various manual tools can be used to analyze images and extract more quantitative data, but this is usually a cumbersome process. To facilitate quantitative AFM imaging, automated image analysis routines are being developed. Viral particles imaged in water were used as a test case to develop an algorithm that automatically extracts average dimensional information from a large set of individual particles. The extracted information allows statistical analyses of the dimensional characteristics of the particles and facilitates interpretation related to the binding of the particles to the surface. This algorithm is being extended for analysis of other biological samples and physical objects that are imaged by AFM.

Image analysis for ophthalmological diagnosis image processing of Corvis ST images using Matlab

CERN Document Server

Koprowski, Robert

2016-01-01

This monograph focuses on the use of analysis and processing methods for images from the Corvis® ST tonometer. The presented analysis is associated with the quantitative, repeatable and fully automatic evaluation of the response of the eye, eyeball and cornea to an air-puff. All the described algorithms were practically implemented in MATLAB®. The monograph also describes and provides the full source code designed to perform the discussed calculations. As a result, this monograph is intended for scientists, graduate students and students of computer science and bioengineering as well as doctors wishing to expand their knowledge of modern diagnostic methods assisted by various image analysis and processing methods.

Applications of stochastic geometry in image analysis

NARCIS (Netherlands)

Lieshout, van M.N.M.; Kendall, W.S.; Molchanov, I.S.

2009-01-01

A discussion is given of various stochastic geometry models (random fields, sequential object processes, polygonal field models) which can be used in intermediate and high-level image analysis. Two examples are presented of actual image analysis problems (motion tracking in video,

A report on digital image processing and analysis

International Nuclear Information System (INIS)

Singh, B.; Alex, J.; Haridasan, G.

1989-01-01

This report presents developments in software, connected with digital image processing and analysis in the Centre. In image processing, one resorts to either alteration of grey level values so as to enhance features in the image or resorts to transform domain operations for restoration or filtering. Typical transform domain operations like Karhunen-Loeve transforms are statistical in nature and are used for a good registration of images or template - matching. Image analysis procedures segment grey level images into images contained within selectable windows, for the purpose of estimating geometrical features in the image, like area, perimeter, projections etc. In short, in image processing both the input and output are images, whereas in image analyses, the input is an image whereas the output is a set of numbers and graphs. (author). 19 refs

APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

International Nuclear Information System (INIS)

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

1999-01-01

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

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

Science.gov (United States)

Kim, Yongmin; Alexander, Thomas

1986-06-01

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

CONTEXT BASED FOOD IMAGE ANALYSIS

OpenAIRE

He, Ye; Xu, Chang; Khanna, Nitin; Boushey, Carol J.; Delp, Edward J.

2013-01-01

We are developing a dietary assessment system that records daily food intake through the use of food images. Recognizing food in an image is difficult due to large visual variance with respect to eating or preparation conditions. This task becomes even more challenging when different foods have similar visual appearance. In this paper we propose to incorporate two types of contextual dietary information, food co-occurrence patterns and personalized learning models, in food image analysis to r...

Tolerance analysis through computational imaging simulations

Science.gov (United States)

Birch, Gabriel C.; LaCasse, Charles F.; Stubbs, Jaclynn J.; Dagel, Amber L.; Bradley, Jon

2017-11-01

The modeling and simulation of non-traditional imaging systems require holistic consideration of the end-to-end system. We demonstrate this approach through a tolerance analysis of a random scattering lensless imaging system.

Uses of software in digital image analysis: a forensic report

Science.gov (United States)

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.

An image scanner for real time analysis of spark chamber images

International Nuclear Information System (INIS)

Cesaroni, F.; Penso, G.; Locci, A.M.; Spano, M.A.

1975-01-01

The notes describes the semiautomatic scanning system at LNF for the analysis of spark chamber images. From the projection of the images on the scanner table, the trajectory in the real space is reconstructed

Application of automatic image analysis in wood science

Science.gov (United States)

Charles W. McMillin

1982-01-01

In this paper I describe an image analysis system and illustrate with examples the application of automatic quantitative measurement to wood science. Automatic image analysis, a powerful and relatively new technology, uses optical, video, electronic, and computer components to rapidly derive information from images with minimal operator interaction. Such instruments...

Chemical imaging and solid state analysis at compact surfaces using UV imaging

DEFF Research Database (Denmark)

Wu, Jian X.; Rehder, Sönke; van den Berg, Frans

2014-01-01

and excipients in a non-invasive way, as well as mapping the glibenclamide solid state form. An exploratory data analysis supported the critical evaluation of the mapping results and the selection of model parameters for the chemical mapping. The present study demonstrated that the multi-wavelength UV imaging......Fast non-destructive multi-wavelength UV imaging together with multivariate image analysis was utilized to visualize distribution of chemical components and their solid state form at compact surfaces. Amorphous and crystalline solid forms of the antidiabetic compound glibenclamide...

Digital image processing and analysis human and computer vision applications with CVIPtools

CERN Document Server

Umbaugh, Scott E

2010-01-01

Section I Introduction to Digital Image Processing and AnalysisDigital Image Processing and AnalysisOverviewImage Analysis and Computer VisionImage Processing and Human VisionKey PointsExercisesReferencesFurther ReadingComputer Imaging SystemsImaging Systems OverviewImage Formation and SensingCVIPtools SoftwareImage RepresentationKey PointsExercisesSupplementary ExercisesReferencesFurther ReadingSection II Digital Image Analysis and Computer VisionIntroduction to Digital Image AnalysisIntroductionPreprocessingBinary Image AnalysisKey PointsExercisesSupplementary ExercisesReferencesFurther Read

Making cytological diagnoses on digital images using the iPath network.

Science.gov (United States)

Dalquen, Peter; Savic Prince, Spasenija; Spieler, Peter; Kunze, Dietmar; Neumann, Heinrich; Eppenberger-Castori, Serenella; Adams, Heiner; Glatz, Katharina; Bubendorf, Lukas

2014-01-01

The iPath telemedicine platform Basel is mainly used for histological and cytological consultations, but also serves as a valuable learning tool. To study the level of accuracy in making diagnoses based on still images achieved by experienced cytopathologists, to identify limiting factors, and to provide a cytological image series as a learning set. Images from 167 consecutive cytological specimens of different origin were uploaded on the iPath platform and evaluated by four cytopathologists. Only wet-fixed and well-stained specimens were used. The consultants made specific diagnoses and categorized each as benign, suspicious or malignant. For all consultants, specificity and sensitivity regarding categorized diagnoses were 83-92 and 85-93%, respectively; the overall accuracy was 88-90%. The interobserver agreement was substantial (κ = 0.791). The lowest rate of concordance was achieved in urine and bladder washings and in the identification of benign lesions. Using a digital image set for diagnostic purposes implies that even under optimal conditions the accuracy rate will not exceed to 80-90%, mainly because of lacking supportive immunocytochemical or molecular tests. This limitation does not disqualify digital images for teleconsulting or as a learning aid. The series of images used for the study are open to the public at http://pathorama.wordpress.com/extragenital-cytology-2013/. © 2014 S. Karger AG, Basel.

Interpretation of medical images by model guided analysis

International Nuclear Information System (INIS)

Karssemeijer, N.

1989-01-01

Progress in the development of digital pictorial information systems stimulates a growing interest in the use of image analysis techniques in medicine. Especially when precise quantitative information is required the use of fast and reproducable computer analysis may be more appropriate than relying on visual judgement only. Such quantitative information can be valuable, for instance, in diagnostics or in irradiation therapy planning. As medical images are mostly recorded in a prescribed way, human anatomy guarantees a common image structure for each particular type of exam. In this thesis it is investigated how to make use of this a priori knowledge to guide image analysis. For that purpose models are developed which are suited to capture common image structure. The first part of this study is devoted to an analysis of nuclear medicine images of myocardial perfusion. In ch. 2 a model of these images is designed in order to represent characteristic image properties. It is shown that for these relatively simple images a compact symbolic description can be achieved, without significant loss of diagnostically importance of several image properties. Possibilities for automatic interpretation of more complex images is investigated in the following chapters. The central topic is segmentation of organs. Two methods are proposed and tested on a set of abdominal X-ray CT scans. Ch. 3 describes a serial approach based on a semantic network and the use of search areas. Relational constraints are used to guide the image processing and to classify detected image segments. In teh ch.'s 4 and 5 a more general parallel approach is utilized, based on a markov random field image model. A stochastic model used to represent prior knowledge about the spatial arrangement of organs is implemented as an external field. (author). 66 refs.; 27 figs.; 6 tabs

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Science.gov (United States)

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

The development of striatal patch/matrix organization after prenatal methylazoxymethanol: a combined immunocytochemical and bromo-deoxy-uridine birthdating study.

Science.gov (United States)

Snyder-Keller, A M

1995-10-01

The antimitotic drug methylazoxymethanol was used to destroy striatal patch neurons during their three-day-period of neurogenesis in the rat. Single or multiple injections of methylazoxymethanol were given during embryonic days 13-15, the period when patch neurons are known to undergo their final cell division. Methylazoxymethanol treatments produced a dramatic reduction in striatal volume. Immunocytochemical analysis revealed the continued presence of patches of neurons that were substance P-immunoreactive and devoid of calbindin and enkephalin immunoreactivity. Both the number of patches and relative volume occupied by patches was reduced in methylazoxymethanol-treated striata. Patch neurons could also be labelled by an intrastriatal injection of FluoroGold during the first postnatal week. The early ingrowth of nigrostriatal dopamine afferents was less noticeably patchy in the methylazoxymethanol-treated animals, in part owing to an overall increase in density. Large reductions in the number of neurons immunoreactive for choline acetyltransferase were observed, whereas NADPH diaphorase-stained neurons were not reduced unless methylazoxymethanol was given on embryonic day 15. Injections of bromo-deoxy-uridine, either during or after the 24 h that each methylazoxymethanol injection was considered to be effective, revealed that (i) some patch neurons continued to be generated in the 24-h period following methylazoxymethanol administration, and (ii) many patch neurons were generated after the effects of methylazoxymethanol had worn off. These findings demonstrate that it was impossible to completely eliminate the patches using methylazoxymethanol injections during the period of patch neurogenesis. However, methylazoxymethanol treatment during this time did produce a dramatic loss of cells and a relatively greater reduction in patch volume. Despite this disruption, the appropriate compartmentalization of neuroactive substances appeared to be maintained.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images

Science.gov (United States)

Lingley-Papadopoulos, Colleen A.; Loew, Murray H.; Zara, Jason M.

2009-07-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Wavelet analysis enables system-independent texture analysis of optical coherence tomography images.

Science.gov (United States)

Lingley-Papadopoulos, Colleen A; Loew, Murray H; Zara, Jason M

2009-01-01

Texture analysis for tissue characterization is a current area of optical coherence tomography (OCT) research. We discuss some of the differences between OCT systems and the effects those differences have on the resulting images and subsequent image analysis. In addition, as an example, two algorithms for the automatic recognition of bladder cancer are compared: one that was developed on a single system with no consideration for system differences, and one that was developed to address the issues associated with system differences. The first algorithm had a sensitivity of 73% and specificity of 69% when tested using leave-one-out cross-validation on data taken from a single system. When tested on images from another system with a different central wavelength, however, the method classified all images as cancerous regardless of the true pathology. By contrast, with the use of wavelet analysis and the removal of system-dependent features, the second algorithm reported sensitivity and specificity values of 87 and 58%, respectively, when trained on images taken with one imaging system and tested on images taken with another.

Analysis of PET hypoxia imaging in the quantitative imaging for personalized cancer medicine program

International Nuclear Information System (INIS)

Yeung, Ivan; Driscoll, Brandon; Keller, Harald; Shek, Tina; Jaffray, David; Hedley, David

2014-01-01

Quantitative imaging is an important tool in clinical trials of testing novel agents and strategies for cancer treatment. The Quantitative Imaging Personalized Cancer Medicine Program (QIPCM) provides clinicians and researchers participating in multi-center clinical trials with a central repository for their imaging data. In addition, a set of tools provide standards of practice (SOP) in end-to-end quality assurance of scanners and image analysis. The four components for data archiving and analysis are the Clinical Trials Patient Database, the Clinical Trials PACS, the data analysis engine(s) and the high-speed networks that connect them. The program provides a suite of software which is able to perform RECIST, dynamic MRI, CT and PET analysis. The imaging data can be assessed securely from remote and analyzed by researchers with these software tools, or with tools provided by the users and installed at the server. Alternatively, QIPCM provides a service for data analysis on the imaging data according developed SOP. An example of a clinical study in which patients with unresectable pancreatic adenocarcinoma were studied with dynamic PET-FAZA for hypoxia measurement will be discussed. We successfully quantified the degree of hypoxia as well as tumor perfusion in a group of 20 patients in terms of SUV and hypoxic fraction. It was found that there is no correlation between bulk tumor perfusion and hypoxia status in this cohort. QIPCM also provides end-to-end QA testing of scanners used in multi-center clinical trials. Based on quality assurance data from multiple CT-PET scanners, we concluded that quality control of imaging was vital in the success in multi-center trials as different imaging and reconstruction parameters in PET imaging could lead to very different results in hypoxia imaging. (author)

GEOPOSITIONING PRECISION ANALYSIS OF MULTIPLE IMAGE TRIANGULATION USING LRO NAC LUNAR IMAGES

Directory of Open Access Journals (Sweden)

K. Di

2016-06-01

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

An application of image processing techniques in computed tomography image analysis

DEFF Research Database (Denmark)

McEvoy, Fintan

2007-01-01

number of animals and image slices, automation of the process was desirable. The open-source and free image analysis program ImageJ was used. A macro procedure was created that provided the required functionality. The macro performs a number of basic image processing procedures. These include an initial...... process designed to remove the scanning table from the image and to center the animal in the image. This is followed by placement of a vertical line segment from the mid point of the upper border of the image to the image center. Measurements are made between automatically detected outer and inner...... boundaries of subcutaneous adipose tissue along this line segment. This process was repeated as the image was rotated (with the line position remaining unchanged) so that measurements around the complete circumference were obtained. Additionally, an image was created showing all detected boundary points so...

Frequency domain analysis of knock images

Science.gov (United States)

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

2014-12-01

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

Isolation of chicken taste buds for real-time Ca2+ imaging.

Science.gov (United States)

Kudo, Ken-ichi; Kawabata, Fuminori; Nomura, Toumi; Aridome, Ayumi; Nishimura, Shotaro; Tabata, Shoji

2014-10-01

We isolated chicken taste buds and used a real-time Ca2+ imaging technique to investigate the functions of the taste cells. With RT-PCR, we found that isolated chicken taste bud-like cell subsets express chicken gustducin messenger RNA. Immunocytochemical techniques revealed that the cell subsets were also immunopositive for chicken gustducin. These results provided strong evidence that the isolated cell subsets contain chicken taste buds. The isolated cell subsets were spindle-shaped and approximately 61-75 μm wide and 88-98 μm long, and these characteristics are similar to those of sectional chicken taste buds. Using Ca2+ imaging, we observed the buds' response to 2 mmol/L quinine hydrochloride (a bitter substance) and their response to a mixture of 25 mmol/L L-glutamic acid monopotassium salt monohydrate and 1 mmol/L inosine 5'-monophosphate disodium salt, umami substances. The present study is the first morphological demonstration of isolated chicken taste buds, and our results indicate that the isolated taste buds were intact and functional approaches for examining the taste senses of the chicken using Ca2+ imaging can be informative. © 2014 Japanese Society of Animal Science.

ImageJ-MATLAB: a bidirectional framework for scientific image analysis interoperability.

Science.gov (United States)

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

2017-02-15

ImageJ-MATLAB is a lightweight Java library facilitating bi-directional interoperability between MATLAB and ImageJ. By defining a standard for translation between matrix and image data structures, researchers are empowered to select the best tool for their image-analysis tasks. Freely available extension to ImageJ2 ( http://imagej.net/Downloads ). Installation and use instructions available at http://imagej.net/MATLAB_Scripting. Tested with ImageJ 2.0.0-rc-54 , Java 1.8.0_66 and MATLAB R2015b. eliceiri@wisc.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

The MicroAnalysis Toolkit: X-ray Fluorescence Image Processing Software

International Nuclear Information System (INIS)

Webb, S. M.

2011-01-01

The MicroAnalysis Toolkit is an analysis suite designed for the processing of x-ray fluorescence microprobe data. The program contains a wide variety of analysis tools, including image maps, correlation plots, simple image math, image filtering, multiple energy image fitting, semi-quantitative elemental analysis, x-ray fluorescence spectrum analysis, principle component analysis, and tomographic reconstructions. To be as widely useful as possible, data formats from many synchrotron sources can be read by the program with more formats available by request. An overview of the most common features will be presented.

Image sequence analysis in nuclear medicine: (1) Parametric imaging using statistical modelling

International Nuclear Information System (INIS)

Liehn, J.C.; Hannequin, P.; Valeyre, J.

1989-01-01

This is a review of parametric imaging methods on Nuclear Medicine. A Parametric Image is an image in which each pixel value is a function of the value of the same pixel of an image sequence. The Local Model Method is the fitting of each pixel time activity curve by a model which parameter values form the Parametric Images. The Global Model Method is the modelling of the changes between two images. It is applied to image comparison. For both methods, the different models, the identification criterion, the optimization methods and the statistical properties of the images are discussed. The analysis of one or more Parametric Images is performed using 1D or 2D histograms. The statistically significant Parametric Images, (Images of significant Variances, Amplitudes and Differences) are also proposed [fr

Multiplicative calculus in biomedical image analysis

NARCIS (Netherlands)

Florack, L.M.J.; Assen, van H.C.

2011-01-01

We advocate the use of an alternative calculus in biomedical image analysis, known as multiplicative (a.k.a. non-Newtonian) calculus. It provides a natural framework in problems in which positive images or positive definite matrix fields and positivity preserving operators are of interest. Indeed,

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

Science.gov (United States)

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.

Fractal-Based Image Analysis In Radiological Applications

Science.gov (United States)

Dellepiane, S.; Serpico, S. B.; Vernazza, G.; Viviani, R.

1987-10-01

We present some preliminary results of a study aimed to assess the actual effectiveness of fractal theory and to define its limitations in the area of medical image analysis for texture description, in particular, in radiological applications. A general analysis to select appropriate parameters (mask size, tolerance on fractal dimension estimation, etc.) has been performed on synthetically generated images of known fractal dimensions. Moreover, we analyzed some radiological images of human organs in which pathological areas can be observed. Input images were subdivided into blocks of 6x6 pixels; then, for each block, the fractal dimension was computed in order to create fractal images whose intensity was related to the D value, i.e., texture behaviour. Results revealed that the fractal images could point out the differences between normal and pathological tissues. By applying histogram-splitting segmentation to the fractal images, pathological areas were isolated. Two different techniques (i.e., the method developed by Pentland and the "blanket" method) were employed to obtain fractal dimension values, and the results were compared; in both cases, the appropriateness of the fractal description of the original images was verified.

Digital image analysis of NDT radiographs

International Nuclear Information System (INIS)

Graeme, W.A. Jr.; Eizember, A.C.; Douglass, J.

1989-01-01

Prior to the introduction of Charge Coupled Device (CCD) detectors the majority of image analysis performed on NDT radiographic images was done visually in the analog domain. While some film digitization was being performed, the process was often unable to capture all the usable information on the radiograph or was too time consuming. CCD technology now provides a method to digitize radiographic film images without losing the useful information captured in the original radiograph in a timely process. Incorporating that technology into a complete digital radiographic workstation allows analog radiographic information to be processed, providing additional information to the radiographer. Once in the digital domain, that data can be stored, and fused with radioscopic and other forms of digital data. The result is more productive analysis and management of radiographic inspection data. The principal function of the NDT Scan IV digital radiography system is the digitization, enhancement and storage of radiographic images

Breast cancer histopathology image analysis: a review.

Science.gov (United States)

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.

Image processing and analysis software development

International Nuclear Information System (INIS)

Shahnaz, R.

1999-01-01

The work presented in this project is aimed at developing a software 'IMAGE GALLERY' to investigate various image processing and analysis techniques. The work was divided into two parts namely the image processing techniques and pattern recognition, which further comprised of character and face recognition. Various image enhancement techniques including negative imaging, contrast stretching, compression of dynamic, neon, diffuse, emboss etc. have been studied. Segmentation techniques including point detection, line detection, edge detection have been studied. Also some of the smoothing and sharpening filters have been investigated. All these imaging techniques have been implemented in a window based computer program written in Visual Basic Neural network techniques based on Perception model have been applied for face and character recognition. (author)

Forensic Analysis of Digital Image Tampering

Science.gov (United States)

2004-12-01

analysis of when each method fails, which Chapter 4 discusses. Finally, a test image containing an invisible watermark using LSB steganography is...2.2 – Example of invisible watermark using Steganography Software F5 ............. 8 Figure 2.3 – Example of copy-move image forgery [12...used to embed the hidden watermark is Steganography Software F5 version 11+ discussed in Section 2.2. Original JPEG Image – 580 x 435 – 17.4

Automated thermal mapping techniques using chromatic image analysis

Science.gov (United States)

Buck, Gregory M.

1989-01-01

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

[Quantitative data analysis for live imaging of bone.

Science.gov (United States)

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.

Flame analysis using image processing techniques

Science.gov (United States)

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.

Image analysis for material characterisation

Science.gov (United States)

Livens, Stefan

In this thesis, a number of image analysis methods are presented as solutions to two applications concerning the characterisation of materials. Firstly, we deal with the characterisation of corrosion images, which is handled using a multiscale texture analysis method based on wavelets. We propose a feature transformation that deals with the problem of rotation invariance. Classification is performed with a Learning Vector Quantisation neural network and with combination of outputs. In an experiment, 86,2% of the images showing either pit formation or cracking, are correctly classified. Secondly, we develop an automatic system for the characterisation of silver halide microcrystals. These are flat crystals with a triangular or hexagonal base and a thickness in the 100 to 200 nm range. A light microscope is used to image them. A novel segmentation method is proposed, which allows to separate agglomerated crystals. For the measurement of shape, the ratio between the largest and the smallest radius yields the best results. The thickness measurement is based on the interference colours that appear for light reflected at the crystals. The mean colour of different thickness populations is determined, from which a calibration curve is derived. With this, the thickness of new populations can be determined accurately.

Precision Statistical Analysis of Images Based on Brightness Distribution

Directory of Open Access Journals (Sweden)

Muzhir Shaban Al-Ani

2017-07-01

Full Text Available Study the content of images is considered an important topic in which reasonable and accurate analysis of images are generated. Recently image analysis becomes a vital field because of huge number of images transferred via transmission media in our daily life. These crowded media with images lead to highlight in research area of image analysis. In this paper, the implemented system is passed into many steps to perform the statistical measures of standard deviation and mean values of both color and grey images. Whereas the last step of the proposed method concerns to compare the obtained results in different cases of the test phase. In this paper, the statistical parameters are implemented to characterize the content of an image and its texture. Standard deviation, mean and correlation values are used to study the intensity distribution of the tested images. Reasonable results are obtained for both standard deviation and mean value via the implementation of the system. The major issue addressed in the work is concentrated on brightness distribution via statistical measures applying different types of lighting.

Methods for processing and analysis functional and anatomical brain images: computerized tomography, emission tomography and nuclear resonance imaging

International Nuclear Information System (INIS)

Mazoyer, B.M.

1988-01-01

The various methods for brain image processing and analysis are presented and compared. The following topics are developed: the physical basis of brain image comparison (nature and formation of signals intrinsic performance of the methods image characteristics); mathematical methods for image processing and analysis (filtering, functional parameter extraction, morphological analysis, robotics and artificial intelligence); methods for anatomical localization (neuro-anatomy atlas, proportional stereotaxic atlas, numerized atlas); methodology of cerebral image superposition (normalization, retiming); image networks [fr

Computer-based image analysis in radiological diagnostics and image-guided therapy: 3D-Reconstruction, contrast medium dynamics, surface analysis, radiation therapy and multi-modal image fusion

International Nuclear Information System (INIS)

Beier, J.

2001-01-01

This book deals with substantial subjects of postprocessing and analysis of radiological image data, a particular emphasis was put on pulmonary themes. For a multitude of purposes the developed methods and procedures can directly be transferred to other non-pulmonary applications. The work presented here is structured in 14 chapters, each describing a selected complex of research. The chapter order reflects the sequence of the processing steps starting from artefact reduction, segmentation, visualization, analysis, therapy planning and image fusion up to multimedia archiving. In particular, this includes virtual endoscopy with three different scene viewers (Chap. 6), visualizations of the lung disease bronchiectasis (Chap. 7), surface structure analysis of pulmonary tumors (Chap. 8), quantification of contrast medium dynamics from temporal 2D and 3D image sequences (Chap. 9) as well as multimodality image fusion of arbitrary tomographical data using several visualization techniques (Chap. 12). Thus, the software systems presented cover the majority of image processing applications necessary in radiology and were entirely developed, implemented and validated in the clinical routine of a university medical school. (orig.) [de

Applications Of Binary Image Analysis Techniques

Science.gov (United States)

Tropf, H.; Enderle, E.; Kammerer, H. P.

1983-10-01

After discussing the conditions where binary image analysis techniques can be used, three new applications of the fast binary image analysis system S.A.M. (Sensorsystem for Automation and Measurement) are reported: (1) The human view direction is measured at TV frame rate while the subject's head is free movable. (2) Industrial parts hanging on a moving conveyor are classified prior to spray painting by robot. (3) In automotive wheel assembly, the eccentricity of the wheel is minimized by turning the tyre relative to the rim in order to balance the eccentricity of the components.

Analysis of licensed South African diagnostic imaging equipment ...

African Journals Online (AJOL)

Analysis of licensed South African diagnostic imaging equipment. ... Pan African Medical Journal ... Introduction: Objective: To conduct an analysis of all registered South Africa (SA) diagnostic radiology equipment, assess the number of equipment units per capita by imaging modality, and compare SA figures with published ...

Application of Image Texture Analysis for Evaluation of X-Ray Images of Fungal-Infected Maize Kernels

DEFF Research Database (Denmark)

Orina, Irene; Manley, Marena; Kucheryavskiy, Sergey V.

2018-01-01

The feasibility of image texture analysis to evaluate X-ray images of fungal-infected maize kernels was investigated. X-ray images of maize kernels infected with Fusarium verticillioides and control kernels were acquired using high-resolution X-ray micro-computed tomography. After image acquisition...... developed using partial least squares discriminant analysis (PLS-DA), and accuracies of 67 and 73% were achieved using first-order statistical features and GLCM extracted features, respectively. This work provides information on the possible application of image texture as method for analysing X-ray images......., homogeneity and contrast) were extracted from the side, front and top views of each kernel and used as inputs for principal component analysis (PCA). The first-order statistical image features gave a better separation of the control from infected kernels on day 8 post-inoculation. Classification models were...

Multi-spectral Image Analysis for Astaxanthin Coating Classification

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

2011-01-01

Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels...

A short introduction to image analysis - Matlab exercises

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg

2000-01-01

This document contain a short introduction to Image analysis. In addition small exercises has been prepared in order to support the theoretical understanding.......This document contain a short introduction to Image analysis. In addition small exercises has been prepared in order to support the theoretical understanding....

Vaccine Images on Twitter: Analysis of What Images are Shared

Science.gov (United States)

Dredze, Mark

2018-01-01

Background Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. Objective The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. Methods We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. Results Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet’s textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. Conclusions We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis. PMID:29615386

Vaccine Images on Twitter: Analysis of What Images are Shared.

Science.gov (United States)

Chen, Tao; Dredze, Mark

2018-04-03

Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet's textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis. ©Tao Chen, Mark Dredze. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.04.2018.

An approach for quantitative image quality analysis for CT

Science.gov (United States)

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

2016-03-01

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

Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

International Nuclear Information System (INIS)

Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

2004-01-01

The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

An expert image analysis system for chromosome analysis application

International Nuclear Information System (INIS)

Wu, Q.; Suetens, P.; Oosterlinck, A.; Van den Berghe, H.

1987-01-01

This paper reports a recent study on applying a knowledge-based system approach as a new attempt to solve the problem of chromosome classification. A theoretical framework of an expert image analysis system is proposed, based on such a study. In this scheme, chromosome classification can be carried out under a hypothesize-and-verify paradigm, by integrating a rule-based component, in which the expertise of chromosome karyotyping is formulated with an existing image analysis system which uses conventional pattern recognition techniques. Results from the existing system can be used to bring in hypotheses, and with the rule-based verification and modification procedures, improvement of the classification performance can be excepted

Research of second harmonic generation images based on texture analysis

Science.gov (United States)

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.

CALIPSO: an interactive image analysis software package for desktop PACS workstations

Science.gov (United States)

Ratib, Osman M.; Huang, H. K.

1990-07-01

The purpose of this project is to develop a low cost workstation for quantitative analysis of multimodality images using a Macintosh II personal computer. In the current configuration the Macintosh operates as a stand alone workstation where images are imported either from a central PACS server through a standard Ethernet network or recorded through video digitizer board. The CALIPSO software developed contains a large variety ofbasic image display and manipulation tools. We focused our effort however on the design and implementation ofquantitative analysis methods that can be applied to images from different imaging modalities. Analysis modules currently implemented include geometric and densitometric volumes and ejection fraction calculation from radionuclide and cine-angiograms Fourier analysis ofcardiac wall motion vascular stenosis measurement color coded parametric display of regional flow distribution from dynamic coronary angiograms automatic analysis ofmyocardial distribution ofradiolabelled tracers from tomoscintigraphic images. Several of these analysis tools were selected because they use similar color coded andparametric display methods to communicate quantitative data extracted from the images. 1. Rationale and objectives of the project Developments of Picture Archiving and Communication Systems (PACS) in clinical environment allow physicians and radiologists to assess radiographic images directly through imaging workstations (''). This convenient access to the images is often limited by the number of workstations available due in part to their high cost. There is also an increasing need for quantitative analysis ofthe images. During thepast decade

Digital image analysis of X-ray television with an image digitizer

International Nuclear Information System (INIS)

Mochizuki, Yasuo; Akaike, Hisahiko; Ogawa, Hitoshi; Kyuma, Yukishige

1995-01-01

When video signals of X-ray fluoroscopy were transformed from analog-to-digital ones with an image digitizer, their digital characteristic curves, pre-sampling MTF's and digital Wiener spectral could be measured. This method was advant ageous in that it was able to carry out data sampling because the pixel values inputted could be verified on a CRT. The system of image analysis by this method is inexpensive and effective in evaluating the image quality of digital system. Also, it is expected that this method can be used as a tool for learning the measurement techniques and physical characteristics of digital image quality effectively. (author)

Image analysis in x-ray cinefluorography

Energy Technology Data Exchange (ETDEWEB)

Ikuse, J; Yasuhara, H; Sugimoto, H [Toshiba Corp., Kawasaki, Kanagawa (Japan)

1979-02-01

For the cinefluorographic image in the cardiovascular diagnostic system, the image quality is evaluated by means of MTF (Modulation Transfer Function), and object contrast by introducing the concept of x-ray spectrum analysis. On the basis of these results, further investigation is made of optimum X-ray exposure factors set for cinefluorography and the cardiovascular diagnostic system.

Mesh Processing in Medical-Image Analysis-a Tutorial

DEFF Research Database (Denmark)

Levine, Joshua A.; Paulsen, Rasmus Reinhold; Zhang, Yongjie

2012-01-01

Medical-image analysis requires an understanding of sophisticated scanning modalities, constructing geometric models, building meshes to represent domains, and downstream biological applications. These four steps form an image-to-mesh pipeline. For research in this field to progress, the imaging...

5-ALA induced fluorescent image analysis of actinic keratosis

Science.gov (United States)

Cho, Yong-Jin; Bae, Youngwoo; Choi, Eung-Ho; Jung, Byungjo

2010-02-01

In this study, we quantitatively analyzed 5-ALA induced fluorescent images of actinic keratosis using digital fluorescent color and hyperspectral imaging modalities. UV-A was utilized to induce fluorescent images and actinic keratosis (AK) lesions were demarcated from surrounding the normal region with different methods. Eight subjects with AK lesion were participated in this study. In the hyperspectral imaging modality, spectral analysis method was utilized for hyperspectral cube image and AK lesions were demarcated from the normal region. Before image acquisition, we designated biopsy position for histopathology of AK lesion and surrounding normal region. Erythema index (E.I.) values on both regions were calculated from the spectral cube data. Image analysis of subjects resulted in two different groups: the first group with the higher fluorescence signal and E.I. on AK lesion than the normal region; the second group with lower fluorescence signal and without big difference in E.I. between two regions. In fluorescent color image analysis of facial AK, E.I. images were calculated on both normal and AK lesions and compared with the results of hyperspectral imaging modality. The results might indicate that the different intensity of fluorescence and E.I. among the subjects with AK might be interpreted as different phases of morphological and metabolic changes of AK lesions.

Regulatory peptides in the upper respiratory system and oral cavity of man. An immunocytochemical and radioimmunological study

International Nuclear Information System (INIS)

Hauser-Kronberger, C.

1992-01-01

In the present study a dense network of peptide-immunoreactive nerve fibres in the upper respiratory system and the oral cavity of man was investigated. The occurrence, distribution and concentrations of regulatory peptide immunoreactivities in human nasal mucosa, soft palate, ventricular fold, vocal cord, epiglottis, subglottis, glandula submandibularis and glandula parotis were investigated using highly efficient immunocytochemical and radio-immunological methods. In the tissues investigated vasoactive intestinal polypeptide (VIP) and other derivatives from the VIP-precursor (peptide histidine methionine = PHM), prepro VIP (111-122)), neuropeptide tyrosine (NPY) and its C-flanking peptide (CPON), calcitonin gene-related peptide (CGRP), substance P, neurokinin A, bombesin-flanking peptide and somatostatin were detected. The regulatory peptides demonstrated also included the recently isolated peptides helospectin and pituitary adenylate cyclase activating peptide (PACAP). Single endocrine-like cells were for the first time demonstrated within the respiratory epithelium and in the lamina propria of the nasal mucosa and soft palate and in groups within ducts. Ultrastructural immunelectronmicroscopy was performed using an ABC-pre-embedding method. In addition, semithin Epon resin sections were immunostained. The concentrations of VIP, NPY, CGRP, substance P and neurokinin A were measured using radioimmunological methods. The peptide immunoreactivities demonstrated in a dense network of neuronal structures and endocrine cells give indication for the presence of a complex regulatory system with potent physiological mechanisms in the upper respiratory system and allocated tissues of man

Image analysis of microsialograms of the mouse parotid gland using digital image processing

International Nuclear Information System (INIS)

Yoshiura, K.; Ohki, M.; Yamada, N.

1991-01-01

The authors compared two digital-image feature-extraction methods for the analysis of microsialograms of the mouse parotid gland following either overfilling, experimentally induced acute sialoadenitis or irradiation. Microsialograms were digitized using a drum-scanning microdensitometer. The grey levels were then partitioned into four bands representing soft tissue, peripheral minor, middle-sized and major ducts, and run-length and histogram analysis of the digital images performed. Serial analysis of microsialograms during progressive filling showed that both methods depicted the structural characteristics of the ducts at each grey level. However, in the experimental groups, run-length analysis showed slight changes in the peripheral duct system more clearly. This method was therefore considered more effective than histogram analysis

Introduction to the Multifractal Analysis of Images

OpenAIRE

Lévy Véhel , Jacques

1998-01-01

International audience; After a brief review of some classical approaches in image segmentation, the basics of multifractal theory and its application to image analysis are presented. Practical methods for multifractal spectrum estimation are discussed and some experimental results are given.

Computerised image analysis of biocrystallograms originating from agricultural products

DEFF Research Database (Denmark)

Andersen, Jens-Otto; Henriksen, Christian B.; Laursen, J.

1999-01-01

Procedures are presented for computerised image analysis of iocrystallogram images, originating from biocrystallization investigations of agricultural products. The biocrystallization method is based on the crystallographic phenomenon that when adding biological substances, such as plant extracts...... on up to eight parameters indicated strong relationships, with R2 up to 0.98. It is concluded that the procedures were able to discriminate the seven groups of images, and are applicable for biocrystallization investigations of agricultural products. Perspectives for the application of image analysis...

From Pixels to Geographic Objects in Remote Sensing Image Analysis

NARCIS (Netherlands)

Addink, E.A.; Van Coillie, Frieke M.B.; Jong, Steven M. de

Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received

Development of Image Analysis Software of MAXI

Science.gov (United States)

Eguchi, S.; Ueda, Y.; Hiroi, K.; Isobe, N.; Sugizaki, M.; Suzuki, M.; Tomida, H.; Maxi Team

2010-12-01

Monitor of All-sky X-ray Image (MAXI) is an X-ray all-sky monitor, attached to the Japanese experiment module Kibo on the International Space Station. The main scientific goals of the MAXI mission include the discovery of X-ray novae followed by prompt alerts to the community (Negoro et al., in this conference), and production of X-ray all-sky maps and new source catalogs with unprecedented sensitivities. To extract the best capabilities of the MAXI mission, we are working on the development of detailed image analysis tools. We utilize maximum likelihood fitting to a projected sky image, where we take account of the complicated detector responses, such as the background and point spread functions (PSFs). The modeling of PSFs, which strongly depend on the orbit and attitude of MAXI, is a key element in the image analysis. In this paper, we present the status of our software development.

Quantitative methods for the analysis of electron microscope images

DEFF Research Database (Denmark)

Skands, Peter Ulrik Vallø

1996-01-01

The topic of this thesis is an general introduction to quantitative methods for the analysis of digital microscope images. The images presented are primarily been acquired from Scanning Electron Microscopes (SEM) and interfermeter microscopes (IFM). The topic is approached though several examples...... foundation of the thesis fall in the areas of: 1) Mathematical Morphology; 2) Distance transforms and applications; and 3) Fractal geometry. Image analysis opens in general the possibility of a quantitative and statistical well founded measurement of digital microscope images. Herein lies also the conditions...

OpenComet: An automated tool for comet assay image analysis

Directory of Open Access Journals (Sweden)

Benjamin M. Gyori

2014-01-01

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

TEM validation of immunohistochemical staining prior to assessment of tumour angiogenesis by computerised image analysis

International Nuclear Information System (INIS)

Killingsworth, M.C.

2002-01-01

could be evaluated using computerised image analysis. Vessel area, aspect ratio, roundness and dendritic pattern were selected as parameters for measurement. ii) Assessment of other cell types associated with growing new vessels. Initial attempts at automated measurement of angiogenic parameters were made difficult by high numbers of CD34 positive cell processes surrounding tumour glands in association with blood vessels. EM examination revealed fibroblasts, lymphocytes and macrophages in close contact with developing endothelial sprouts. Fibroblasts in particular displayed slender elongated cell processes that could be confused with endothelial sprouts. This raised the question as to whether anti-CD34 staining was cross-reacting with these other cell types, iii) Immunocytochemical assessment of anti-CD34 specificity. Immunoelectron microscopic studies to check for anti-CD34 cross-reactive staining of fibroblasts associated with growing new vessels will now be carried out. Copyright (2002) Australian Society for Electron Microscopy Inc

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

Science.gov (United States)

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

Breast cancer histopathology image analysis : a review

NARCIS (Netherlands)

Veta, M.; Pluim, J.P.W.; Diest, van P.J.; Viergever, M.A.

2014-01-01

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

Analysis of Two-Dimensional Electrophoresis Gel Images

DEFF Research Database (Denmark)

Pedersen, Lars

2002-01-01

This thesis describes and proposes solutions to some of the currently most important problems in pattern recognition and image analysis of two-dimensional gel electrophoresis (2DGE) images. 2DGE is the leading technique to separate individual proteins in biological samples with many biological...

An Integrative Object-Based Image Analysis Workflow for Uav Images

Science.gov (United States)

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.

AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES

Directory of Open Access Journals (Sweden)

H. Yu

2016-06-01

Full Text Available 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.

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

Science.gov (United States)

Bigler, Erin D

2015-09-01

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

Analysis of live cell images: Methods, tools and opportunities.

Science.gov (United States)

Nketia, Thomas A; Sailem, Heba; Rohde, Gustavo; Machiraju, Raghu; Rittscher, Jens

2017-02-15

Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits. Copyright © 2017. Published by Elsevier Inc.

Image Sharing Technologies and Reduction of Imaging Utilization: A Systematic Review and Meta-analysis

Science.gov (United States)

Vest, Joshua R.; Jung, Hye-Young; Ostrovsky, Aaron; Das, Lala Tanmoy; McGinty, Geraldine B.

2016-01-01

Introduction Image sharing technologies may reduce unneeded imaging by improving provider access to imaging information. A systematic review and meta-analysis were conducted to summarize the impact of image sharing technologies on patient imaging utilization. Methods Quantitative evaluations of the effects of PACS, regional image exchange networks, interoperable electronic heath records, tools for importing physical media, and health information exchange systems on utilization were identified through a systematic review of the published and gray English-language literature (2004–2014). Outcomes, standard effect sizes (ESs), settings, technology, populations, and risk of bias were abstracted from each study. The impact of image sharing technologies was summarized with random-effects meta-analysis and meta-regression models. Results A total of 17 articles were included in the review, with a total of 42 different studies. Image sharing technology was associated with a significant decrease in repeat imaging (pooled effect size [ES] = −0.17; 95% confidence interval [CI] = [−0.25, −0.09]; P utilization (pooled ES = 0.20; 95% CI = [0.07, 0.32]; P = .002). For all outcomes combined, image sharing technology was not associated with utilization. Most studies were at risk for bias. Conclusions Image sharing technology was associated with reductions in repeat and unnecessary imaging, in both the overall literature and the most-rigorous studies. Stronger evidence is needed to further explore the role of specific technologies and their potential impact on various modalities, patient populations, and settings. PMID:26614882

Computed image analysis of neutron radiographs

International Nuclear Information System (INIS)

Dinca, M.; Anghel, E.; Preda, M.; Pavelescu, M.

2008-01-01

Similar with X-radiography, using neutron like penetrating particle, there is in practice a nondestructive technique named neutron radiology. When the registration of information is done on a film with the help of a conversion foil (with high cross section for neutrons) that emits secondary radiation (β,γ) that creates a latent image, the technique is named neutron radiography. A radiographic industrial film that contains the image of the internal structure of an object, obtained by neutron radiography, must be subsequently analyzed to obtain qualitative and quantitative information about the structural integrity of that object. There is possible to do a computed analysis of a film using a facility with next main components: an illuminator for film, a CCD video camera and a computer (PC) with suitable software. The qualitative analysis intends to put in evidence possibly anomalies of the structure due to manufacturing processes or induced by working processes (for example, the irradiation activity in the case of the nuclear fuel). The quantitative determination is based on measurements of some image parameters: dimensions, optical densities. The illuminator has been built specially to perform this application but can be used for simple visual observation. The illuminated area is 9x40 cm. The frame of the system is a comparer of Abbe Carl Zeiss Jena type, which has been adapted to achieve this application. The video camera assures the capture of image that is stored and processed by computer. A special program SIMAG-NG has been developed at INR Pitesti that beside of the program SMTV II of the special acquisition module SM 5010 can analyze the images of a film. The major application of the system was the quantitative analysis of a film that contains the images of some nuclear fuel pins beside a dimensional standard. The system was used to measure the length of the pellets of the TRIGA nuclear fuel. (authors)

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

Science.gov (United States)

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.

Evaluation of Yogurt Microstructure Using Confocal Laser Scanning Microscopy and Image Analysis.

Science.gov (United States)

Skytte, Jacob L; Ghita, Ovidiu; Whelan, Paul F; Andersen, Ulf; Møller, Flemming; Dahl, Anders B; Larsen, Rasmus

2015-06-01

The microstructure of protein networks in yogurts defines important physical properties of the yogurt and hereby partly its quality. Imaging this protein network using confocal scanning laser microscopy (CSLM) has shown good results, and CSLM has become a standard measuring technique for fermented dairy products. When studying such networks, hundreds of images can be obtained, and here image analysis methods are essential for using the images in statistical analysis. Previously, methods including gray level co-occurrence matrix analysis and fractal analysis have been used with success. However, a range of other image texture characterization methods exists. These methods describe an image by a frequency distribution of predefined image features (denoted textons). Our contribution is an investigation of the choice of image analysis methods by performing a comparative study of 7 major approaches to image texture description. Here, CSLM images from a yogurt fermentation study are investigated, where production factors including fat content, protein content, heat treatment, and incubation temperature are varied. The descriptors are evaluated through nearest neighbor classification, variance analysis, and cluster analysis. Our investigation suggests that the texton-based descriptors provide a fuller description of the images compared to gray-level co-occurrence matrix descriptors and fractal analysis, while still being as applicable and in some cases as easy to tune. © 2015 Institute of Food Technologists®

Geographic Object-Based Image Analysis: Towards a new paradigm

NARCIS (Netherlands)

Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.A.|info:eu-repo/dai/nl/224281216; Queiroz Feitosa, R.; van der Meer, F.D.|info:eu-repo/dai/nl/138940908; van der Werff, H.M.A.; van Coillie, F.; Tiede, A.

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature

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

Science.gov (United States)

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.

Some selected quantitative methods of thermal image analysis in Matlab.

Science.gov (United States)

Koprowski, Robert

2016-05-01

The paper presents a new algorithm based on some selected automatic quantitative methods for analysing thermal images. It shows the practical implementation of these image analysis methods in Matlab. It enables to perform fully automated and reproducible measurements of selected parameters in thermal images. The paper also shows two examples of the use of the proposed image analysis methods for the area of ​​the skin of a human foot and face. The full source code of the developed application is also provided as an attachment. The main window of the program during dynamic analysis of the foot thermal image. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

V-SIPAL - A VIRTUAL LABORATORY FOR SATELLITE IMAGE PROCESSING AND ANALYSIS

Directory of Open Access Journals (Sweden)

K. M. Buddhiraju

2012-09-01

Full Text Available 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.

Analysis of Neural Stem Cells from Human Cortical Brain Structures In Vitro.

Science.gov (United States)

Aleksandrova, M A; Poltavtseva, R A; Marei, M V; Sukhikh, G T

2016-05-01

Comparative immunohistochemical analysis of the neocortex from human fetuses showed that neural stem and progenitor cells are present in the brain throughout the gestation period, at least from week 8 through 26. At the same time, neural stem cells from the first and second trimester fetuses differed by the distribution, morphology, growth, and quantity. Immunocytochemical analysis of neural stem cells derived from fetuses at different gestation terms and cultured under different conditions showed their differentiation capacity. Detailed analysis of neural stem cell populations derived from fetuses on gestation weeks 8-9, 18-20, and 26 expressing Lex/SSEA1 was performed.

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

Science.gov (United States)

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.

Cnn Based Retinal Image Upscaling Using Zero Component Analysis

Science.gov (United States)

Nasonov, A.; Chesnakov, K.; Krylov, A.

2017-05-01

The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images. The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.

Intrasubject registration for change analysis in medical imaging

NARCIS (Netherlands)

Staring, M.

2008-01-01

Image matching is important for the comparison of medical images. Comparison is of clinical relevance for the analysis of differences due to changes in the health of a patient. For example, when a disease is imaged at two time points, then one wants to know if it is stable, has regressed, or

[Evaluation of dental plaque by quantitative digital image analysis system].

Science.gov (United States)

Huang, Z; Luan, Q X

2016-04-18

To analyze the plaque staining image by using image analysis software, to verify the maneuverability, practicability and repeatability of this technique, and to evaluate the influence of different plaque stains. In the study, 30 volunteers were enrolled from the new dental students of Peking University Health Science Center in accordance with the inclusion criteria. The digital images of the anterior teeth were acquired after plaque stained according to filming standardization.The image analysis was performed using Image Pro Plus 7.0, and the Quigley-Hein plaque indexes of the anterior teeth were evaluated. The plaque stain area percentage and the corresponding dental plaque index were highly correlated,and the Spearman correlation coefficient was 0.776 (Pchart showed only a few spots outside the 95% consistency boundaries. The different plaque stains image analysis results showed that the difference of the tooth area measurements was not significant, while the difference of the plaque area measurements significant (P<0.01). This method is easy in operation and control,highly related to the calculated percentage of plaque area and traditional plaque index, and has good reproducibility.The different plaque staining method has little effect on image segmentation results.The sensitive plaque stain for image analysis is suggested.

Difference Image Analysis of Galactic Microlensing. I. Data Analysis

Energy Technology Data Exchange (ETDEWEB)

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

1999-08-20

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

Analysis of RTM extended images for VTI media

KAUST Repository

Li, Vladimir; Tsvankin, Ilya; Alkhalifah, Tariq Ali

2015-01-01

velocity analysis remain generally valid in the extended image space for complex media. The dependence of RMO on errors in the anisotropy parameters provides essential insights for anisotropic wavefield tomography using extended images.

Peripheral blood smear image analysis: A comprehensive review

Directory of Open Access Journals (Sweden)

Emad A Mohammed

2014-01-01

Full Text Available Peripheral blood smear image examination is a part of the routine work of every laboratory. The manual examination of these images is tedious, time-consuming and suffers from interobserver variation. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The image segmentation step addresses the problem of extraction of the object or region of interest from the complicated peripheral blood smear image. Support vector machine (SVM and artificial neural networks (ANNs are two common approaches to image segmentation. Features extraction and selection aims to derive descriptive characteristics of the extracted object, which are similar within the same object class and different between different objects. This will facilitate the last step of the image analysis process: pattern classification. The goal of pattern classification is to assign a class to the selected features from a group of known classes. There are two types of classifier learning algorithms: supervised and unsupervised. Supervised learning algorithms predict the class of the object under test using training data of known classes. The training data have a predefined label for every class and the learning algorithm can utilize this data to predict the class of a test object. Unsupervised learning algorithms use unlabeled training data and divide them into groups using similarity measurements. Unsupervised learning algorithms predict the group to which a new test object belong to, based on the training data without giving an explicit class to that object. ANN, SVM, decision tree and K-nearest neighbor are possible approaches to classification algorithms. Increased discrimination may be obtained by combining several classifiers together.

Point defect characterization in HAADF-STEM images using multivariate statistical analysis

International Nuclear Information System (INIS)

Sarahan, Michael C.; Chi, Miaofang; Masiel, Daniel J.; Browning, Nigel D.

2011-01-01

Quantitative analysis of point defects is demonstrated through the use of multivariate statistical analysis. This analysis consists of principal component analysis for dimensional estimation and reduction, followed by independent component analysis to obtain physically meaningful, statistically independent factor images. Results from these analyses are presented in the form of factor images and scores. Factor images show characteristic intensity variations corresponding to physical structure changes, while scores relate how much those variations are present in the original data. The application of this technique is demonstrated on a set of experimental images of dislocation cores along a low-angle tilt grain boundary in strontium titanate. A relationship between chemical composition and lattice strain is highlighted in the analysis results, with picometer-scale shifts in several columns measurable from compositional changes in a separate column. -- Research Highlights: → Multivariate analysis of HAADF-STEM images. → Distinct structural variations among SrTiO 3 dislocation cores. → Picometer atomic column shifts correlated with atomic column population changes.

Utilizing Minkowski functionals for image analysis: a marching square algorithm

International Nuclear Information System (INIS)

Mantz, Hubert; Jacobs, Karin; Mecke, Klaus

2008-01-01

Comparing noisy experimental image data with statistical models requires a quantitative analysis of grey-scale images beyond mean values and two-point correlations. A real-space image analysis technique is introduced for digitized grey-scale images, based on Minkowski functionals of thresholded patterns. A novel feature of this marching square algorithm is the use of weighted side lengths for pixels, so that boundary lengths are captured accurately. As examples to illustrate the technique we study surface topologies emerging during the dewetting process of thin films and analyse spinodal decomposition as well as turbulent patterns in chemical reaction–diffusion systems. The grey-scale value corresponds to the height of the film or to the concentration of chemicals, respectively. Comparison with analytic calculations in stochastic geometry models reveals a remarkable agreement of the examples with a Gaussian random field. Thus, a statistical test for non-Gaussian features in experimental data becomes possible with this image analysis technique—even for small image sizes. Implementations of the software used for the analysis are offered for download

Etching and image analysis of the microstructure in marble

DEFF Research Database (Denmark)

Alm, Ditte; Brix, Susanne; Howe-Rasmussen, Helle

2005-01-01

of grains exposed on that surface are measured on the microscope images using image analysis by the program Adobe Photoshop 7.0 with Image Processing Toolkit 4.0. The parameters measured by the program on microscope images of thin sections of two marble types are used for calculation of the coefficient...

Objective analysis of image quality of video image capture systems

Science.gov (United States)

Rowberg, Alan H.

1990-07-01

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

Hyperspectral Image Analysis of Food Quality

DEFF Research Database (Denmark)

Arngren, Morten

inspection.Near-infrared spectroscopy can address these issues by offering a fast and objectiveanalysis of the food quality. A natural extension to these single spectrumNIR systems is to include image information such that each pixel holds a NIRspectrum. This augmented image information offers several......Assessing the quality of food is a vital step in any food processing line to ensurethe best food quality and maximum profit for the farmer and food manufacturer.Traditional quality evaluation methods are often destructive and labourintensive procedures relying on wet chemistry or subjective human...... extensions to the analysis offood quality. This dissertation is concerned with hyperspectral image analysisused to assess the quality of single grain kernels. The focus is to highlight thebenefits and challenges of using hyperspectral imaging for food quality presentedin two research directions. Initially...

Deep Learning in Medical Image Analysis.

Science.gov (United States)

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

Teaching image analysis at DIKU

DEFF Research Database (Denmark)

Johansen, Peter

2010-01-01

The early development of computer vision at Department of Computer Science at University of Copenhagen (DIKU) is briefly described. The different disciplines in computer vision are introduced, and the principles for teaching two courses, an image analysis course, and a robot lab class are outlined....

From Digital Imaging to Computer Image Analysis of Fine Art

Science.gov (United States)

Stork, David G.

An expanding range of techniques from computer vision, pattern recognition, image analysis, and computer graphics are being applied to problems in the history of art. The success of these efforts is enabled by the growing corpus of high-resolution multi-spectral digital images of art (primarily paintings and drawings), sophisticated computer vision methods, and most importantly the engagement of some art scholars who bring questions that may be addressed through computer methods. This paper outlines some general problem areas and opportunities in this new inter-disciplinary research program.

Signal and image multiresolution analysis

CERN Document Server

Ouahabi, Abdelialil

2012-01-01

Multiresolution analysis using the wavelet transform has received considerable attention in recent years by researchers in various fields. It is a powerful tool for efficiently representing signals and images at multiple levels of detail with many inherent advantages, including compression, level-of-detail display, progressive transmission, level-of-detail editing, filtering, modeling, fractals and multifractals, etc.This book aims to provide a simple formalization and new clarity on multiresolution analysis, rendering accessible obscure techniques, and merging, unifying or completing

Theoretical analysis of radiographic images by nonstationary Poisson processes

International Nuclear Information System (INIS)

Tanaka, Kazuo; Uchida, Suguru; Yamada, Isao.

1980-01-01

This paper deals with the noise analysis of radiographic images obtained in the usual fluorescent screen-film system. The theory of nonstationary Poisson processes is applied to the analysis of the radiographic images containing the object information. The ensemble averages, the autocorrelation functions, and the Wiener spectrum densities of the light-energy distribution at the fluorescent screen and of the film optical-density distribution are obtained. The detection characteristics of the system are evaluated theoretically. Numerical examples one-dimensional image are shown and the results are compared with those obtained under the assumption that the object image is related to the background noise by the additive process. (author)

Role of image analysis in quantitative characterisation of nuclear fuel materials

International Nuclear Information System (INIS)

Dubey, J.N.; Rao, T.S.; Pandey, V.D.; Majumdar, S.

2005-01-01

Image analysis is one of the important techniques, widely used for materials characterization. It provides the quantitative estimation of the microstructural features present in the material. This information is very much valuable for finding out the criteria for taking up the fuel for high burn up. Radiometallurgy Division has been carrying out development and fabrication of plutonium related fuels for different type of reactors viz. Purnima, Fast Breeder Test Reactor (FBTR), Prototype Fast Breeder Reactor (PFBR), Boiling Water Reactor (BWR), Advanced Heavy Water Reactor (AHWR), Pressurised Heavy Water Reactor (PHWR) and KAMINI Reactor. Image analysis has been carried out on microstructures of PHWR, AHWR, FBTR and KAMINI fuels. Samples were prepared as per standard ASTM metallographic procedure. Digital images of the microstructure of these specimens were obtained using CCD camera, attached to the optical microscope. These images are stores on computer and used for detection and analysis of features of interest with image analysis software. Quantitative image analysis technique has been standardised and used for finding put type of the porosity, its size, shape and distribution in the above sintered oxide and carbide fuels. This technique has also been used for quantitative estimation of different phases present in KAMINI fuel. Image analysis results have been summarised and presented in this paper. (author)

Analysis of Variance in Statistical Image Processing

Science.gov (United States)

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.

Towards automatic quantitative analysis of cardiac MR perfusion images

NARCIS (Netherlands)

Breeuwer, M.; Quist, M.; Spreeuwers, Lieuwe Jan; Paetsch, I.; Al-Saadi, N.; Nagel, E.

2001-01-01

Magnetic Resonance Imaging (MRI) is a powerful technique for imaging cardiovascular diseases. The introduction of cardiovascular MRI into clinical practice is however hampered by the lack of efficient and reliable automatic image analysis methods. This paper focuses on the automatic evaluation of

Knowledge-based analysis and understanding of 3D medical images

International Nuclear Information System (INIS)

Dhawan, A.P.; Juvvadi, S.

1988-01-01

The anatomical three-dimensional (3D) medical imaging modalities, such as X-ray CT and MRI, have been well recognized in the diagnostic radiology for several years while the nuclear medicine modalities, such as PET, have just started making a strong impact through functional imaging. Though PET images provide the functional information about the human organs, they are hard to interpret because of the lack of anatomical information. The authors objective is to develop a knowledge-based biomedical image analysis system which can interpret the anatomical images (such as CT). The anatomical information thus obtained can then be used in analyzing PET images of the same patient. This will not only help in interpreting PET images but it will also provide a means of studying the correlation between the anatomical and functional imaging. This paper presents the preliminary results of the knowledge based biomedical image analysis system for interpreting CT images of the chest

Image Post-Processing and Analysis. Chapter 17

Energy Technology Data Exchange (ETDEWEB)

Yushkevich, P. A. [University of Pennsylvania, Philadelphia (United States)

2014-09-15

For decades, scientists have used computers to enhance and analyse medical images. At first, they developed simple computer algorithms to enhance the appearance of interesting features in images, helping humans read and interpret them better. Later, they created more advanced algorithms, where the computer would not only enhance images but also participate in facilitating understanding of their content. Segmentation algorithms were developed to detect and extract specific anatomical objects in images, such as malignant lesions in mammograms. Registration algorithms were developed to align images of different modalities and to find corresponding anatomical locations in images from different subjects. These algorithms have made computer aided detection and diagnosis, computer guided surgery and other highly complex medical technologies possible. Nowadays, the field of image processing and analysis is a complex branch of science that lies at the intersection of applied mathematics, computer science, physics, statistics and biomedical sciences. This chapter will give a general overview of the most common problems in this field and the algorithms that address them.

Textural Analysis of Fatique Crack Surfaces: Image Pre-processing

Directory of Open Access Journals (Sweden)

H. Lauschmann

2000-01-01

Full Text Available For the fatique crack history reconstitution, new methods of quantitative microfractography are beeing developed based on the image processing and textural analysis. SEM magnifications between micro- and macrofractography are used. Two image pre-processing operatins were suggested and proved to prepare the crack surface images for analytical treatment: 1. Normalization is used to transform the image to a stationary form. Compared to the generally used equalization, it conserves the shape of brightness distribution and saves the character of the texture. 2. Binarization is used to transform the grayscale image to a system of thick fibres. An objective criterion for the threshold brightness value was found as that resulting into the maximum number of objects. Both methods were succesfully applied together with the following textural analysis.

Astronomical Image and Data Analysis

CERN Document Server

Starck, J.-L

2006-01-01

With information and scale as central themes, this comprehensive survey explains how to handle real problems in astronomical data analysis using a modern arsenal of powerful techniques. It treats those innovative methods of image, signal, and data processing that are proving to be both effective and widely relevant. The authors are leaders in this rapidly developing field and draw upon decades of experience. They have been playing leading roles in international projects such as the Virtual Observatory and the Grid. The book addresses not only students and professional astronomers and astrophysicists, but also serious amateur astronomers and specialists in earth observation, medical imaging, and data mining. The coverage includes chapters or appendices on: detection and filtering; image compression; multichannel, multiscale, and catalog data analytical methods; wavelets transforms, Picard iteration, and software tools. This second edition of Starck and Murtagh's highly appreciated reference again deals with to...

Secure thin client architecture for DICOM image analysis

Science.gov (United States)

Mogatala, Harsha V. R.; Gallet, Jacqueline

2005-04-01

This paper presents a concept of Secure Thin Client (STC) Architecture for Digital Imaging and Communications in Medicine (DICOM) image analysis over Internet. STC Architecture provides in-depth analysis and design of customized reports for DICOM images using drag-and-drop and data warehouse technology. Using a personal computer and a common set of browsing software, STC can be used for analyzing and reporting detailed patient information, type of examinations, date, Computer Tomography (CT) dose index, and other relevant information stored within the images header files as well as in the hospital databases. STC Architecture is three-tier architecture. The First-Tier consists of drag-and-drop web based interface and web server, which provides customized analysis and reporting ability to the users. The Second-Tier consists of an online analytical processing (OLAP) server and database system, which serves fast, real-time, aggregated multi-dimensional data using OLAP technology. The Third-Tier consists of a smart algorithm based software program which extracts DICOM tags from CT images in this particular application, irrespective of CT vendor's, and transfers these tags into a secure database system. This architecture provides Winnipeg Regional Health Authorities (WRHA) with quality indicators for CT examinations in the hospitals. It also provides health care professionals with analytical tool to optimize radiation dose and image quality parameters. The information is provided to the user by way of a secure socket layer (SSL) and role based security criteria over Internet. Although this particular application has been developed for WRHA, this paper also discusses the effort to extend the Architecture to other hospitals in the region. Any DICOM tag from any imaging modality could be tracked with this software.

Microarray BASICA: Background Adjustment, Segmentation, Image Compression and Analysis of Microarray Images

Directory of Open Access Journals (Sweden)

Jianping Hua

2004-01-01

Full Text Available This paper presents microarray BASICA: an integrated image processing tool for background adjustment, segmentation, image compression, and analysis of cDNA microarray images. BASICA uses a fast Mann-Whitney test-based algorithm to segment cDNA microarray images, and performs postprocessing to eliminate the segmentation irregularities. The segmentation results, along with the foreground and background intensities obtained with the background adjustment, are then used for independent compression of the foreground and background. We introduce a new distortion measurement for cDNA microarray image compression and devise a coding scheme by modifying the embedded block coding with optimized truncation (EBCOT algorithm (Taubman, 2000 to achieve optimal rate-distortion performance in lossy coding while still maintaining outstanding lossless compression performance. Experimental results show that the bit rate required to ensure sufficiently accurate gene expression measurement varies and depends on the quality of cDNA microarray images. For homogeneously hybridized cDNA microarray images, BASICA is able to provide from a bit rate as low as 5 bpp the gene expression data that are 99% in agreement with those of the original 32 bpp images.

Determination of fish gender using fractal analysis of ultrasound images

DEFF Research Database (Denmark)

McEvoy, Fintan J.; Tomkiewicz, Jonna; Støttrup, Josianne

2009-01-01

The gender of cod Gadus morhua can be determined by considering the complexity in their gonadal ultrasonographic appearance. The fractal dimension (DB) can be used to describe this feature in images. B-mode gonadal ultrasound images in 32 cod, where gender was known, were collected. Fractal...... by subjective analysis alone. The mean (and standard deviation) of the fractal dimension DB for male fish was 1.554 (0.073) while for female fish it was 1.468 (0.061); the difference was statistically significant (P=0.001). The area under the ROC curve was 0.84 indicating the value of fractal analysis in gender...... result. Fractal analysis is useful for gender determination in cod. This or a similar form of analysis may have wide application in veterinary imaging as a tool for quantification of complexity in images...

Web Based Distributed Coastal Image Analysis System, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — This project develops Web based distributed image analysis system processing the Moderate Resolution Imaging Spectroradiometer (MODIS) data to provide decision...

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

Science.gov (United States)

Rhoten, Bethany A

2016-05-01

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

Methods in quantitative image analysis.

Science.gov (United States)

Oberholzer, M; Ostreicher, M; Christen, H; Brühlmann, M

1996-05-01

The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value

Image analysis and machine learning for detecting malaria.

Science.gov (United States)

Poostchi, Mahdieh; Silamut, Kamolrat; Maude, Richard J; Jaeger, Stefan; Thoma, George

2018-04-01

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis. Published by Elsevier Inc.

Basic strategies for valid cytometry using image analysis

NARCIS (Netherlands)

Jonker, A.; Geerts, W. J.; Chieco, P.; Moorman, A. F.; Lamers, W. H.; van Noorden, C. J.

1997-01-01

The present review provides a starting point for setting up an image analysis system for quantitative densitometry and absorbance or fluorescence measurements in cell preparations, tissue sections or gels. Guidelines for instrumental settings that are essential for the valid application of image

Chromatic Image Analysis For Quantitative Thermal Mapping

Science.gov (United States)

Buck, Gregory M.

1995-01-01

Chromatic image analysis system (CIAS) developed for use in noncontact measurements of temperatures on aerothermodynamic models in hypersonic wind tunnels. Based on concept of temperature coupled to shift in color spectrum for optical measurement. Video camera images fluorescence emitted by phosphor-coated model at two wavelengths. Temperature map of model then computed from relative brightnesses in video images of model at those wavelengths. Eliminates need for intrusive, time-consuming, contact temperature measurements by gauges, making it possible to map temperatures on complex surfaces in timely manner and at reduced cost.

Mathematical foundations of image processing and analysis

CERN Document Server

Pinoli, Jean-Charles

2014-01-01

Mathematical Imaging is currently a rapidly growing field in applied mathematics, with an increasing need for theoretical mathematics. This book, the second of two volumes, emphasizes the role of mathematics as a rigorous basis for imaging sciences. It provides a comprehensive and convenient overview of the key mathematical concepts, notions, tools and frameworks involved in the various fields of gray-tone and binary image processing and analysis, by proposing a large, but coherent, set of symbols and notations, a complete list of subjects and a detailed bibliography. It establishes a bridg

Low Cost Desktop Image Analysis Workstation With Enhanced Interactive User Interface

Science.gov (United States)

Ratib, Osman M.; Huang, H. K.

1989-05-01

A multimodality picture archiving and communication system (PACS) is in routine clinical use in the UCLA Radiology Department. Several types workstations are currently implemented for this PACS. Among them, the Apple Macintosh II personal computer was recently chosen to serve as a desktop workstation for display and analysis of radiological images. This personal computer was selected mainly because of its extremely friendly user-interface, its popularity among the academic and medical community and its low cost. In comparison to other microcomputer-based systems the Macintosh II offers the following advantages: the extreme standardization of its user interface, file system and networking, and the availability of a very large variety of commercial software packages. In the current configuration the Macintosh II operates as a stand-alone workstation where images are imported from a centralized PACS server through an Ethernet network using a standard TCP-IP protocol, and stored locally on magnetic disk. The use of high resolution screens (1024x768 pixels x 8bits) offer sufficient performance for image display and analysis. We focused our project on the design and implementation of a variety of image analysis algorithms ranging from automated structure and edge detection to sophisticated dynamic analysis of sequential images. Specific analysis programs were developed for ultrasound images, digitized angiograms, MRI and CT tomographic images and scintigraphic images.

Morphometric image analysis of giant vesicles

DEFF Research Database (Denmark)

Husen, Peter Rasmussen; Arriaga, Laura; Monroy, Francisco

2012-01-01

We have developed a strategy to determine lengths and orientations of tie lines in the coexistence region of liquid-ordered and liquid-disordered phases of cholesterol containing ternary lipid mixtures. The method combines confocal-fluorescence-microscopy image stacks of giant unilamellar vesicles...... (GUVs), a dedicated 3D-image analysis, and a quantitative analysis based in equilibrium thermodynamic considerations. This approach was tested in GUVs composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine/1,2-palmitoyl-sn-glycero-3-phosphocholine/cholesterol. In general, our results show a reasonable...... agreement with previously reported data obtained by other methods. For example, our computed tie lines were found to be nonhorizontal, indicating a difference in cholesterol content in the coexisting phases. This new, to our knowledge, analytical strategy offers a way to further exploit fluorescence...

Standardization of Image Quality Analysis – ISO 19264

DEFF Research Database (Denmark)

Wüller, Dietmar; Kejser, Ulla Bøgvad

2016-01-01

There are a variety of image quality analysis tools available for the archiving world, which are based on different test charts and analysis algorithms. ISO has formed a working group in 2012 to harmonize these approaches and create a standard way of analyzing the image quality for archiving...... systems. This has resulted in three documents that have been or are going to be published soon. ISO 19262 defines the terms used in the area of image capture to unify the language. ISO 19263 describes the workflow issues and provides detailed information on how the measurements are done. Last...... but not least ISO 19264 describes the measurements in detail and provides aims and tolerance levels for the different aspects. This paper will present the new ISO 19264 technical specification to analyze image quality based on a single capture of a multi-pattern test chart, and discuss the reasoning behind its...

Telemetry Timing Analysis for Image Reconstruction of Kompsat Spacecraft

Directory of Open Access Journals (Sweden)

Jin-Ho Lee

2000-06-01

Full Text Available The KOMPSAT (KOrea Multi-Purpose SATellite has two optical imaging instruments called EOC (Electro-Optical Camera and OSMI (Ocean Scanning Multispectral Imager. The image data of these instruments are transmitted to ground station and restored correctly after post-processing with the telemetry data transferred from KOMPSAT spacecraft. The major timing information of the KOMPSAT is OBT (On-Board Time which is formatted by the on-board computer of the spacecraft, based on 1Hz sync. pulse coming from the GPS receiver involved. The OBT is transmitted to ground station with the house-keeping telemetry data of the spacecraft while it is distributed to the instruments via 1553B data bus for synchronization during imaging and formatting. The timing information contained in the spacecraft telemetry data would have direct relation to the image data of the instruments, which should be well explained to get a more accurate image. This paper addresses the timing analysis of the KOMPSAT spacecraft and instruments, including the gyro data timing analysis for the correct restoration of the EOC and OSMI image data at ground station.

Analysis and clinical usefullness of cardiac ECT images

International Nuclear Information System (INIS)

Hayashi, Makoto; Kagawa, Masaaki; Yamada, Yukinori

1983-01-01

We estimated basically and clinically myocardial ECT image and ECG gated cardiac blood-pool ECT image. ROC curve is used for the evaluation of the accuracy in diagnostic myocardial infarction. The accuracy in diagnostic of MI is superior in myocardial ECT image and ECT estimation is unnecessary skillfulness and experience. We can absene the whole defect of MI than planar image by using ECT. LVEDV between estimated volume and contrast volume is according to it and get one step for automatic analysis of cardiac volume. (author)

Fourier analysis: from cloaking to imaging

Science.gov (United States)

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

2016-04-01

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

Flow cytometric analysis of RNA synthesis by detection of bromouridine incorporation

DEFF Research Database (Denmark)

Larsen, J K; Jensen, Peter Østrup; Larsen, J

2001-01-01

RNA synthesis has traditionally been investigated by a laborious and time-consuming radiographic method involving incorporation of tritiated uridine. Now a faster non-radioactive alternative has emerged, based on immunocytochemical detection. This method utilizes the brominated RNA precursor...... bromouridine, which is taken into a cell, phosphorylated, and incorporated into nascent RNA. The BrU-substituted RNA is detected by permeabilizing the cells and staining with certain anti-BrdU antibodies. This dynamic approach yields information complementing that provided by cellular RNA content analysis...

A software package for biomedical image processing and analysis

International Nuclear Information System (INIS)

Goncalves, J.G.M.; Mealha, O.

1988-01-01

The decreasing cost of computing power and the introduction of low cost imaging boards justifies the increasing number of applications of digital image processing techniques in the area of biomedicine. There is however a large software gap to be fulfilled, between the application and the equipment. The requirements to bridge this gap are twofold: good knowledge of the hardware provided and its interface to the host computer, and expertise in digital image processing and analysis techniques. A software package incorporating these two requirements was developed using the C programming language, in order to create a user friendly image processing programming environment. The software package can be considered in two different ways: as a data structure adapted to image processing and analysis, which acts as the backbone and the standard of communication for all the software; and as a set of routines implementing the basic algorithms used in image processing and analysis. Hardware dependency is restricted to a single module upon which all hardware calls are based. The data structure that was built has four main features: hierchical, open, object oriented, and object dependent dimensions. Considering the vast amount of memory needed by imaging applications and the memory available in small imaging systems, an effective image memory management scheme was implemented. This software package is being used for more than one and a half years by users with different applications. It proved to be an excellent tool for helping people to get adapted into the system, and for standardizing and exchanging software, yet preserving flexibility allowing for users' specific implementations. The philosophy of the software package is discussed and the data structure that was built is described in detail

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

Science.gov (United States)

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.

Multifractal analysis of 2D gray soil images

Science.gov (United States)

González-Torres, Ivan; Losada, Juan Carlos; Heck, Richard; Tarquis, Ana M.

2015-04-01

Soil structure, understood as the spatial arrangement of soil pores, is one of the key factors in soil modelling processes. Geometric properties of individual and interpretation of the morphological parameters of pores can be estimated from thin sections or 3D Computed Tomography images (Tarquis et al., 2003), but there is no satisfactory method to binarized these images and quantify the complexity of their spatial arrangement (Tarquis et al., 2008, Tarquis et al., 2009; Baveye et al., 2010). The objective of this work was to apply a multifractal technique, their singularities (α) and f(α) spectra, to quantify it without applying any threshold (Gónzalez-Torres, 2014). Intact soil samples were collected from four horizons of an Argisol, formed on the Tertiary Barreiras group of formations in Pernambuco state, Brazil (Itapirema Experimental Station). The natural vegetation of the region is tropical, coastal rainforest. From each horizon, showing different porosities and spatial arrangements, three adjacent samples were taken having a set of twelve samples. The intact soil samples were imaged using an EVS (now GE Medical. London, Canada) MS-8 MicroCT scanner with 45 μm pixel-1 resolution (256x256 pixels). Though some samples required paring to fit the 64 mm diameter imaging tubes, field orientation was maintained. References Baveye, P.C., M. Laba, W. Otten, L. Bouckaert, P. Dello, R.R. Goswami, D. Grinev, A. Houston, Yaoping Hu, Jianli Liu, S. Mooney, R. Pajor, S. Sleutel, A. Tarquis, Wei Wang, Qiao Wei, Mehmet Sezgin. Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data. Geoderma, 157, 51-63, 2010. González-Torres, Iván. Theory and application of multifractal analysis methods in images for the study of soil structure. Master thesis, UPM, 2014. Tarquis, A.M., R.J. Heck, J.B. Grau; J. Fabregat, M.E. Sanchez and J.M. Antón. Influence of Thresholding in Mass and Entropy Dimension of 3-D

Design and validation of Segment - freely available software for cardiovascular image analysis

International Nuclear Information System (INIS)

Heiberg, Einar; Sjögren, Jane; Ugander, Martin; Carlsson, Marcus; Engblom, Henrik; Arheden, Håkan

2010-01-01

Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page (http://segment.heiberg.se). Segment

Quantitative analysis and classification of AFM images of human hair.

Science.gov (United States)

Gurden, S P; Monteiro, V F; Longo, E; Ferreira, M M C

2004-07-01

The surface topography of human hair, as defined by the outer layer of cellular sheets, termed cuticles, largely determines the cosmetic properties of the hair. The condition of the cuticles is of great cosmetic importance, but also has the potential to aid diagnosis in the medical and forensic sciences. Atomic force microscopy (AFM) has been demonstrated to offer unique advantages for analysis of the hair surface, mainly due to the high image resolution and the ease of sample preparation. This article presents an algorithm for the automatic analysis of AFM images of human hair. The cuticular structure is characterized using a series of descriptors, such as step height, tilt angle and cuticle density, allowing quantitative analysis and comparison of different images. The usefulness of this approach is demonstrated by a classification study. Thirty-eight AFM images were measured, consisting of hair samples from (a) untreated and bleached hair samples, and (b) the root and distal ends of the hair fibre. The multivariate classification technique partial least squares discriminant analysis is used to test the ability of the algorithm to characterize the images according to the properties of the hair samples. Most of the images (86%) were found to be classified correctly.

SIMA: Python software for analysis of dynamic fluorescence imaging data

Directory of Open Access Journals (Sweden)

Patrick eKaifosh

2014-09-01

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

Multisource Images Analysis Using Collaborative Clustering

Directory of Open Access Journals (Sweden)

Pierre Gançarski

2008-04-01

Full Text Available The development of very high-resolution (VHR satellite imagery has produced a huge amount of data. The multiplication of satellites which embed different types of sensors provides a lot of heterogeneous images. Consequently, the image analyst has often many different images available, representing the same area of the Earth surface. These images can be from different dates, produced by different sensors, or even at different resolutions. The lack of machine learning tools using all these representations in an overall process constraints to a sequential analysis of these various images. In order to use all the information available simultaneously, we propose a framework where different algorithms can use different views of the scene. Each one works on a different remotely sensed image and, thus, produces different and useful information. These algorithms work together in a collaborative way through an automatic and mutual refinement of their results, so that all the results have almost the same number of clusters, which are statistically similar. Finally, a unique result is produced, representing a consensus among the information obtained by each clustering method on its own image. The unified result and the complementarity of the single results (i.e., the agreement between the clustering methods as well as the disagreement lead to a better understanding of the scene. The experiments carried out on multispectral remote sensing images have shown that this method is efficient to extract relevant information and to improve the scene understanding.

Image quality preferences among radiographers and radiologists. A conjoint analysis

International Nuclear Information System (INIS)

Ween, Borgny; Kristoffersen, Doris Tove; Hamilton, Glenys A.; Olsen, Dag Rune

2005-01-01

Purpose: The aim of this study was to investigate the image quality preferences among radiographers and radiologists. The radiographers' preferences are mainly related to technical parameters, whereas radiologists assess image quality based on diagnostic value. Methods: A conjoint analysis was undertaken to survey image quality preferences; the study included 37 respondents: 19 radiographers and 18 radiologists. Digital urograms were post-processed into 8 images with different properties of image quality for 3 different patients. The respondents were asked to rank the images according to their personally perceived subjective image quality. Results: Nearly half of the radiographers and radiologists were consistent in their ranking of the image characterised as 'very best image quality'. The analysis showed, moreover, that chosen filtration level and image intensity were responsible for 72% and 28% of the preferences, respectively. The corresponding figures for each of the two professions were 76% and 24% for the radiographers, and 68% and 32% for the radiologists. In addition, there were larger variations in image preferences among the radiologists, as compared to the radiographers. Conclusions: Radiographers revealed a more consistent preference than the radiologists with respect to image quality. There is a potential for image quality improvement by developing sets of image property criteria

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation

International Nuclear Information System (INIS)

Zhao, Zhanqi; Möller, Knut; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich

2014-01-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton–Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR C ) and (4) GREIT with individual thorax geometry (GR T ). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal–Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms. (paper)

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.

Science.gov (United States)

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2014-06-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.

Remote Sensing Digital Image Analysis An Introduction

CERN Document Server

Richards, John A

2013-01-01

Remote Sensing Digital Image Analysis provides the non-specialist with a treatment of the quantitative analysis of satellite and aircraft derived remotely sensed data. Since the first edition of the book there have been significant developments in the algorithms used for the processing and analysis of remote sensing imagery; nevertheless many of the fundamentals have substantially remained the same.  This new edition presents material that has retained value since those early days, along with new techniques that can be incorporated into an operational framework for the analysis of remote sensing data. The book is designed as a teaching text for the senior undergraduate and postgraduate student, and as a fundamental treatment for those engaged in research using digital image processing in remote sensing.  The presentation level is for the mathematical non-specialist.  Since the very great number of operational users of remote sensing come from the earth sciences communities, the text is pitched at a leve...

Morphological images analysis and chromosomic aberrations classification based on fuzzy logic

International Nuclear Information System (INIS)

Souza, Leonardo Peres

2011-01-01

This work has implemented a methodology for automation of images analysis of chromosomes of human cells irradiated at IEA-R1 nuclear reactor (located at IPEN, Sao Paulo, Brazil), and therefore subject to morphological aberrations. This methodology intends to be a tool for helping cytogeneticists on identification, characterization and classification of chromosomal metaphasic analysis. The methodology development has included the creation of a software application based on artificial intelligence techniques using Fuzzy Logic combined with image processing techniques. The developed application was named CHRIMAN and is composed of modules that contain the methodological steps which are important requirements in order to achieve an automated analysis. The first step is the standardization of the bi-dimensional digital image acquisition procedure through coupling a simple digital camera to the ocular of the conventional metaphasic analysis microscope. Second step is related to the image treatment achieved through digital filters application; storing and organization of information obtained both from image content itself, and from selected extracted features, for further use on pattern recognition algorithms. The third step consists on characterizing, counting and classification of stored digital images and extracted features information. The accuracy in the recognition of chromosome images is 93.9%. This classification is based on classical standards obtained at Buckton [1973], and enables support to geneticist on chromosomic analysis procedure, decreasing analysis time, and creating conditions to include this method on a broader evaluation system on human cell damage due to ionizing radiation exposure. (author)

On the applicability of numerical image mapping for PIV image analysis near curved interfaces

International Nuclear Information System (INIS)

Masullo, Alessandro; Theunissen, Raf

2017-01-01

This paper scrutinises the general suitability of image mapping for particle image velocimetry (PIV) applications. Image mapping can improve PIV measurement accuracy by eliminating overlap between the PIV interrogation windows and an interface, as illustrated by some examples in the literature. Image mapping transforms the PIV images using a curvilinear interface-fitted mesh prior to performing the PIV cross correlation. However, degrading effects due to particle image deformation and the Jacobian transformation inherent in the mapping along curvilinear grid lines have never been deeply investigated. Here, the implementation of image mapping from mesh generation to image resampling is presented in detail, and related error sources are analysed. Systematic comparison with standard PIV approaches shows that image mapping is effective only in a very limited set of flow conditions and geometries, and depends strongly on a priori knowledge of the boundary shape and streamlines. In particular, with strongly curved geometries or streamlines that are not parallel to the interface, the image-mapping approach is easily outperformed by more traditional image analysis methodologies invoking suitable spatial relocation of the obtained displacement vector. (paper)

New approach to gallbladder ultrasonic images analysis and lesions recognition.

Science.gov (United States)

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.

Extended -Regular Sequence for Automated Analysis of Microarray Images

Directory of Open Access Journals (Sweden)

Jin Hee-Jeong

2006-01-01

Full Text Available Microarray study enables us to obtain hundreds of thousands of expressions of genes or genotypes at once, and it is an indispensable technology for genome research. The first step is the analysis of scanned microarray images. This is the most important procedure for obtaining biologically reliable data. Currently most microarray image processing systems require burdensome manual block/spot indexing work. Since the amount of experimental data is increasing very quickly, automated microarray image analysis software becomes important. In this paper, we propose two automated methods for analyzing microarray images. First, we propose the extended -regular sequence to index blocks and spots, which enables a novel automatic gridding procedure. Second, we provide a methodology, hierarchical metagrid alignment, to allow reliable and efficient batch processing for a set of microarray images. Experimental results show that the proposed methods are more reliable and convenient than the commercial tools.

Fourier analysis: from cloaking to imaging

International Nuclear Information System (INIS)

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

2016-01-01

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

Multimodal Imaging Brain Connectivity Analysis (MIBCA toolbox

Directory of Open Access Journals (Sweden)

Andre Santos Ribeiro

2015-07-01

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

An instructional guide for leaf color analysis using digital imaging software

Science.gov (United States)

Paula F. Murakami; Michelle R. Turner; Abby K. van den Berg; Paul G. Schaberg

2005-01-01

Digital color analysis has become an increasingly popular and cost-effective method utilized by resource managers and scientists for evaluating foliar nutrition and health in response to environmental stresses. We developed and tested a new method of digital image analysis that uses Scion Image or NIH image public domain software to quantify leaf color. This...

Planning applications in image analysis

Science.gov (United States)

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.

Spatial compression algorithm for the analysis of very large multivariate images

Science.gov (United States)

Keenan, Michael R [Albuquerque, NM

2008-07-15

A method for spatially compressing data sets enables the efficient analysis of very large multivariate images. The spatial compression algorithms use a wavelet transformation to map an image into a compressed image containing a smaller number of pixels that retain the original image's information content. Image analysis can then be performed on a compressed data matrix consisting of a reduced number of significant wavelet coefficients. Furthermore, a block algorithm can be used for performing common operations more efficiently. The spatial compression algorithms can be combined with spectral compression algorithms to provide further computational efficiencies.

Image analysis of multiple moving wood pieces in real time

Science.gov (United States)

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.

Automated image analysis for quantification of filamentous bacteria

DEFF Research Database (Denmark)

Fredborg, Marlene; Rosenvinge, Flemming Schønning; Spillum, Erik

2015-01-01

in systems relying on colorimetry or turbidometry (such as Vitek-2, Phoenix, MicroScan WalkAway). The objective was to examine an automated image analysis algorithm for quantification of filamentous bacteria using the 3D digital microscopy imaging system, oCelloScope. Results Three E. coli strains displaying...

Occupancy Analysis of Sports Arenas Using Thermal Imaging

DEFF Research Database (Denmark)

Gade, Rikke; Jørgensen, Anders; Moeslund, Thomas B.

2012-01-01

This paper presents a system for automatic analysis of the occupancy of sports arenas. By using a thermal camera for image capturing the number of persons and their location on the court are found without violating any privacy issues. The images are binarised with an automatic threshold method...

Study of TCP densification via image analysis

International Nuclear Information System (INIS)

Silva, R.C.; Alencastro, F.S.; Oliveira, R.N.; Soares, G.A.

2011-01-01

Among ceramic materials that mimic human bone, β-type tri-calcium phosphate (β-TCP) has shown appropriate chemical stability and superior resorption rate when compared to hydroxyapatite. In order to increase its mechanical strength, the material is sintered, under controlled time and temperature conditions, to obtain densification without phase change. In the present work, tablets were produced via uniaxial compression and then sintered at 1150°C for 2h. The analysis via XRD and FTIR showed that the sintered tablets were composed only by β-TCP. The SEM images were used for quantification of grain size and volume fraction of pores, via digital image analysis. The tablets showed small pore fraction (between 0,67% and 6,38%) and homogeneous grain size distribution (∼2μm). Therefore, the analysis method seems viable to quantify porosity and grain size. (author)

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

Science.gov (United States)

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.

Paediatric x-ray radiation dose reduction and image quality analysis

International Nuclear Information System (INIS)

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

2013-01-01

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. (paper)

Image enhancement of x-ray microscope using frequency spectrum analysis

International Nuclear Information System (INIS)

Li Wenjie; Chen Jie; Tian Jinping; Zhang Xiaobo; Liu Gang; Tian Yangchao; Liu Yijin; Wu Ziyu

2009-01-01

We demonstrate a new method for x-ray microscope image enhancement using frequency spectrum analysis. Fine sample characteristics are well enhanced with homogeneous visibility and better contrast from single image. This method is easy to implement and really helps to improve the quality of image taken by our imaging system.

Image enhancement of x-ray microscope using frequency spectrum analysis

Energy Technology Data Exchange (ETDEWEB)

Li Wenjie; Chen Jie; Tian Jinping; Zhang Xiaobo; Liu Gang; Tian Yangchao [National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, Anhui 230029 (China); Liu Yijin; Wu Ziyu, E-mail: wuzy@ihep.ac.c, E-mail: ychtian@ustc.edu.c [Institute of High Energy Physics, Chinese Academy of Science, Beijing 100049 (China)

2009-09-01

We demonstrate a new method for x-ray microscope image enhancement using frequency spectrum analysis. Fine sample characteristics are well enhanced with homogeneous visibility and better contrast from single image. This method is easy to implement and really helps to improve the quality of image taken by our imaging system.

ANALYSIS OF SST IMAGES BY WEIGHTED ENSEMBLE TRANSFORM KALMAN FILTER

OpenAIRE

Sai , Gorthi; Beyou , Sébastien; Memin , Etienne

2011-01-01

International audience; This paper presents a novel, efficient scheme for the analysis of Sea Surface Temperature (SST) ocean images. We consider the estimation of the velocity fields and vorticity values from a sequence of oceanic images. The contribution of this paper lies in proposing a novel, robust and simple approach based onWeighted Ensemble Transform Kalman filter (WETKF) data assimilation technique for the analysis of real SST images, that may contain coast regions or large areas of ...

General Staining and Segmentation Procedures for High Content Imaging and Analysis.

Science.gov (United States)

Chambers, Kevin M; Mandavilli, Bhaskar S; Dolman, Nick J; Janes, Michael S

2018-01-01

Automated quantitative fluorescence microscopy, also known as high content imaging (HCI), is a rapidly growing analytical approach in cell biology. Because automated image analysis relies heavily on robust demarcation of cells and subcellular regions, reliable methods for labeling cells is a critical component of the HCI workflow. Labeling of cells for image segmentation is typically performed with fluorescent probes that bind DNA for nuclear-based cell demarcation or with those which react with proteins for image analysis based on whole cell staining. These reagents, along with instrument and software settings, play an important role in the successful segmentation of cells in a population for automated and quantitative image analysis. In this chapter, we describe standard procedures for labeling and image segmentation in both live and fixed cell samples. The chapter will also provide troubleshooting guidelines for some of the common problems associated with these aspects of HCI.

Vector sparse representation of color image using quaternion matrix analysis.

Science.gov (United States)

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

2015-04-01

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

Imaging analysis of direct alanine uptake by rice seedlings

International Nuclear Information System (INIS)

Nihei, Naoto; Masuda, Sayaka; Rai, Hiroki; Nakanishi, Tomoko M.

2008-01-01

We presented alanine, a kind of amino acids, uptake by a rice seedling to study the basic mechanism of the organic fertilizer effectiveness in organic farming. The rice grown in the culture solution containing alanine as a nitrogen source absorbed alanine approximately two times faster than that grown with NH 4 + from analysis of 14 C-alanine images by Imaging Plate method. It was suggested that the active transport ability of the rice seeding was induced in roots by existence of alanine in the rhizosphere. The alanine uptake images of the rice roots were acquired every 5 minutes successively by the real-time autoradiography system we developed. The analysis of the successive images showed that alanine uptake was not uniform throughout the root but especially active at the root tip. (author)

A hyperspectral image analysis workbench for environmental science applications

Energy Technology Data Exchange (ETDEWEB)

Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.

1992-10-01

A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or ``hyperspectral`` imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne`s Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image ``texture spectra`` derived from fractal signatures computed for subimage tiles at each wavelength.

Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

Energy Technology Data Exchange (ETDEWEB)

Drukker, Karen, E-mail: kdrukker@uchicago.edu; Giger, Maryellen L.; Li, Hui [Department of Radiology, University of Chicago, Chicago, Illinois 60637 (United States); Duewer, Fred; Malkov, Serghei; Joe, Bonnie; Kerlikowske, Karla; Shepherd, John A. [Radiology Department, University of California, San Francisco, California 94143 (United States); Flowers, Chris I. [Department of Radiology, University of South Florida, Tampa, Florida 33612 (United States); Drukteinis, Jennifer S. [Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612 (United States)

2014-03-15

Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy. Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, “QIA alone,” (2) the three-compartment breast (3CB) composition measure—derived from the dual-energy mammography—of water, lipid, and protein thickness were assessed, “3CB alone”, and (3) information from QIA and 3CB was combined, “QIA + 3CB.” Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland–Altman plots, and Receiver Operating Characteristic (ROC) analysis. Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the “QIA alone” method, 0.72 (0.07) for “3CB alone” method, and 0.86 (0.04) for “QIA+3CB” combined. The difference in AUC was 0.043 between “QIA + 3CB” and “QIA alone” but failed to reach statistical significance (95% confidence interval [–0.17 to + 0.26]). Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.

Digital transplantation pathology: combining whole slide imaging, multiplex staining and automated image analysis.

Science.gov (United States)

Isse, K; Lesniak, A; Grama, K; Roysam, B; Minervini, M I; Demetris, A J

2012-01-01

Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. "-Omics" analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: (a) spatial-temporal relationships; (b) rare events/cells; (c) complex structural context; and (d) integration into a "systems" model. Nevertheless, except for immunostaining, no transformative advancements have "modernized" routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology-global "-omic" analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes. ©Copyright 2011 The American Society of Transplantation and the American Society of Transplant Surgeons.

Computerized analysis of brain perfusion parameter images

International Nuclear Information System (INIS)

Turowski, B.; Haenggi, D.; Wittsack, H.J.; Beck, A.; Aurich, V.

2007-01-01

Purpose: The development of a computerized method which allows a direct quantitative comparison of perfusion parameters. The display should allow a clear direct comparison of brain perfusion parameters in different vascular territories and over the course of time. The analysis is intended to be the basis for further evaluation of cerebral vasospasm after subarachnoid hemorrhage (SAH). The method should permit early diagnosis of cerebral vasospasm. Materials and Methods: The Angiotux 2D-ECCET software was developed with a close cooperation between computer scientists and clinicians. Starting from parameter images of brain perfusion, the cortex was marked, segmented and assigned to definite vascular territories. The underlying values were averages for each segment and were displayed in a graph. If a follow-up was available, the mean values of the perfusion parameters were displayed in relation to time. The method was developed under consideration of CT perfusion values but is applicable for other methods of perfusion imaging. Results: Computerized analysis of brain perfusion parameter images allows an immediate comparison of these parameters and follow-up of mean values in a clear and concise manner. Values are related to definite vascular territories. The tabular output facilitates further statistic evaluations. The computerized analysis is precisely reproducible, i. e., repetitions result in exactly the same output. (orig.)

Image decomposition as a tool for validating stress analysis models

Directory of Open Access Journals (Sweden)

Mottershead J.

2010-06-01

Full Text Available It is good practice to validate analytical and numerical models used in stress analysis for engineering design by comparison with measurements obtained from real components either in-service or in the laboratory. In reality, this critical step is often neglected or reduced to placing a single strain gage at the predicted hot-spot of stress. Modern techniques of optical analysis allow full-field maps of displacement, strain and, or stress to be obtained from real components with relative ease and at modest cost. However, validations continued to be performed only at predicted and, or observed hot-spots and most of the wealth of data is ignored. It is proposed that image decomposition methods, commonly employed in techniques such as fingerprinting and iris recognition, can be employed to validate stress analysis models by comparing all of the key features in the data from the experiment and the model. Image decomposition techniques such as Zernike moments and Fourier transforms have been used to decompose full-field distributions for strain generated from optical techniques such as digital image correlation and thermoelastic stress analysis as well as from analytical and numerical models by treating the strain distributions as images. The result of the decomposition is 101 to 102 image descriptors instead of the 105 or 106 pixels in the original data. As a consequence, it is relatively easy to make a statistical comparison of the image descriptors from the experiment and from the analytical/numerical model and to provide a quantitative assessment of the stress analysis.

The analysis of image feature robustness using cometcloud

Directory of Open Access Journals (Sweden)

Xin Qi

2012-01-01

Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

Quantitative Image Simulation and Analysis of Nanoparticles

DEFF Research Database (Denmark)

Madsen, Jacob; Hansen, Thomas Willum

Microscopy (HRTEM) has become a routine analysis tool for structural characterization at atomic resolution, and with the recent development of in-situ TEMs, it is now possible to study catalytic nanoparticles under reaction conditions. However, the connection between an experimental image, and the underlying...... physical phenomena or structure is not always straightforward. The aim of this thesis is to use image simulation to better understand observations from HRTEM images. Surface strain is known to be important for the performance of nanoparticles. Using simulation, we estimate of the precision and accuracy...... of strain measurements from TEM images, and investigate the stability of these measurements to microscope parameters. This is followed by our efforts toward simulating metal nanoparticles on a metal-oxide support using the Charge Optimized Many Body (COMB) interatomic potential. The simulated interface...

Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review

Energy Technology Data Exchange (ETDEWEB)

Michallek, Florian; Dewey, Marc [Humboldt-Universitaet zu Berlin, Freie Universitaet Berlin, Charite - Universitaetsmedizin Berlin, Medical School, Department of Radiology, Berlin (Germany)

2014-01-15

To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application. A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science. Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis. Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting. (orig.)

Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review

International Nuclear Information System (INIS)

Michallek, Florian; Dewey, Marc

2014-01-01

To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application. A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science. Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis. Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting. (orig.)

Independent component analysis based filtering for penumbral imaging

International Nuclear Information System (INIS)

Chen Yenwei; Han Xianhua; Nozaki, Shinya

2004-01-01

We propose a filtering based on independent component analysis (ICA) for Poisson noise reduction. In the proposed filtering, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (shrinkage). The proposed filter, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simulation results and experimental results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters

MORPHOLOGY BY IMAGE ANALYSIS K. Belaroui and M. N Pons ...

African Journals Online (AJOL)

31 déc. 2012 ... Keywords: Characterization; particle size; morphology; image analysis; porous media. 1. INTRODUCTION. La puissance de l'analyse d'images comme ... en une image numérique au moyen d'un convertisseur analogique digital (A/D). Les points de l'image sont disposés suivant une grille en réseau carré, ...

Traffic analysis and control using image processing

Science.gov (United States)

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.

Developments in Dynamic Analysis for quantitative PIXE true elemental imaging

International Nuclear Information System (INIS)

Ryan, C.G.

2001-01-01

Dynamic Analysis (DA) is a method for projecting quantitative major and trace element images from PIXE event data-streams (off-line or on-line) obtained using the Nuclear Microprobe. The method separates full elemental spectral signatures to produce images that strongly reject artifacts due to overlapping elements, detector effects (such as escape peaks and tailing) and background. The images are also quantitative, stored in ppm-charge units, enabling images to be directly interrogated for the concentrations of all elements in areas of the images. Recent advances in the method include the correction for changing X-ray yields due to varying sample compositions across the image area and the construction of statistical variance images. The resulting accuracy of major element concentrations extracted directly from these images is better than 3% relative as determined from comparisons with electron microprobe point analysis. These results are complemented by error estimates derived from the variance images together with detection limits. This paper provides an update of research on these issues, introduces new software designed to make DA more accessible, and illustrates the application of the method to selected geological problems.

Feed particle size evaluation: conventional approach versus digital holography based image analysis

Directory of Open Access Journals (Sweden)

Vittorio Dell’Orto

2010-01-01

Full Text Available The aim of this study was to evaluate the application of image analysis approach based on digital holography in defining particle size in comparison with the sieve shaker method (sieving method as reference method. For this purpose ground corn meal was analyzed by a sieve shaker Retsch VS 1000 and by image analysis approach based on digital holography. Particle size from digital holography were compared with results obtained by screen (sieving analysis for each of size classes by a cumulative distribution plot. Comparison between particle size values obtained by sieving method and image analysis indicated that values were comparable in term of particle size information, introducing a potential application for digital holography and image analysis in feed industry.

Quantitative analysis of γ-oryzanol content in cold pressed rice bran oil by TLC-image analysis method.

Science.gov (United States)

Sakunpak, Apirak; Suksaeree, Jirapornchai; Monton, Chaowalit; Pathompak, Pathamaporn; Kraisintu, Krisana

2014-02-01

To develop and validate an image analysis method for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. TLC-densitometric and TLC-image analysis methods were developed, validated, and used for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. The results obtained by these two different quantification methods were compared by paired t-test. Both assays provided good linearity, accuracy, reproducibility and selectivity for determination of γ-oryzanol. The TLC-densitometric and TLC-image analysis methods provided a similar reproducibility, accuracy and selectivity for the quantitative determination of γ-oryzanol in cold pressed rice bran oil. A statistical comparison of the quantitative determinations of γ-oryzanol in samples did not show any statistically significant difference between TLC-densitometric and TLC-image analysis methods. As both methods were found to be equal, they therefore can be used for the determination of γ-oryzanol in cold pressed rice bran oil.

An optimal big data workflow for biomedical image analysis

Directory of Open Access Journals (Sweden)

Aurelle Tchagna Kouanou

Full Text Available Background and objective: In the medical field, data volume is increasingly growing, and traditional methods cannot manage it efficiently. In biomedical computation, the continuous challenges are: management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of data, using machine learning and artificial intelligence techniques. It also allows a quick access to data using the NoSQL database. Thus, big data technologies include new frameworks to process medical data in a manner similar to biomedical images. It becomes very important to develop methods and/or architectures based on big data technologies, for a complete processing of biomedical image data. Method: This paper describes big data analytics for biomedical images, shows examples reported in the literature, briefly discusses new methods used in processing, and offers conclusions. We argue for adapting and extending related work methods in the field of big data software, using Hadoop and Spark frameworks. These provide an optimal and efficient architecture for biomedical image analysis. This paper thus gives a broad overview of big data analytics to automate biomedical image diagnosis. A workflow with optimal methods and algorithm for each step is proposed. Results: Two architectures for image classification are suggested. We use the Hadoop framework to design the first, and the Spark framework for the second. The proposed Spark architecture allows us to develop appropriate and efficient methods to leverage a large number of images for classification, which can be customized with respect to each other. Conclusions: The proposed architectures are more complete, easier, and are adaptable in all of the steps from conception. The obtained Spark architecture is the most complete, because it facilitates the implementation of algorithms with its embedded libraries. Keywords: Biomedical images, Big

A hyperspectral image analysis workbench for environmental science applications

Energy Technology Data Exchange (ETDEWEB)

Christiansen, J.H.; Zawada, D.G.; Simunich, K.L.; Slater, J.C.

1992-01-01

A significant challenge to the information sciences is to provide more powerful and accessible means to exploit the enormous wealth of data available from high-resolution imaging spectrometry, or hyperspectral'' imagery, for analysis, for mapping purposes, and for input to environmental modeling applications. As an initial response to this challenge, Argonne's Advanced Computer Applications Center has developed a workstation-based prototype software workbench which employs Al techniques and other advanced approaches to deduce surface characteristics and extract features from the hyperspectral images. Among its current capabilities, the prototype system can classify pixels by abstract surface type. The classification process employs neural network analysis of inputs which include pixel spectra and a variety of processed image metrics, including image texture spectra'' derived from fractal signatures computed for subimage tiles at each wavelength.

Three-dimensional analysis and display of medical images

International Nuclear Information System (INIS)

Bajcsy, R.

1985-01-01

Until recently, the most common medical images were X-rays on film analyzed by an expert, ususally a radiologist, who used, in addition to his/her visual perceptual abilities, knowledge obtained through medical studies, and experience. Today, however, with the advent of various imaging techniques, X-ray computerized axial tomographs (CAT), positron emission tomographs (PET), ultrasound tomographs, nuclear magnetic resonance tomographs (NMR), just to mention a few, the images are generated by computers and displayed on computer-controlled devices; so it is appropriate to think about more quantitative and perhaps automated ways of data analysis. Furthermore, since the data are generated by computer, it is only natural to take advantage of the computer for analysis purposes. In addition, using the computer, one can analyze more data and relate different modalities from the same subject, such as, for example, comparing the CAT images with PET images from the same subject. In the next section (The PET Scanner) the authors shall only briefly mention with appropriate references the modeling of the positron emission tomographic scanner, since this imaging technique is not as widely described in the literature as the CAT scanner. The modeling of the interpreter is not going to be mentioned, since it is a topic that by itself deserves a full paper; see, for example, Pizer [1981]. The thrust of this chapter is on modeling the organs that are being imaged and the matching techniques between the model and the data. The image data is from CAT and PET scans. Although the authors believe that their techniques are applicable to any organ of the human body, the examples are only from the brain

Influence of Averaging Preprocessing on Image Analysis with a Markov Random Field Model

Science.gov (United States)

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.

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

Science.gov (United States)

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.

3D Image Analysis of Geomaterials using Confocal Microscopy

Science.gov (United States)

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

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.

Science.gov (United States)

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

Imaging spectroscopic analysis at the Advanced Light Source

International Nuclear Information System (INIS)

MacDowell, A. A.; Warwick, T.; Anders, S.; Lamble, G.M.; Martin, M.C.; McKinney, W.R.; Padmore, H.A.

1999-01-01

One of the major advances at the high brightness third generation synchrotrons is the dramatic improvement of imaging capability. There is a large multi-disciplinary effort underway at the ALS to develop imaging X-ray, UV and Infra-red spectroscopic analysis on a spatial scale from. a few microns to 10nm. These developments make use of light that varies in energy from 6meV to 15KeV. Imaging and spectroscopy are finding applications in surface science, bulk materials analysis, semiconductor structures, particulate contaminants, magnetic thin films, biology and environmental science. This article is an overview and status report from the developers of some of these techniques at the ALS. The following table lists all the currently available microscopes at the. ALS. This article will describe some of the microscopes and some of the early applications

Visual Analytics Applied to Image Analysis : From Segmentation to Classification

NARCIS (Netherlands)

Rauber, Paulo

2017-01-01

Image analysis is the field of study concerned with extracting information from images. This field is immensely important for commercial and scientific applications, from identifying people in photographs to recognizing diseases in medical images. The goal behind the work presented in this thesis is

Analysis of engineering drawings and raster map images

CERN Document Server

Henderson, Thomas C

2013-01-01

Presents up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps Discusses automatic engineering drawing and map analysis techniques Covers detailed accounts of the use of unsupervised segmentation algorithms to map images

GANALYZER: A TOOL FOR AUTOMATIC GALAXY IMAGE ANALYSIS

International Nuclear Information System (INIS)

Shamir, Lior

2011-01-01

We describe Ganalyzer, a model-based tool that can automatically analyze and classify galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the radial intensity plot, and then computing the slopes of the peaks detected in the radial intensity plot to measure the spirality of the galaxy and determine its morphological class. Unlike algorithms that are based on machine learning, Ganalyzer is based on measuring the spirality of the galaxy, a task that is difficult to perform manually, and in many cases can provide a more accurate analysis compared to manual observation. Ganalyzer is simple to use, and can be easily embedded into other image analysis applications. Another advantage is its speed, which allows it to analyze ∼10,000,000 galaxy images in five days using a standard modern desktop computer. These capabilities can make Ganalyzer a useful tool in analyzing large data sets of galaxy images collected by autonomous sky surveys such as SDSS, LSST, or DES. The software is available for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and the data used in the experiment are available at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.

Ganalyzer: A Tool for Automatic Galaxy Image Analysis

Science.gov (United States)

Shamir, Lior

2011-08-01

We describe Ganalyzer, a model-based tool that can automatically analyze and classify galaxy images. Ganalyzer works by separating the galaxy pixels from the background pixels, finding the center and radius of the galaxy, generating the radial intensity plot, and then computing the slopes of the peaks detected in the radial intensity plot to measure the spirality of the galaxy and determine its morphological class. Unlike algorithms that are based on machine learning, Ganalyzer is based on measuring the spirality of the galaxy, a task that is difficult to perform manually, and in many cases can provide a more accurate analysis compared to manual observation. Ganalyzer is simple to use, and can be easily embedded into other image analysis applications. Another advantage is its speed, which allows it to analyze ~10,000,000 galaxy images in five days using a standard modern desktop computer. These capabilities can make Ganalyzer a useful tool in analyzing large data sets of galaxy images collected by autonomous sky surveys such as SDSS, LSST, or DES. The software is available for free download at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer, and the data used in the experiment are available at http://vfacstaff.ltu.edu/lshamir/downloads/ganalyzer/GalaxyImages.zip.

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

Science.gov (United States)

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.

Image Processing Tools for Improved Visualization and Analysis of Remotely Sensed Images for Agriculture and Forest Classifications

OpenAIRE

SINHA G. R.

2017-01-01

This paper suggests Image Processing tools for improved visualization and better analysis of remotely sensed images. There are methods already available in literature for the purpose but the most important challenge among the limitations is lack of robustness. We propose an optimal method for image enhancement of the images using fuzzy based approaches and few optimization tools. The segmentation images subsequently obtained after de-noising will be classified into distinct information and th...

Analysis of high-throughput plant image data with the information system IAP

Directory of Open Access Journals (Sweden)

Klukas Christian

2012-06-01

Full Text Available This work presents a sophisticated information system, the Integrated Analysis Platform (IAP, an approach supporting large-scale image analysis for different species and imaging systems. In its current form, IAP supports the investigation of Maize, Barley and Arabidopsis plants based on images obtained in different spectra.

Insight into dynamic genome imaging: Canonical framework identification and high-throughput analysis.

Science.gov (United States)

Ronquist, Scott; Meixner, Walter; Rajapakse, Indika; Snyder, John

2017-07-01

The human genome is dynamic in structure, complicating researcher's attempts at fully understanding it. Time series "Fluorescent in situ Hybridization" (FISH) imaging has increased our ability to observe genome structure, but due to cell type and experimental variability this data is often noisy and difficult to analyze. Furthermore, computational analysis techniques are needed for homolog discrimination and canonical framework detection, in the case of time-series images. In this paper we introduce novel ideas for nucleus imaging analysis, present findings extracted using dynamic genome imaging, and propose an objective algorithm for high-throughput, time-series FISH imaging. While a canonical framework could not be detected beyond statistical significance in the analyzed dataset, a mathematical framework for detection has been outlined with extension to 3D image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

Container-Based Clinical Solutions for Portable and Reproducible Image Analysis.

Science.gov (United States)

Matelsky, Jordan; Kiar, Gregory; Johnson, Erik; Rivera, Corban; Toma, Michael; Gray-Roncal, William

2018-05-08

Medical imaging analysis depends on the reproducibility of complex computation. Linux containers enable the abstraction, installation, and configuration of environments so that software can be both distributed in self-contained images and used repeatably by tool consumers. While several initiatives in neuroimaging have adopted approaches for creating and sharing more reliable scientific methods and findings, Linux containers are not yet mainstream in clinical settings. We explore related technologies and their efficacy in this setting, highlight important shortcomings, demonstrate a simple use-case, and endorse the use of Linux containers for medical image analysis.

MO-FG-202-06: Improving the Performance of Gamma Analysis QA with Radiomics- Based Image Analysis

Energy Technology Data Exchange (ETDEWEB)

Wootton, L; Nyflot, M; Ford, E [University of Washington Department of Radiation Oncology, Seattle, WA (United States); Chaovalitwongse, A [University of Washington Department of Industrial and Systems Engineering, Seattle, Washington (United States); University of Washington Department of Radiology, Seattle, WA (United States); Li, N [University of Washington Department of Industrial and Systems Engineering, Seattle, Washington (United States)

2016-06-15

Purpose: The use of gamma analysis for IMRT quality assurance has well-known limitations. Traditionally, a simple thresholding technique is used to evaluated passing criteria. However, like any image the gamma distribution is rich in information which thresholding mostly discards. We therefore propose a novel method of analyzing gamma images that uses quantitative image features borrowed from radiomics, with the goal of improving error detection. Methods: 368 gamma images were generated from 184 clinical IMRT beams. For each beam the dose to a phantom was measured with EPID dosimetry and compared to the TPS dose calculated with and without normally distributed (2mm sigma) errors in MLC positions. The magnitude of 17 intensity histogram and size-zone radiomic features were derived from each image. The features that differed most significantly between image sets were determined with ROC analysis. A linear machine-learning model was trained on these features to classify images as with or without errors on 180 gamma images.The model was then applied to an independent validation set of 188 additional gamma distributions, half with and half without errors. Results: The most significant features for detecting errors were histogram kurtosis (p=0.007) and three size-zone metrics (p<1e-6 for each). The sizezone metrics detected clusters of high gamma-value pixels under mispositioned MLCs. The model applied to the validation set had an AUC of 0.8, compared to 0.56 for traditional gamma analysis with the decision threshold restricted to 98% or less. Conclusion: A radiomics-based image analysis method was developed that is more effective in detecting error than traditional gamma analysis. Though the pilot study here considers only MLC position errors, radiomics-based methods for other error types are being developed, which may provide better error detection and useful information on the source of detected errors. This work was partially supported by a grant from the Agency for

Immunocytochemical Characterization of Alzheimer Disease Hallmarks in APP/PS1 Transgenic Mice Treated with a New Anti-Amyloid-β Vaccine

Directory of Open Access Journals (Sweden)

Iván Carrera

2013-01-01

Full Text Available APP/PS1 double-transgenic mouse models of Alzheimer’s disease (AD, which overexpress mutated forms of the gene for human amyloid precursor protein (APP and presenilin 1 (PS1, have provided robust neuropathological hallmarks of AD-like pattern at early ages. This study characterizes immunocytochemical patterns of AD mouse brain as a model for human AD treated with the EB101 vaccine. In this novel vaccine, a new approach has been taken to circumvent past failures by judiciously selecting an adjuvant consisting of a physiological matrix embedded in liposomes, composed of naturally occurring phospholipids (phosphatidylcholine, phosphatidylglycerol, and cholesterol. Our findings showed that administration of amyloid-β1−42 (Aβ and sphingosine-1-phosphate emulsified in liposome complex (EB101 to APP/PS1 mice before onset of Aβ deposition (7 weeks of age and/or at an older age (35 weeks of age is effective in halting the progression and clearing the AD-like neuropathological hallmarks. Passive immunization with EB101 did not activate inflammatory responses from the immune system and astrocytes. Consistent with a decreased inflammatory background, the basal immunological interaction between the T cells and the affected areas (hippocampus in the brain of treated mice was notably reduced. These results demonstrate that immunization with EB101 vaccine prevents and attenuates AD neuropathology in this type of double-transgenic mice.

A Survey on Deep Learning in Medical Image Analysis

NARCIS (Netherlands)

Litjens, G.J.; Kooi, T.; Ehteshami Bejnordi, B.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; Laak, J.A.W.M. van der; Ginneken, B. van; Sanchez, C.I.

2017-01-01

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared

Development of motion image prediction method using principal component analysis

International Nuclear Information System (INIS)

Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

2012-01-01

Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

Subsurface offset behaviour in velocity analysis with extended reflectivity images

NARCIS (Netherlands)

Mulder, W.A.

2013-01-01

Migration velocity analysis with the constant-density acoustic wave equation can be accomplished by the focusing of extended migration images, obtained by introducing a subsurface shift in the imaging condition. A reflector in a wrong velocity model will show up as a curve in the extended image. In

Fast and objective detection and analysis of structures in downhole images

Science.gov (United States)

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.

Multi spectral imaging analysis for meat spoilage discrimination

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Carstensen, Jens Michael; Papadopoulou, Olga

classification methods: Naive Bayes Classifier as a reference model, Canonical Discriminant Analysis (CDA) and Support Vector Classification (SVC). As the final step, generalization of the models was performed using k-fold validation (k=10). Results showed that image analysis provided good discrimination of meat......In the present study, fresh beef fillets were purchased from a local butcher shop and stored aerobically and in modified atmosphere packaging (MAP, CO2 40%/O2 30%/N2 30%) at six different temperatures (0, 4, 8, 12, 16 and 20°C). Microbiological analysis in terms of total viable counts (TVC......) was performed in parallel with videometer image snapshots and sensory analysis. Odour and colour characteristics of meat were determined by a test panel and attributed into three pre-characterized quality classes, namely Fresh; Semi Fresh and Spoiled during the days of its shelf life. So far, different...

Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

Science.gov (United States)

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.

PIZZARO: Forensic analysis and restoration of image and video data

Czech Academy of Sciences Publication Activity Database

Kamenický, Jan; Bartoš, Michal; Flusser, Jan; Mahdian, Babak; Kotera, Jan; Novozámský, Adam; Saic, Stanislav; Šroubek, Filip; Šorel, Michal; Zita, Aleš; Zitová, Barbara; Šíma, Z.; Švarc, P.; Hořínek, J.

2016-01-01

Roč. 264, č. 1 (2016), s. 153-166 ISSN 0379-0738 R&D Projects: GA MV VG20102013064; GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Image forensic analysis * Image restoration * Image tampering detection * Image source identification Subject RIV: JD - Computer Applications, Robotics Impact factor: 1.989, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/kamenicky-0459504.pdf

Image analysis in the evaluation of the physiological potential of maize seeds1

Directory of Open Access Journals (Sweden)

Crislaine Aparecida Gomes Pinto

Full Text Available The Seed Analysis System (SAS is used in the image analysis of seeds and seedlings, and has the potential for use in the control of seed quality. The aim of this research was to adapt the methodology of image analysis of maize seedlings by SAS, and to verify the potential use of this equipment in the evaluation of the physiological potential of maize seeds. Nine batches of two maize hybrids were characterised by means of the following tests and determinations: germination, first count, accelerated ageing, cold test, seedling emergence at 25 and 30ºC, and speed of emergence index. The image analysis experiment was carried out in a factorial scheme of 9 batches x 4 methods of analysis of the seedling images (with and without the use of NWF as substrate, and with and without manual correction of the images. Images of the seedlings were evaluated using the average lengths of the coleoptile, roots and seedlings; and by the automatic and manual indices of vigour, uniformity and growth produced by the SAS. Use of blue NWF afffects the initial development of maize seedlings. The physiological potential of maize seeds can be evaluated in seedlings which are seeded on white paper towels at a temperature of 25 °C and evaluated on the third day. Image analysis should be carried out with the SAS software using automatic calibration and with no correction of the seedling images. Use of SAS equipment for the analysis of seedling images is a potential tool in evaluating the physiological quality of maize seeds.

Adaptive multiresolution Hermite-Binomial filters for image edge and texture analysis

NARCIS (Netherlands)

Gu, Y.H.; Katsaggelos, A.K.

1994-01-01

A new multiresolution image analysis approach using adaptive Hermite-Binomial filters is presented in this paper. According to the local image structural and textural properties, the analysis filter kernels are made adaptive both in their scales and orders. Applications of such an adaptive filtering

Digital image sequence processing, compression, and analysis

CERN Document Server

Reed, Todd R

2004-01-01

IntroductionTodd R. ReedCONTENT-BASED IMAGE SEQUENCE REPRESENTATIONPedro M. Q. Aguiar, Radu S. Jasinschi, José M. F. Moura, andCharnchai PluempitiwiriyawejTHE COMPUTATION OF MOTIONChristoph Stiller, Sören Kammel, Jan Horn, and Thao DangMOTION ANALYSIS AND DISPLACEMENT ESTIMATION IN THE FREQUENCY DOMAINLuca Lucchese and Guido Maria CortelazzoQUALITY OF SERVICE ASSESSMENT IN NEW GENERATION WIRELESS VIDEO COMMUNICATIONSGaetano GiuntaERROR CONCEALMENT IN DIGITAL VIDEOFrancesco G.B. De NataleIMAGE SEQUENCE RESTORATION: A WIDER PERSPECTIVEAnil KokaramVIDEO SUMMARIZATIONCuneyt M. Taskiran and Edward

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

Science.gov (United States)

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.

Transfer function analysis of positron-emitting tracer imaging system (PETIS) data

International Nuclear Information System (INIS)

Keutgen, N.; Matsuhashi, S.; Mizuniwa, C.; Ito, T.; Fujimura, T.; Ishioka, N.S.; Watanabe, S.; Sekine, T.; Uchida, H.; Hashimoto, S.

2002-01-01

Quantitative analysis of the two-dimensional image data obtained with the positron-emitting tracer imaging system (PETIS) for plant physiology has been carried out using a transfer function analysis method. While a cut leaf base of Chinese chive (Allium tuberosum Rottler) or a cut stem of soybean (Glycine max L.) was immersed in an aqueous solution containing the [ 18 F] F - ion or [ 13 N]NO 3 - ion, tracer images of the leaf of Chinese chive and the trifoliate of soybean were recorded with PETIS. From the time sequence of images, the tracer transfer function was estimated from which the speed of tracer transport and the fraction moved between specified image positions were deduced

Multifractal analysis of three-dimensional histogram from color images

International Nuclear Information System (INIS)

Chauveau, Julien; Rousseau, David; Richard, Paul; Chapeau-Blondeau, Francois

2010-01-01

Natural images, especially color or multicomponent images, are complex information-carrying signals. To contribute to the characterization of this complexity, we investigate the possibility of multiscale organization in the colorimetric structure of natural images. This is realized by means of a multifractal analysis applied to the three-dimensional histogram from natural color images. The observed behaviors are confronted to those of reference models with known multifractal properties. We use for this purpose synthetic random images with trivial monofractal behavior, and multidimensional multiplicative cascades known for their actual multifractal behavior. The behaviors observed on natural images exhibit similarities with those of the multifractal multiplicative cascades and display the signature of elaborate multiscale organizations stemming from the histograms of natural color images. This type of characterization of colorimetric properties can be helpful to various tasks of digital image processing, as for instance modeling, classification, indexing.

Facial Image Analysis in Anthropology: A Review

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2011-01-01

Roč. 49, č. 2 (2011), s. 141-153 ISSN 0323-1119 Institutional support: RVO:67985807 Keywords : face * computer-assisted methods * template matching * geometric morphopetrics * robust image analysis Subject RIV: IN - Informatics, Computer Science

Flexibility analysis in adolescent idiopathic scoliosis on side-bending images using the EOS imaging system.

Science.gov (United States)

Hirsch, C; Ilharreborde, B; Mazda, K

2016-06-01

Analysis of preoperative flexibility in adolescent idiopathic scoliosis (AIS) is essential to classify the curves, determine their structurality, and select the fusion levels during preoperative planning. Side-bending x-rays are the gold standard for the analysis of preoperative flexibility. The objective of this study was to examine the feasibility and performance of side-bending images taken in the standing position using the EOS imaging system. All patients who underwent preoperative assessment between April 2012 and January 2013 for AIS were prospectively included in the study. The work-up included standing AP and lateral EOS x-rays of the spine, standard side-bending x-rays in the supine position, and standing bending x-rays in the EOS booth. The irradiation dose was measured for each of the tests. Two-dimensional reducibility of the Cobb angle was measured on both types of bending x-rays. The results were based on the 50 patients in the study. No significant difference was demonstrated for reducibility of the Cobb angle between the standing side-bending images with the EOS imaging system and those in the supine position for all types of Lenke deformation. The irradiation dose was five times lower during the EOS bending imaging. The standing side-bending images in the EOS device contributed the same results as the supine images, with five times less irradiation. They should therefore be used in clinical routine. 2. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Pathological diagnosis of bladder cancer by image analysis of hypericin induced fluorescence cystoscopic images

Science.gov (United States)

Kah, James C. Y.; Olivo, Malini C.; Lau, Weber K. O.; Sheppard, Colin J. R.

2005-08-01

Photodynamic diagnosis of bladder carcinoma based on hypericin fluorescence cystoscopy has shown to have a higher degree of sensitivity for the detection of flat bladder carcinoma compared to white light cystoscopy. The potential of the photosensitizer hypericin-induced fluorescence in performing non-invasive optical biopsy to grade bladder cancer in vivo using fluorescence cystoscopic image analysis without surgical resection for tissue biopsy is investigated in this study. The correlation between tissue fluorescence and histopathology of diseased tissue was explored and a diagnostic algorithm based on fluorescence image analysis was developed to classify the bladder cancer without surgical resection for tissue biopsy. Preliminary results suggest a correlation between tissue fluorescence and bladder cancer grade. By combining both the red-to-blue and red-to-green intensity ratios into a 2D scatter plot yields an average sensitivity and specificity of around 70% and 85% respectively for pathological cancer grading of the three different grades of bladder cancer. Therefore, the diagnostic algorithm based on colorimetric intensity ratio analysis of hypericin fluorescence cystoscopic images developed in this preliminary study shows promising potential to optically diagnose and grade bladder cancer in vivo.

Cutting-edge analysis of extracellular microparticles using ImageStream(X) imaging flow cytometry.

Science.gov (United States)

Headland, Sarah E; Jones, Hefin R; D'Sa, Adelina S V; Perretti, Mauro; Norling, Lucy V

2014-06-10

Interest in extracellular vesicle biology has exploded in the past decade, since these microstructures seem endowed with multiple roles, from blood coagulation to inter-cellular communication in pathophysiology. In order for microparticle research to evolve as a preclinical and clinical tool, accurate quantification of microparticle levels is a fundamental requirement, but their size and the complexity of sample fluids present major technical challenges. Flow cytometry is commonly used, but suffers from low sensitivity and accuracy. Use of Amnis ImageStream(X) Mk II imaging flow cytometer afforded accurate analysis of calibration beads ranging from 1 μm to 20 nm; and microparticles, which could be observed and quantified in whole blood, platelet-rich and platelet-free plasma and in leukocyte supernatants. Another advantage was the minimal sample preparation and volume required. Use of this high throughput analyzer allowed simultaneous phenotypic definition of the parent cells and offspring microparticles along with real time microparticle generation kinetics. With the current paucity of reliable techniques for the analysis of microparticles, we propose that the ImageStream(X) could be used effectively to advance this scientific field.

Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

Directory of Open Access Journals (Sweden)

Hae-Gil Hwang

2005-01-01

Full Text Available Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS, and invasive ductal carcinoma (CA. To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each, respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

Mathematical methods in time series analysis and digital image processing

CERN Document Server

Kurths, J; Maass, P; Timmer, J

2008-01-01

The aim of this volume is to bring together research directions in theoretical signal and imaging processing developed rather independently in electrical engineering, theoretical physics, mathematics and the computer sciences. In particular, mathematically justified algorithms and methods, the mathematical analysis of these algorithms, and methods as well as the investigation of connections between methods from time series analysis and image processing are reviewed. An interdisciplinary comparison of these methods, drawing upon common sets of test problems from medicine and geophysical/enviromental sciences, is also addressed. This volume coherently summarizes work carried out in the field of theoretical signal and image processing. It focuses on non-linear and non-parametric models for time series as well as on adaptive methods in image processing.

Complete chromogen separation and analysis in double immunohistochemical stains using Photoshop-based image analysis.

Science.gov (United States)

Lehr, H A; van der Loos, C M; Teeling, P; Gown, A M

1999-01-01

Simultaneous detection of two different antigens on paraffin-embedded and frozen tissues can be accomplished by double immunohistochemistry. However, many double chromogen systems suffer from signal overlap, precluding definite signal quantification. To separate and quantitatively analyze the different chromogens, we imported images into a Macintosh computer using a CCD camera attached to a diagnostic microscope and used Photoshop software for the recognition, selection, and separation of colors. We show here that Photoshop-based image analysis allows complete separation of chromogens not only on the basis of their RGB spectral characteristics, but also on the basis of information concerning saturation, hue, and luminosity intrinsic to the digitized images. We demonstrate that Photoshop-based image analysis provides superior results compared to color separation using bandpass filters. Quantification of the individual chromogens is then provided by Photoshop using the Histogram command, which supplies information on the luminosity (corresponding to gray levels of black-and-white images) and on the number of pixels as a measure of spatial distribution. (J Histochem Cytochem 47:119-125, 1999)

Towards a framework for agent-based image analysis of remote-sensing data.

Science.gov (United States)

Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera

2015-04-03

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

Direct identification of pure penicillium species using image analysis

DEFF Research Database (Denmark)

Dørge, Thorsten Carlheim; Carstensen, Jens Michael; Frisvad, Jens Christian

2000-01-01

This paper presents a method for direct identification of fungal species solely by means of digital image analysis of colonies as seen after growth on a standard medium. The method described is completely automated and hence objective once digital images of the reference fungi have been establish...

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

Science.gov (United States)

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.

Image analysis of ocular fundus for retinopathy characterization

Energy Technology Data Exchange (ETDEWEB)

Ushizima, Daniela; Cuadros, Jorge

2010-02-05

Automated analysis of ocular fundus images is a common procedure in countries as England, including both nonemergency examination and retinal screening of patients with diabetes mellitus. This involves digital image capture and transmission of the images to a digital reading center for evaluation and treatment referral. In collaboration with the Optometry Department, University of California, Berkeley, we have tested computer vision algorithms to segment vessels and lesions in ground-truth data (DRIVE database) and hundreds of images of non-macular centric and nonuniform illumination views of the eye fundus from EyePACS program. Methods under investigation involve mathematical morphology (Figure 1) for image enhancement and pattern matching. Recently, we have focused in more efficient techniques to model the ocular fundus vasculature (Figure 2), using deformable contours. Preliminary results show accurate segmentation of vessels and high level of true-positive microaneurysms.

NDVI and Panchromatic Image Correlation Using Texture Analysis

Science.gov (United States)

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

A framework for noise-power spectrum analysis of multidimensional images

International Nuclear Information System (INIS)

Siewerdsen, J.H.; Cunningham, I.A.; Jaffray, D.A.

2002-01-01

A methodological framework for experimental analysis of the noise-power spectrum (NPS) of multidimensional images is presented that employs well-known properties of the n-dimensional (nD) Fourier transform. The approach is generalized to n dimensions, reducing to familiar cases for n=1 (e.g., time series) and n=2 (e.g., projection radiography) and demonstrated experimentally for two cases in which n=3 (viz., using an active matrix flat-panel imager for x-ray fluoroscopy and cone-beam CT to form three-dimensional (3D) images in spatiotemporal and volumetric domains, respectively). The relationship between fully nD NPS analysis and various techniques for analyzing a 'central slice' of the NPS is formulated in a manner that is directly applicable to measured nD data, highlights the effects of correlation, and renders issues of NPS normalization transparent. The spatiotemporal NPS of fluoroscopic images is analyzed under varying conditions of temporal correlation (image lag) to investigate the degree to which the NPS is reduced by such correlation. For first-frame image lag of ∼5-8 %, the NPS is reduced by ∼20% compared to the lag-free case. A simple model is presented that results in an approximate rule of thumb for computing the effect of image lag on NPS under conditions of spatiotemporal separability. The volumetric NPS of cone-beam CT images is analyzed under varying conditions of spatial correlation, controlled by adjustment of the reconstruction filter. The volumetric NPS is found to be highly asymmetric, exhibiting a ramp characteristic in transverse planes (typical of filtered back-projection) and a band-limited characteristic in the longitudinal direction (resulting from low-pass characteristics of the imager). Such asymmetry could have implications regarding the detectability of structures visualized in transverse versus sagittal or coronal planes. In all cases, appreciation of the full dimensionality of the image data is essential to obtaining

Sensory analysis for magnetic resonance-image analysis: Using human perception and cognition to segment and assess the interior of potatoes

DEFF Research Database (Denmark)

Martens, Harald; Thybo, A.K.; Andersen, H.J.

2002-01-01

were developed by the panel during preliminary training sessions, and consisted in definitions of various biological compartments inside the tubers. The results from the sensory and the computer-assisted image analyses of the shape and interior structure of the tubers were related to the experimental...... able to detect differences between varieties as well as storage times. The sensory image analysis gave better discrimination between varieties than the computer-assisted image analysis presently employed, and was easier to interpret. Some sensory descriptors could be predicted from the computer......-assisted image analysis. The present results offer new information about using sensory analysis of MR-images not only for food science but also for medical applications for analysing MR and X-ray images and for training of personnel, such as radiologists and radiographers. (C) 2002 Elsevier Science Ltd....

Quantitative Analysis in Nuclear Medicine Imaging

CERN Document Server

2006-01-01

This book provides a review of image analysis techniques as they are applied in the field of diagnostic and therapeutic nuclear medicine. Driven in part by the remarkable increase in computing power and its ready and inexpensive availability, this is a relatively new yet rapidly expanding field. Likewise, although the use of radionuclides for diagnosis and therapy has origins dating back almost to the discovery of natural radioactivity itself, radionuclide therapy and, in particular, targeted radionuclide therapy has only recently emerged as a promising approach for therapy of cancer and, to a lesser extent, other diseases. As effort has, therefore, been made to place the reviews provided in this book in a broader context. The effort to do this is reflected by the inclusion of introductory chapters that address basic principles of nuclear medicine imaging, followed by overview of issues that are closely related to quantitative nuclear imaging and its potential role in diagnostic and therapeutic applications. ...

Quantitative analysis of γ-oryzanol content in cold pressed rice bran oil by TLC-image analysis method

OpenAIRE

Sakunpak, Apirak; Suksaeree, Jirapornchai; Monton, Chaowalit; Pathompak, Pathamaporn; Kraisintu, Krisana

2014-01-01

Objective: To develop and validate an image analysis method for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. Methods: TLC-densitometric and TLC-image analysis methods were developed, validated, and used for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. The results obtained by these two different quantification methods were compared by paired t-test. Results: Both assays provided good linearity, accuracy, reproducibility and selectivity for dete...

Detailed analysis of latencies in image-based dynamic MLC tracking

International Nuclear Information System (INIS)

Poulsen, Per Rugaard; Cho, Byungchul; Sawant, Amit; Ruan, Dan; Keall, Paul J.

2010-01-01

Purpose: Previous measurements of the accuracy of image-based real-time dynamic multileaf collimator (DMLC) tracking show that the major contributor to errors is latency, i.e., the delay between target motion and MLC response. Therefore the purpose of this work was to develop a method for detailed analysis of latency contributions during image-based DMLC tracking. Methods: A prototype DMLC tracking system integrated with a linear accelerator was used for tracking a phantom with an embedded fiducial marker during treatment delivery. The phantom performed a sinusoidal motion. Real-time target localization was based on x-ray images acquired either with a portal imager or a kV imager mounted orthogonal to the treatment beam. Each image was stored in a file on the imaging workstation. A marker segmentation program opened the image file, determined the marker position in the image, and transferred it to the DMLC tracking program. This program estimated the three-dimensional target position by a single-imager method and adjusted the MLC aperture to the target position. Imaging intervals ΔT image from 150 to 1000 ms were investigated for both kV and MV imaging. After the experiments, the recorded images were synchronized with MLC log files generated by the MLC controller and tracking log files generated by the tracking program. This synchronization allowed temporal analysis of the information flow for each individual image from acquisition to completed MLC adjustment. The synchronization also allowed investigation of the MLC adjustment dynamics on a considerably finer time scale than the 50 ms time resolution of the MLC log files. Results: For ΔT image =150 ms, the total time from image acquisition to completed MLC adjustment was 380±9 ms for MV and 420±12 ms for kV images. The main part of this time was from image acquisition to completed image file writing (272 ms for MV and 309 ms for kV). Image file opening (38 ms), marker segmentation (4 ms), MLC position

Detailed analysis of latencies in image-based dynamic MLC tracking

Energy Technology Data Exchange (ETDEWEB)

Poulsen, Per Rugaard; Cho, Byungchul; Sawant, Amit; Ruan, Dan; Keall, Paul J. [Department of Radiation Oncology, Stanford University, Stanford, California 94305 and Department of Oncology and Department of Medical Physics, Aarhus University Hospital, 8000 Aarhus (Denmark); Department of Radiation Oncology, Stanford University, Stanford, California 94305 and Department of Radiation Oncology, Asan Medical Center, Seoul 138-736 (Korea, Republic of); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States)

2010-09-15

Purpose: Previous measurements of the accuracy of image-based real-time dynamic multileaf collimator (DMLC) tracking show that the major contributor to errors is latency, i.e., the delay between target motion and MLC response. Therefore the purpose of this work was to develop a method for detailed analysis of latency contributions during image-based DMLC tracking. Methods: A prototype DMLC tracking system integrated with a linear accelerator was used for tracking a phantom with an embedded fiducial marker during treatment delivery. The phantom performed a sinusoidal motion. Real-time target localization was based on x-ray images acquired either with a portal imager or a kV imager mounted orthogonal to the treatment beam. Each image was stored in a file on the imaging workstation. A marker segmentation program opened the image file, determined the marker position in the image, and transferred it to the DMLC tracking program. This program estimated the three-dimensional target position by a single-imager method and adjusted the MLC aperture to the target position. Imaging intervals {Delta}T{sub image} from 150 to 1000 ms were investigated for both kV and MV imaging. After the experiments, the recorded images were synchronized with MLC log files generated by the MLC controller and tracking log files generated by the tracking program. This synchronization allowed temporal analysis of the information flow for each individual image from acquisition to completed MLC adjustment. The synchronization also allowed investigation of the MLC adjustment dynamics on a considerably finer time scale than the 50 ms time resolution of the MLC log files. Results: For {Delta}T{sub image}=150 ms, the total time from image acquisition to completed MLC adjustment was 380{+-}9 ms for MV and 420{+-}12 ms for kV images. The main part of this time was from image acquisition to completed image file writing (272 ms for MV and 309 ms for kV). Image file opening (38 ms), marker segmentation (4 ms

Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard

2012-01-01

We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising...... procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...

MR image analysis: Longitudinal cardiac motion influences left ventricular measurements

International Nuclear Information System (INIS)

Berkovic, Patrick; Hemmink, Maarten; Parizel, Paul M.; Vrints, Christiaan J.; Paelinck, Bernard P.

2010-01-01

Background: Software for the analysis of left ventricular (LV) volumes and mass using border detection in short-axis images only, is hampered by through-plane cardiac motion. Therefore we aimed to evaluate software that involves longitudinal cardiac motion. Methods: Twenty-three consecutive patients underwent 1.5-Tesla cine magnetic resonance (MR) imaging of the entire heart in the long-axis and short-axis orientation with breath-hold steady-state free precession imaging. Offline analysis was performed using software that uses short-axis images (Medis MASS) and software that includes two-chamber and four-chamber images to involve longitudinal LV expansion and shortening (CAAS-MRV). Intraobserver and interobserver reproducibility was assessed by using Bland-Altman analysis. Results: Compared with MASS software, CAAS-MRV resulted in significantly smaller end-diastolic (156 ± 48 ml versus 167 ± 52 ml, p = 0.001) and end-systolic LV volumes (79 ± 48 ml versus 94 ± 52 ml, p < 0.001). In addition, CAAS-MRV resulted in higher LV ejection fraction (52 ± 14% versus 46 ± 13%, p < 0.001) and calculated LV mass (154 ± 52 g versus 142 ± 52 g, p = 0.004). Intraobserver and interobserver limits of agreement were similar for both methods. Conclusion: MR analysis of LV volumes and mass involving long-axis LV motion is a highly reproducible method, resulting in smaller LV volumes, higher ejection fraction and calculated LV mass.

Tridimensional ultrasonic images analysis for the in service inspection of fast breeder reactors

International Nuclear Information System (INIS)

Dancre, M.

1999-11-01

Tridimensional image analysis provides a set of methods for the intelligent extraction of information in order to visualize, recognize or inspect objects in volumetric images. In this field of research, we are interested in algorithmic and methodological aspects to extract surface visual information embedded in volume ultrasonic images. The aim is to help a non-acoustician operator, possibly the system itself, to inspect surfaces of vessel and internals in Fast Breeder Reactors (FBR). Those surfaces are immersed in liquid metal, what justifies the ultrasonic technology choice. We expose firstly a state of the art on the visualization of volume ultrasonic images, the methods of noise analysis, the geometrical modelling for surface analysis and finally curves and surfaces matching. These four points are then inserted in a global analysis strategy that relies on an acoustical analysis (echoes recognition), an object analysis (object recognition and reconstruction) and a surface analysis (surface defects detection). Few literature can be found on ultrasonic echoes recognition through image analysis. We suggest an original method that can be generalized to all images with structured and non-structured noise. From a technical point of view, this methodology applied to echoes recognition turns out to be a cooperative approach between morphological mathematics and snakes (active contours). An entropy maximization technique is required for volumetric data binarization. (author)

Automated image analysis in the study of collagenous colitis

DEFF Research Database (Denmark)

Fiehn, Anne-Marie Kanstrup; Kristensson, Martin; Engel, Ulla

2016-01-01

PURPOSE: The aim of this study was to develop an automated image analysis software to measure the thickness of the subepithelial collagenous band in colon biopsies with collagenous colitis (CC) and incomplete CC (CCi). The software measures the thickness of the collagenous band on microscopic...... slides stained with Van Gieson (VG). PATIENTS AND METHODS: A training set consisting of ten biopsies diagnosed as CC, CCi, and normal colon mucosa was used to develop the automated image analysis (VG app) to match the assessment by a pathologist. The study set consisted of biopsies from 75 patients...

Automated rice leaf disease detection using color image analysis

Science.gov (United States)

Pugoy, Reinald Adrian D. L.; Mariano, Vladimir Y.

2011-06-01

In rice-related institutions such as the International Rice Research Institute, assessing the health condition of a rice plant through its leaves, which is usually done as a manual eyeball exercise, is important to come up with good nutrient and disease management strategies. In this paper, an automated system that can detect diseases present in a rice leaf using color image analysis is presented. In the system, the outlier region is first obtained from a rice leaf image to be tested using histogram intersection between the test and healthy rice leaf images. Upon obtaining the outlier, it is then subjected to a threshold-based K-means clustering algorithm to group related regions into clusters. Then, these clusters are subjected to further analysis to finally determine the suspected diseases of the rice leaf.

Intramuscular leukemic relapse: clinical signs and imaging findings. A multicentric analysis

Energy Technology Data Exchange (ETDEWEB)

Surov, Alexey [Martin Luther University Halle-Wittenberg, Department of Radiology, Halle (Germany); University of Leipzig, Department of Diagnostic and Interventional Radiology, Leipzig (Germany); Kiratli, Hayyam [Hacettepe University School of Medicine, Department of Ophthalmology, Ankara (Turkey); Im, Soo Ah [Seoul St. Mary' s Hospital, Department of Radiology, Seoul (Korea, Republic of); Manabe, Yasuhiro [National Hospital Organization Okayama Medical Center, Department of Neurology, Okayama (Japan); O' Neill, Alibhe; Shinagare, Atul B. [Brigham and Women' s Hospital, Department of Radiology, Boston, MA (United States); Spielmann, Rolf Peter [Martin Luther University Halle-Wittenberg, Department of Radiology, Halle (Germany)

2014-09-26

Leukemia is a group of malignant diseases involving peripheral blood and bone marrow. Extramedullary tumor manifestation in leukemia can also occur. They more often involve lymph nodes, skin, and bones. Intramuscular leukemic relapse (ILR) is very unusual. The aim of this analysis was to summarize the reported data regarding clinical signs and radiological features of ILR. The PubMed database was searched for publications related to ILR. After an analysis of all identified articles, 20 publications matched the inclusion criteria. The authors of the 20 publications were contacted and provided imaging of their cases for review. The following were recorded: age, gender, primary diagnosis, clinical signs, pattern, localization and size of the intramuscular leukemic relapse. Images of 16 patients were provided [8 computer tomographic (CT) images and 15 magnetic resonance images, MRI]. Furthermore, one patient with ILR was identified in our institutional database. Therefore, images of 17 patients were available for further analysis. Overall, 32 cases with ILR were included in the analysis. In most cases acute myeloid leukemia was diagnosed. Most ILRs were localized in the extremities (44 %) and in the extraocular muscles (44 %). Clinically, ILR manifested as local pain, swelling and muscle weakness. Radiologically, ILR presented most frequently with diffuse muscle infiltration. On postcontrast CT/MRI, most lesions demonstrated homogeneous enhancement. ILRs were hypo-/isointense on T1w and hyperintense on T2w images. ILR manifests commonly as focal pain, swelling and muscle weakness. ILR predominantly involved the extraocular musculature and the extremities. Radiologically, diffuse muscle infiltration was the most common imaging finding. (orig.)

Low-level processing for real-time image analysis

Science.gov (United States)

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.

Image registration based on virtual frame sequence analysis

Energy Technology Data Exchange (ETDEWEB)

Chen, H.; Ng, W.S. [Nanyang Technological University, Computer Integrated Medical Intervention Laboratory, School of Mechanical and Aerospace Engineering, Singapore (Singapore); Shi, D. (Nanyang Technological University, School of Computer Engineering, Singapore, Singpore); Wee, S.B. [Tan Tock Seng Hospital, Department of General Surgery, Singapore (Singapore)

2007-08-15

This paper is to propose a new framework for medical image registration with large nonrigid deformations, which still remains one of the biggest challenges for image fusion and further analysis in many medical applications. Registration problem is formulated as to recover a deformation process with the known initial state and final state. To deal with large nonlinear deformations, virtual frames are proposed to be inserted to model the deformation process. A time parameter is introduced and the deformation between consecutive frames is described with a linear affine transformation. Experiments are conducted with simple geometric deformation as well as complex deformations presented in MRI and ultrasound images. All the deformations are characterized with nonlinearity. The positive results demonstrated the effectiveness of this algorithm. The framework proposed in this paper is feasible to register medical images with large nonlinear deformations and is especially useful for sequential images. (orig.)

Automated Image Analysis of Offshore Infrastructure Marine Biofouling

Directory of Open Access Journals (Sweden)

Kate Gormley

2018-01-01

Full Text Available In the UK, some of the oldest oil and gas installations have been in the water for over 40 years and have considerable colonisation by marine organisms, which may lead to both industry challenges and/or potential biodiversity benefits (e.g., artificial reefs. The project objective was to test the use of an automated image analysis software (CoralNet on images of marine biofouling from offshore platforms on the UK continental shelf, with the aim of (i training the software to identify the main marine biofouling organisms on UK platforms; (ii testing the software performance on 3 platforms under 3 different analysis criteria (methods A–C; (iii calculating the percentage cover of marine biofouling organisms and (iv providing recommendations to industry. Following software training with 857 images, and testing of three platforms, results showed that diversity of the three platforms ranged from low (in the central North Sea to moderate (in the northern North Sea. The two central North Sea platforms were dominated by the plumose anemone Metridium dianthus; and the northern North Sea platform showed less obvious species domination. Three different analysis criteria were created, where the method of selection of points, number of points assessed and confidence level thresholds (CT varied: (method A random selection of 20 points with CT 80%, (method B stratified random of 50 points with CT of 90% and (method C a grid approach of 100 points with CT of 90%. Performed across the three platforms, the results showed that there were no significant differences across the majority of species and comparison pairs. No significant difference (across all species was noted between confirmed annotations methods (A, B and C. It was considered that the software performed well for the classification of the main fouling species in the North Sea. Overall, the study showed that the use of automated image analysis software may enable a more efficient and consistent

Automated MicroSPECT/MicroCT Image Analysis of the Mouse Thyroid Gland.

Science.gov (United States)

Cheng, Peng; Hollingsworth, Brynn; Scarberry, Daniel; Shen, Daniel H; Powell, Kimerly; Smart, Sean C; Beech, John; Sheng, Xiaochao; Kirschner, Lawrence S; Menq, Chia-Hsiang; Jhiang, Sissy M

2017-11-01

The ability of thyroid follicular cells to take up iodine enables the use of radioactive iodine (RAI) for imaging and targeted killing of RAI-avid thyroid cancer following thyroidectomy. To facilitate identifying novel strategies to improve 131 I therapeutic efficacy for patients with RAI refractory disease, it is desired to optimize image acquisition and analysis for preclinical mouse models of thyroid cancer. A customized mouse cradle was designed and used for microSPECT/CT image acquisition at 1 hour (t1) and 24 hours (t24) post injection of 123 I, which mainly reflect RAI influx/efflux equilibrium and RAI retention in the thyroid, respectively. FVB/N mice with normal thyroid glands and TgBRAF V600E mice with thyroid tumors were imaged. In-house CTViewer software was developed to streamline image analysis with new capabilities, along with display of 3D voxel-based 123 I gamma photon intensity in MATLAB. The customized mouse cradle facilitates consistent tissue configuration among image acquisitions such that rigid body registration can be applied to align serial images of the same mouse via the in-house CTViewer software. CTViewer is designed specifically to streamline SPECT/CT image analysis with functions tailored to quantify thyroid radioiodine uptake. Automatic segmentation of thyroid volumes of interest (VOI) from adjacent salivary glands in t1 images is enabled by superimposing the thyroid VOI from the t24 image onto the corresponding aligned t1 image. The extent of heterogeneity in 123 I accumulation within thyroid VOIs can be visualized by 3D display of voxel-based 123 I gamma photon intensity. MicroSPECT/CT image acquisition and analysis for thyroidal RAI uptake is greatly improved by the cradle and the CTViewer software, respectively. Furthermore, the approach of superimposing thyroid VOIs from t24 images to select thyroid VOIs on corresponding aligned t1 images can be applied to studies in which the target tissue has differential radiotracer retention

Image analysis to evaluate the browning degree of banana (Musa spp.) peel.

Science.gov (United States)

Cho, Jeong-Seok; Lee, Hyeon-Jeong; Park, Jung-Hoon; Sung, Jun-Hyung; Choi, Ji-Young; Moon, Kwang-Deog

2016-03-01

Image analysis was applied to examine banana peel browning. The banana samples were divided into 3 treatment groups: no treatment and normal packaging (Cont); CO2 gas exchange packaging (CO); normal packaging with an ethylene generator (ET). We confirmed that the browning of banana peels developed more quickly in the CO group than the other groups based on sensory test and enzyme assay. The G (green) and CIE L(∗), a(∗), and b(∗) values obtained from the image analysis sharply increased or decreased in the CO group. And these colour values showed high correlation coefficients (>0.9) with the sensory test results. CIE L(∗)a(∗)b(∗) values using a colorimeter also showed high correlation coefficients but comparatively lower than those of image analysis. Based on this analysis, browning of the banana occurred more quickly for CO2 gas exchange packaging, and image analysis can be used to evaluate the browning of banana peels. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Science.gov (United States)

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.

Mediman: Object oriented programming approach for medical image analysis

International Nuclear Information System (INIS)

Coppens, A.; Sibomana, M.; Bol, A.; Michel, C.

1993-01-01

Mediman is a new image analysis package which has been developed to analyze quantitatively Positron Emission Tomography (PET) data. It is object-oriented, written in C++ and its user interface is based on InterViews on top of which new classes have been added. Mediman accesses data using external data representation or import/export mechanism which avoids data duplication. Multimodality studies are organized in a simple database which includes images, headers, color tables, lists and objects of interest (OOI's) and history files. Stored color table parameters allow to focus directly on the interesting portion of the dynamic range. Lists allow to organize the study according to modality, acquisition protocol, time and spatial properties. OOI's (points, lines and regions) are stored in absolute 3-D coordinates allowing correlation with other co-registered imaging modalities such as MRI or SPECT. OOI's have visualization properties and are organized into groups. Quantitative ROI analysis of anatomic images consists of position, distance, volume calculation on selected OOI's. An image calculator is connected to mediman. Quantitation of metabolic images is performed via profiles, sectorization, time activity curves and kinetic modeling. Mediman is menu and mouse driven, macro-commands can be registered and replayed. Its interface is customizable through a configuration file. The benefit of the object-oriented approach are discussed from a development point of view

Texture analysis of computed tomography images of acute ischemic stroke patients

International Nuclear Information System (INIS)

Oliveira, M.S.; Castellano, G.; Fernandes, P.T.; Avelar, W.M.; Santos, S.L.M.; Li, L.M.

2009-01-01

Computed tomography (CT) images are routinely used to assess ischemic brain stroke in the acute phase. They can provide important clues about whether to treat the patient by thrombolysis with tissue plasminogen activator. However, in the acute phase, the lesions may be difficult to detect in the images using standard visual analysis. The objective of the present study was to determine if texture analysis techniques applied to CT images of stroke patients could differentiate between normal tissue and affected areas that usually go unperceived under visual analysis. We performed a pilot study in which texture analysis, based on the gray level co-occurrence matrix, was applied to the CT brain images of 5 patients and of 5 control subjects and the results were compared by discriminant analysis. Thirteen regions of interest, regarding areas that may be potentially affected by ischemic stroke, were selected for calculation of texture parameters. All regions of interest for all subjects were classified as lesional or non-lesional tissue by an expert neuroradiologist. Visual assessment of the discriminant analysis graphs showed differences in the values of texture parameters between patients and controls, and also between texture parameters for lesional and non-lesional tissue of the patients. This suggests that texture analysis can indeed be a useful tool to help neurologists in the early assessment of ischemic stroke and quantification of the extent of the affected areas. (author)

Application of forensic image analysis in accident investigations.

Science.gov (United States)

Verolme, Ellen; Mieremet, Arjan

2017-09-01

Forensic investigations are primarily meant to obtain objective answers that can be used for criminal prosecution. Accident analyses are usually performed to learn from incidents and to prevent similar events from occurring in the future. Although the primary goal may be different, the steps in which information is gathered, interpreted and weighed are similar in both types of investigations, implying that forensic techniques can be of use in accident investigations as well. The use in accident investigations usually means that more information can be obtained from the available information than when used in criminal investigations, since the latter require a higher evidence level. In this paper, we demonstrate the applicability of forensic techniques for accident investigations by presenting a number of cases from one specific field of expertise: image analysis. With the rapid spread of digital devices and new media, a wealth of image material and other digital information has become available for accident investigators. We show that much information can be distilled from footage by using forensic image analysis techniques. These applications show that image analysis provides information that is crucial for obtaining the sequence of events and the two- and three-dimensional geometry of an accident. Since accident investigation focuses primarily on learning from accidents and prevention of future accidents, and less on the blame that is crucial for criminal investigations, the field of application of these forensic tools may be broader than would be the case in purely legal sense. This is an important notion for future accident investigations. Copyright © 2017 Elsevier B.V. All rights reserved.

Positron emission tomography: Physics, instrumentation, and image analysis

International Nuclear Information System (INIS)

Porenta, G.

1994-01-01

Positron emission tomography (PET) is a noninvasive diagnostic technique that permits reconstruction of cross-sectional images of the human body which depict the biodistribution of PET tracer substances. A large variety of physiological PET tracers, mostly based on isotopes of carbon, nitrogen, oxygen, and fluorine is available and allows the in vivo investigation of organ perfusion, metabolic pathways and biomolecular processes in normal and diseased states. PET cameras utilize the physical characteristics of positron decay to derive quantitative measurements of tracer concentrations, a capability that has so far been elusive for conventional SPECT (single photon emission computed tomography) imaging techniques. Due to the short half lives of most PET isotopes, an on-site cyclotron and a radiochemistry unit are necessary to provide an adequate supply of PET tracers. While operating a PET center in the past was a complex procedure restricted to few academic centers with ample resources. PET technology has rapidly advanced in recent years and has entered the commercial nuclear medicine market. To date, the availability of compact cyclotrons with remote computer control, automated synthesis units for PET radiochemistry, high-performance PET cameras, and userfriendly analysis workstations permits installation of a clinical PET center within most nuclear medicine facilities. This review provides simple descriptions of important aspects concerning physics, instrumentation, and image analysis in PET imaging which should be understood by medical personnel involved in the clinical operation of a PET imaging center. (author)

Radiotherapy for carcinoma of the vagina. Immunocytochemical and cytofluorometric analysis of prognostic factors

Energy Technology Data Exchange (ETDEWEB)

Blecharz, P. [Maria Sklodowska-Curie Memorial Institute, Krakow (Poland). Dept. of Gynecological Oncology; Reinfuss, M.; Jakubowicz, J. [Maria Sklodowska-Curie Memorial Institute, Krakow (Poland). Dept. of Radiation Oncology; Rys, J. [Maria Sklodowska-Curie Memorial Institute, Krakow (Poland). Dept. of Tumor Pathology Oncology; Skotnicki, P.; Wysocki, W. [Maria Sklodowska-Curie Memorial Institute, Krakow (Poland). Dept. of Oncological Surgery

2013-05-15

Background and purpose: The aim of this study was to assess the potential prognostic factors in patients with primary invasive vaginal carcinoma (PIVC) treated with radical irradiation. Patients and methods: The analysis was performed on 77 patients with PIVC treated between 1985 and 2005 in the Maria Sklodowska-Curie Memorial Institute of Oncology, Cancer Center in Krakow. A total of 36 patients (46.8 %) survived 5 years with no evidence of disease (NED). The following groups of factors were assessed for potential prognostic value: population-based (age), clinical (Karnofsky Performance Score [KPS], hemoglobin level, primary location of the vaginal lesion, macroscopic type, length of the involved vaginal wall, FIGO stage), microscopic (microscopic type, grade, mitotic index, presence of atypical mitoses, lymphatic vessels invasion, lymphocytes/plasmocytes infiltration, focal necrosis, VAIN-3), immunohistochemical (protein p53 expression, MIB-1 index), cytofluorometric (ploidity, index DI, S-phase fraction, proliferation index SG2M) factors. Results: Significantly better 5-year NED was observed in patients: < 60 years, KPS {<=} 80, FIGO stage I and II, grade G1-2, MIB-1 index < 70, S-phase fraction < 10, and proliferation index < 25. Independent factors for better prognosis in the multivariate Cox analysis were age < 60 years, FIGO stage I or II, and MIB-1 index < 70. Conclusion: Independent prognostic factors in the radically irradiated PIVC patients were as follows: age, FIGO stage, MIB-1 index. (orig.)

Phase Image Analysis in Conduction Disturbance Patients

Energy Technology Data Exchange (ETDEWEB)

Kwark, Byeng Su; Choi, Si Wan; Kang, Seung Sik; Park, Ki Nam; Lee, Kang Wook; Jeon, Eun Seok; Park, Chong Hun [Chung Nam University Hospital, Daejeon (Korea, Republic of)

1994-03-15

It is known that the normal His-Purkinje system provides for nearly synchronous activation of right (RV) and left (LV) ventricles. When His-Purkinje conduction is abnormal, the resulting sequence of ventricular contraction must be correspondingly abnormal. These abnormal mechanical consequences were difficult to demonstrate because of the complexity and the rapidity of its events. To determine the relationship of the phase changes and the abnormalities of ventricular conduction, we performed phase image analysis of Tc-RBC gated blood pool scintigrams in patients with intraventricular conduction disturbances (24 complete left bundle branch block (C-LBBB), 15 complete right bundle branch block (C-RBBB), 13 Wolff-Parkinson-White syndrome (WPW), 10 controls). The results were as follows; 1) The ejection fraction (EF), peak ejection rate (PER), and peak filling rate (PFR) of LV in gated blood pool scintigraphy (GBPS) were significantly lower in patients with C-LBBB than in controls (44.4 +- 13.9% vs 69.9 +- 4.2%, 2.48 +- 0.98 vs 3.51 +- 0,62, 1.76 +- 0.71 vs 3.38 +- 0.92, respectively, p<0.05). 2) In the phase angle analysis of LV, Standard deviation (SD), width of half maximum of phase angle (FWHM), and range of phase angle were significantly increased in patients with C-LBBB than in controls (20.6 + 18.1 vs S.6 + I.8, 22. 5 + 9.2 vs 16.0 + 3.9, 95.7 + 31.7 vs 51.3 + 5.4, respectively, p<0.05). 3) There was no significant difference in EF, PER, PFR between patients with the WolffParkinson-White syndrome and controls. 4) Standard deviation and range of phase angle were significantly higher in patients with WPW syndrome than in controls (10.6 + 2.6 vs 8.6 + 1.8, p<0.05, 69.8 + 11.7 vs 51.3 + 5 4, p<0.001, respectively), however, there was no difference between the two groups in full width of half maximum. 5) Phase image analysis revealed relatively uniform phase across the both ventriles in patients with normal conduction, but markedly delayed phase in the left ventricle

3-D Image Analysis of Fluorescent Drug Binding

Directory of Open Access Journals (Sweden)

M. Raquel Miquel

2005-01-01

Full Text Available Fluorescent ligands provide the means of studying receptors in whole tissues using confocal laser scanning microscopy and have advantages over antibody- or non-fluorescence-based method. Confocal microscopy provides large volumes of images to be measured. Histogram analysis of 3-D image volumes is proposed as a method of graphically displaying large amounts of volumetric image data to be quickly analyzed and compared. The fluorescent ligand BODIPY FL-prazosin (QAPB was used in mouse aorta. Histogram analysis reports the amount of ligand-receptor binding under different conditions and the technique is sensitive enough to detect changes in receptor availability after antagonist incubation or genetic manipulations. QAPB binding was concentration dependent, causing concentration-related rightward shifts in the histogram. In the presence of 10 μM phenoxybenzamine (blocking agent, the QAPB (50 nM histogram overlaps the autofluorescence curve. The histogram obtained for the 1D knockout aorta lay to the left of that of control and 1B knockout aorta, indicating a reduction in 1D receptors. We have shown, for the first time, that it is possible to graphically display binding of a fluorescent drug to a biological tissue. Although our application is specific to adrenergic receptors, the general method could be applied to any volumetric, fluorescence-image-based assay.

Automatic analysis of the micronucleus test in primary human lymphocytes using image analysis.

Science.gov (United States)

Frieauff, W; Martus, H J; Suter, W; Elhajouji, A

2013-01-01

The in vitro micronucleus test (MNT) is a well-established test for early screening of new chemical entities in industrial toxicology. For assessing the clastogenic or aneugenic potential of a test compound, micronucleus induction in cells has been shown repeatedly to be a sensitive and a specific parameter. Various automated systems to replace the tedious and time-consuming visual slide analysis procedure as well as flow cytometric approaches have been discussed. The ROBIAS (Robotic Image Analysis System) for both automatic cytotoxicity assessment and micronucleus detection in human lymphocytes was developed at Novartis where the assay has been used to validate positive results obtained in the MNT in TK6 cells, which serves as the primary screening system for genotoxicity profiling in early drug development. In addition, the in vitro MNT has become an accepted alternative to support clinical studies and will be used for regulatory purposes as well. The comparison of visual with automatic analysis results showed a high degree of concordance for 25 independent experiments conducted for the profiling of 12 compounds. For concentration series of cyclophosphamide and carbendazim, a very good correlation between automatic and visual analysis by two examiners could be established, both for the relative division index used as cytotoxicity parameter, as well as for micronuclei scoring in mono- and binucleated cells. Generally, false-positive micronucleus decisions could be controlled by fast and simple relocation of the automatically detected patterns. The possibility to analyse 24 slides within 65h by automatic analysis over the weekend and the high reproducibility of the results make automatic image processing a powerful tool for the micronucleus analysis in primary human lymphocytes. The automated slide analysis for the MNT in human lymphocytes complements the portfolio of image analysis applications on ROBIAS which is supporting various assays at Novartis.

Architectural design and analysis of a programmable image processor

International Nuclear Information System (INIS)

Siyal, M.Y.; Chowdhry, B.S.; Rajput, A.Q.K.

2003-01-01

In this paper we present an architectural design and analysis of a programmable image processor, nicknamed Snake. The processor was designed with a high degree of parallelism to speed up a range of image processing operations. Data parallelism found in array processors has been included into the architecture of the proposed processor. The implementation of commonly used image processing algorithms and their performance evaluation are also discussed. The performance of Snake is also compared with other types of processor architectures. (author)

Image seedling analysis to evaluate tomato seed physiological potential

Directory of Open Access Journals (Sweden)

Vanessa Neumann Silva

Full Text Available Computerized seedling image analysis are one of the most recently techniques to detect differences of vigor between seed lots. The aim of this study was verify the hability of computerized seedling image analysis by SVIS® to detect differences of vigor between tomato seed lots as information provided by traditionally vigor tests. Ten lots of tomato seeds, cultivar Santa Clara, were stored for 12 months in controlled environment at 20 ± 1 ºC and 45-50% of relative humidity of the air. The moisture content of the seeds was monitored and the physiological potential tested at 0, 6 and 12 months after storage, with germination test, first count of germination, traditional accelerated ageing and with saturated salt solution, electrical conductivity, seedling emergence and with seed vigor imaging system (SVIS®. A completely randomized experimental design was used with four replications. The parameters obtained by the computerized seedling analysis (seedling length and indexes of vigor and seedling growth with software SVIS® are efficient to detect differences between tomato seed lots of high and low vigor.

Semi-automated analysis of three-dimensional track images

International Nuclear Information System (INIS)

Meesen, G.; Poffijn, A.

2001-01-01

In the past, three-dimensional (3-d) track images in solid state detectors were difficult to obtain. With the introduction of the confocal scanning laser microscope it is now possible to record 3-d track images in a non-destructive way. These 3-d track images can latter be used to measure typical track parameters. Preparing the detectors and recording the 3-d images however is only the first step. The second step in this process is enhancing the image quality by means of deconvolution techniques to obtain the maximum possible resolution. The third step is extracting the typical track parameters. This can be done on-screen by an experienced operator. For large sets of data however, this manual technique is not desirable. This paper will present some techniques to analyse 3-d track data in an automated way by means of image analysis routines. Advanced thresholding techniques guarantee stable results in different recording situations. By using pre-knowledge about the track shape, reliable object identification is obtained. In case of ambiguity, manual intervention is possible

Analysis and improvement of the quantum image matching

Science.gov (United States)

Dang, Yijie; Jiang, Nan; Hu, Hao; Zhang, Wenyin

2017-11-01

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543-3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.

Evaluation of Yogurt Microstructure Using Confocal Laser Scanning Microscopy and Image Analysis

DEFF Research Database (Denmark)

Skytte, Jacob Lercke; Ghita, Ovidiu; Whelan, Paul F.

2015-01-01

The microstructure of protein networks in yogurts defines important physical properties of the yogurt and hereby partly its quality. Imaging this protein network using confocal scanning laser microscopy (CSLM) has shown good results, and CSLM has become a standard measuring technique for fermented...... to image texture description. Here, CSLM images from a yogurt fermentation study are investigated, where production factors including fat content, protein content, heat treatment, and incubation temperature are varied. The descriptors are evaluated through nearest neighbor classification, variance analysis...... scanning microscopy images can be used to provide information on the protein microstructure in yogurt products. For large numbers of microscopy images, subjective evaluation becomes a difficult or even impossible approach, if the images should be incorporated in any form of statistical analysis alongside...

Material Science Image Analysis using Quant-CT in ImageJ

Energy Technology Data Exchange (ETDEWEB)

Ushizima, Daniela M.; Bianchi, Andrea G. C.; DeBianchi, Christina; Bethel, E. Wes

2015-01-05

We introduce a computational analysis workflow to access properties of solid objects using nondestructive imaging techniques that rely on X-ray imaging. The goal is to process and quantify structures from material science sample cross sections. The algorithms can differentiate the porous media (high density material) from the void (background, low density media) using a Boolean classifier, so that we can extract features, such as volume, surface area, granularity spectrum, porosity, among others. Our workflow, Quant-CT, leverages several algorithms from ImageJ, such as statistical region merging and 3D object counter. It also includes schemes for bilateral filtering that use a 3D kernel, for parallel processing of sub-stacks, and for handling over-segmentation using histogram similarities. The Quant-CT supports fast user interaction, providing the ability for the user to train the algorithm via subsamples to feed its core algorithms with automated parameterization. Quant-CT plugin is currently available for testing by personnel at the Advanced Light Source and Earth Sciences Divisions and Energy Frontier Research Center (EFRC), LBNL, as part of their research on porous materials. The goal is to understand the processes in fluid-rock systems for the geologic sequestration of CO2, and to develop technology for the safe storage of CO2 in deep subsurface rock formations. We describe our implementation, and demonstrate our plugin on porous material images. This paper targets end-users, with relevant information for developers to extend its current capabilities.

Second order statistical analysis of US image texture

International Nuclear Information System (INIS)

Tanzi, F.; Novario, R.

1999-01-01

The study reports the sonographic image texture of the neonatal heart in different stages of development by calculating numerical parameters extracted from the gray scale co-occurrence matrix. To show pixel values differences and enhance texture structure, images were equalized and then the gray level range was reduced to 16 to allow sufficiently high occupancy frequency of the co-occurrence matrix. Differences are so little significant that they may be due to different factors affecting image texture and the variability introduced by manual ROI positioning; therefore no definitive conclusions can be drawn as to considering this kind of analysis capable of discriminating different stages of myocardial development [it

WE-G-207-05: Relationship Between CT Image Quality, Segmentation Performance, and Quantitative Image Feature Analysis

Energy Technology Data Exchange (ETDEWEB)

Lee, J; Nishikawa, R [University of Pittsburgh, Pittsburgh, PA (United States); Reiser, I [The University of Chicago, Chicago, IL (United States); Boone, J [UC Davis Medical Center, Sacramento, CA (United States)

2015-06-15

Purpose: Segmentation quality can affect quantitative image feature analysis. The objective of this study is to examine the relationship between computed tomography (CT) image quality, segmentation performance, and quantitative image feature analysis. Methods: A total of 90 pathology proven breast lesions in 87 dedicated breast CT images were considered. An iterative image reconstruction (IIR) algorithm was used to obtain CT images with different quality. With different combinations of 4 variables in the algorithm, this study obtained a total of 28 different qualities of CT images. Two imaging tasks/objectives were considered: 1) segmentation and 2) classification of the lesion as benign or malignant. Twenty-three image features were extracted after segmentation using a semi-automated algorithm and 5 of them were selected via a feature selection technique. Logistic regression was trained and tested using leave-one-out-cross-validation and its area under the ROC curve (AUC) was recorded. The standard deviation of a homogeneous portion and the gradient of a parenchymal portion of an example breast were used as an estimate of image noise and sharpness. The DICE coefficient was computed using a radiologist’s drawing on the lesion. Mean DICE and AUC were used as performance metrics for each of the 28 reconstructions. The relationship between segmentation and classification performance under different reconstructions were compared. Distributions (median, 95% confidence interval) of DICE and AUC for each reconstruction were also compared. Results: Moderate correlation (Pearson’s rho = 0.43, p-value = 0.02) between DICE and AUC values was found. However, the variation between DICE and AUC values for each reconstruction increased as the image sharpness increased. There was a combination of IIR parameters that resulted in the best segmentation with the worst classification performance. Conclusion: There are certain images that yield better segmentation or classification

Digital image analysis in breast pathology-from image processing techniques to artificial intelligence.

Science.gov (United States)

Robertson, Stephanie; Azizpour, Hossein; Smith, Kevin; Hartman, Johan

2018-04-01

Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis. Copyright © 2017 Elsevier Inc. All rights reserved.

A software platform for the analysis of dermatology images

Science.gov (United States)

Vlassi, Maria; Mavraganis, Vlasios; Asvestas, Panteleimon

2017-11-01

The purpose of this paper is to present a software platform developed in Python programming environment that can be used for the processing and analysis of dermatology images. The platform provides the capability for reading a file that contains a dermatology image. The platform supports image formats such as Windows bitmaps, JPEG, JPEG2000, portable network graphics, TIFF. Furthermore, it provides suitable tools for selecting, either manually or automatically, a region of interest (ROI) on the image. The automated selection of a ROI includes filtering for smoothing the image and thresholding. The proposed software platform has a friendly and clear graphical user interface and could be a useful second-opinion tool to a dermatologist. Furthermore, it could be used to classify images including from other anatomical parts such as breast or lung, after proper re-training of the classification algorithms.

New approaches in intelligent image analysis techniques, methodologies and applications

CERN Document Server

Nakamatsu, Kazumi

2016-01-01

This book presents an Introduction and 11 independent chapters, which are devoted to various new approaches of intelligent image processing and analysis. The book also presents new methods, algorithms and applied systems for intelligent image processing, on the following basic topics: Methods for Hierarchical Image Decomposition; Intelligent Digital Signal Processing and Feature Extraction; Data Clustering and Visualization via Echo State Networks; Clustering of Natural Images in Automatic Image Annotation Systems; Control System for Remote Sensing Image Processing; Tissue Segmentation of MR Brain Images Sequence; Kidney Cysts Segmentation in CT Images; Audio Visual Attention Models in Mobile Robots Navigation; Local Adaptive Image Processing; Learning Techniques for Intelligent Access Control; Resolution Improvement in Acoustic Maps. Each chapter is self-contained with its own references. Some of the chapters are devoted to the theoretical aspects while the others are presenting the practical aspects and the...

Image segmentation and particles classification using texture analysis method

Directory of Open Access Journals (Sweden)

Mayar Aly Atteya

Full Text Available Introduction: Ingredients of oily fish include a large amount of polyunsaturated fatty acids, which are important elements in various metabolic processes of humans, and have also been used to prevent diseases. However, in an attempt to reduce cost, recent developments are starting a replace the ingredients of fish oil with products of microalgae, that also produce polyunsaturated fatty acids. To do so, it is important to closely monitor morphological changes in algae cells and monitor their age in order to achieve the best results. This paper aims to describe an advanced vision-based system to automatically detect, classify, and track the organic cells using a recently developed SOPAT-System (Smart On-line Particle Analysis Technology, a photo-optical image acquisition device combined with innovative image analysis software. Methods The proposed method includes image de-noising, binarization and Enhancement, as well as object recognition, localization and classification based on the analysis of particles’ size and texture. Results The methods allowed for correctly computing cell’s size for each particle separately. By computing an area histogram for the input images (1h, 18h, and 42h, the variation could be observed showing a clear increase in cell. Conclusion The proposed method allows for algae particles to be correctly identified with accuracies up to 99% and classified correctly with accuracies up to 100%.

Identification of Fusarium damaged wheat kernels using image analysis

Directory of Open Access Journals (Sweden)

Ondřej Jirsa

2011-01-01

Full Text Available Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue, very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance. The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

Applications of wavelets in morphometric analysis of medical images

Science.gov (United States)

Davatzikos, Christos; Tao, Xiaodong; Shen, Dinggang

2003-11-01

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Positioning of Nuclear Fuel Assemblies by Means of Image Analysis on Tomographic Data

International Nuclear Information System (INIS)

Troeng, Mats

2005-06-01

A tomographic measurement technique for nuclear fuel assemblies has been developed at the Department of Radiation Sciences at Uppsala University. The technique requires highly accurate information about the position of the measured nuclear fuel assembly relative to the measurement equipment. In experimental campaigns performed earlier, separate positioning measurements have therefore been performed in connection to the tomographic measurements. In this work, another positioning approach has been investigated, which requires only the collection of tomographic data. Here, a simplified tomographic reconstruction is performed, whereby an image is obtained. By performing image analysis on this image, the lateral and angular position of the fuel assembly can be determined. The position information can then be used to perform a more accurate tomographic reconstruction involving detailed physical modeling. Two image analysis techniques have been developed in this work. The stability of the two techniques with respect to some central parameters has been studied. The agreement between these image analysis techniques and the previously used positioning technique was found to meet the desired requirements. Furthermore, it has been shown that the image analysis techniques offer more detailed information than the previous technique. In addition, its off-line analysis properties reduce the need for valuable measurement time. When utilizing the positions obtained from the image analysis techniques in tomographic reconstructions of the rod-by-rod power distribution, the repeatability of the reconstructed values was improved. Furthermore, the reconstructions resulted in better agreement to theoretical data

Preliminaries on core image analysis using fault drilling samples; Core image kaiseki kotohajime (danso kussaku core kaisekirei)

Energy Technology Data Exchange (ETDEWEB)

Miyazaki, T; Ito, H [Geological Survey of Japan, Tsukuba (Japan)

1996-05-01

This paper introduces examples of image data analysis on fault drilling samples. The paper describes the following matters: core samples used in the analysis are those obtained from wells drilled piercing the Nojima fault which has moved in the Hygoken-Nanbu Earthquake; the CORESCAN system made by DMT Corporation, Germany, used in acquiring the image data consists of a CCD camera, a light source and core rotation mechanism, and a personal computer, its resolution being about 5 pixels/mm in both axial and circumferential directions, and 24-bit full color; with respect to the opening fractures in core samples collected by using a constant azimuth coring, it was possible to derive values of the opening width, inclination angle, and travel from the image data by using a commercially available software for the personal computer; and comparison of this core image with the BHTV record and the hydrophone VSP record (travel and inclination obtained from the BHTV record agree well with those obtained from the core image). 4 refs., 4 figs.

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

Science.gov (United States)

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.

Development of automatic image analysis methods for high-throughput and high-content screening

NARCIS (Netherlands)

Di, Zi

2013-01-01

This thesis focuses on the development of image analysis methods for ultra-high content analysis of high-throughput screens where cellular phenotype responses to various genetic or chemical perturbations that are under investigation. Our primary goal is to deliver efficient and robust image analysis

Multispectral Image Analysis for Astaxanthin Coating Classification

DEFF Research Database (Denmark)

Ljungqvist, Martin Georg; Ersbøll, Bjarne Kjær; Nielsen, Michael Engelbrecht

2012-01-01

Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. The pellets were divided into two groups: one with pellets coated using synthetic astaxanthin in fish oil and the other with pellets coated...

Automatic analysis of quality of images from X-ray digital flat detectors

International Nuclear Information System (INIS)

Le Meur, Y.

2009-04-01

Since last decade, medical imaging has grown up with the development of new digital imaging techniques. In the field of X-ray radiography, new detectors replace progressively older techniques, based on film or x-ray intensifiers. These digital detectors offer a higher sensibility and reduced overall dimensions. This work has been prepared with Trixell, the world leading company in flat detectors for medical radiography. It deals with quality control on digital images stemming from these detectors. High quality standards of medical imaging impose a close analysis of the defects that can appear on the images. This work describes a complete process for quality analysis of such images. A particular focus is given on the detection task of the defects, thanks to methods well adapted to our context of spatially correlated defects in noise background. (author)

Semiautomated analysis of embryoscope images: Using localized variance of image intensity to detect embryo developmental stages.

Science.gov (United States)

Mölder, Anna; Drury, Sarah; Costen, Nicholas; Hartshorne, Geraldine M; Czanner, Silvester

2015-02-01

Embryo selection in in vitro fertilization (IVF) treatment has traditionally been done manually using microscopy at intermittent time points during embryo development. Novel technique has made it possible to monitor embryos using time lapse for long periods of time and together with the reduced cost of data storage, this has opened the door to long-term time-lapse monitoring, and large amounts of image material is now routinely gathered. However, the analysis is still to a large extent performed manually, and images are mostly used as qualitative reference. To make full use of the increased amount of microscopic image material, (semi)automated computer-aided tools are needed. An additional benefit of automation is the establishment of standardization tools for embryo selection and transfer, making decisions more transparent and less subjective. Another is the possibility to gather and analyze data in a high-throughput manner, gathering data from multiple clinics and increasing our knowledge of early human embryo development. In this study, the extraction of data to automatically select and track spatio-temporal events and features from sets of embryo images has been achieved using localized variance based on the distribution of image grey scale levels. A retrospective cohort study was performed using time-lapse imaging data derived from 39 human embryos from seven couples, covering the time from fertilization up to 6.3 days. The profile of localized variance has been used to characterize syngamy, mitotic division and stages of cleavage, compaction, and blastocoel formation. Prior to analysis, focal plane and embryo location were automatically detected, limiting precomputational user interaction to a calibration step and usable for automatic detection of region of interest (ROI) regardless of the method of analysis. The results were validated against the opinion of clinical experts. © 2015 International Society for Advancement of Cytometry. © 2015 International

A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis

Science.gov (United States)

Aiello, Martina; Gianinetto, Marco

2017-10-01

Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2 values obtained for both optical and radar images.

Principal component analysis of image gradient orientations for face recognition

NARCIS (Netherlands)

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data

Does thorax EIT image analysis depend on the image reconstruction method?

Science.gov (United States)

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2013-04-01

Different methods were proposed to analyze the resulting images of electrical impedance tomography (EIT) measurements during ventilation. The aim of our study was to examine if the analysis methods based on back-projection deliver the same results when applied on images based on other reconstruction algorithms. Seven mechanically ventilated patients with ARDS were examined by EIT. The thorax contours were determined from the routine CT images. EIT raw data was reconstructed offline with (1) filtered back-projection with circular forward model (BPC); (2) GREIT reconstruction method with circular forward model (GREITC) and (3) GREIT with individual thorax geometry (GREITT). Three parameters were calculated on the resulting images: linearity, global ventilation distribution and regional ventilation distribution. The results of linearity test are 5.03±2.45, 4.66±2.25 and 5.32±2.30 for BPC, GREITC and GREITT, respectively (median ±interquartile range). The differences among the three methods are not significant (p = 0.93, Kruskal-Wallis test). The proportions of ventilation in the right lung are 0.58±0.17, 0.59±0.20 and 0.59±0.25 for BPC, GREITC and GREITT, respectively (p = 0.98). The differences of the GI index based on different reconstruction methods (0.53±0.16, 0.51±0.25 and 0.54±0.16 for BPC, GREITC and GREITT, respectively) are also not significant (p = 0.93). We conclude that the parameters developed for images generated with GREITT are comparable with filtered back-projection and GREITC.

Analysis of renal nuclear medicine images

International Nuclear Information System (INIS)

Jose, R.M.J.

2000-01-01

Nuclear medicine imaging of the renal system involves producing time-sequential images showing the distribution of a radiopharmaceutical in the renal system. Producing numerical and graphical data from nuclear medicine studies requires defining regions of interest (ROIs) around various organs within the field of view, such as the left kidney, right kidney and bladder. Automating this process has several advantages: a saving of a clinician's time; enhanced objectivity and reproducibility. This thesis describes the design, implementation and assessment of an automatic ROI generation system. The performance of the system described in this work is assessed by comparing the results to those obtained using manual techniques. Since nuclear medicine images are inherently noisy, the sequence of images is reconstructed using the first few components of a principal components analysis in order to reduce the noise in the images. An image of the summed reconstructed sequence is then formed. This summed image is segmented by using an edge co-occurrence matrix as a feature space for simultaneously classifying regions and locating boundaries. Two methods for assigning the regions of a segmented image to organ class labels are assessed. The first method is based on using Dempster-Shafer theory to combine uncertain evidence from several sources into a single evidence; the second method makes use of a neural network classifier. The use of each technique in classifying the regions of a segmented image are assessed in separate experiments using 40 real patient-studies. A comparative assessment of the two techniques shows that the neural network produces more accurate region labels for the kidneys. The optimum neural system is determined experimentally. Results indicate that combining temporal and spatial information with a priori clinical knowledge produces reasonable ROIs. Consistency in the neural network assignment of regions is enhanced by taking account of the contextual

Discrimination of bromodeoxyuridine labelled and unlabelled mitotic cells in flow cytometric bromodeoxyuridine/DNA analysis

DEFF Research Database (Denmark)

Jensen, P O; Larsen, J K; Christensen, I J

1994-01-01

Bromodeoxyuridine (BrdUrd) labelled and unlabelled mitotic cells, respectively, can be discriminated from interphase cells using a new method, based on immunocytochemical staining of BrdUrd and flow cytometric four-parameter analysis of DNA content, BrdUrd incorporation, and forward and orthogonal...... light scatter. The method was optimized using the human leukemia cell lines HL-60 and K-562. Samples of 10(5) ethanol-fixed cells were treated with pepsin/HCl and stained as a nuclear suspension with anti-BrdUrd antibody, FITC-conjugated secondary antibody, and propidium iodide. Labelled mitoses could...

Semiautomatic digital imaging system for cytogenetic analysis

International Nuclear Information System (INIS)

Chaubey, R.C.; Chauhan, P.C.; Bannur, S.V.; Kulgod, S.V.; Chadda, V.K.; Nigam, R.K.

1999-08-01

The paper describes a digital image processing system, developed indigenously at BARC for size measurement of microscopic biological objects such as cell, nucleus and micronucleus in mouse bone marrow; cytochalasin-B blocked human lymphocytes in-vitro; numerical counting and karyotyping of metaphase chromosomes of human lymphocytes. Errors in karyotyping of chromosomes by the imaging system may creep in due to lack of well-defined position of centromere or extensive bending of chromosomes, which may result due to poor quality of preparation. Good metaphase preparations are mandatory for precise and accurate analysis by the system. Additional new morphological parameters about each chromosome have to be incorporated to improve the accuracy of karyotyping. Though the experienced cytogenetisist is the final judge; however, the system assists him/her to carryout analysis much faster as compared to manual scoring. Further, experimental studies are in progress to validate different software packages developed for various cytogenetic applications. (author)

Automatic analysis of image quality control for Image Guided Radiation Therapy (IGRT) devices in external radiotherapy

International Nuclear Information System (INIS)

Torfeh, Tarraf

2009-01-01

On-board imagers mounted on a radiotherapy treatment machine are very effective devices that improve the geometric accuracy of radiation delivery. However, a precise and regular quality control program is required in order to achieve this objective. Our purpose consisted of developing software tools dedicated to an automatic image quality control of IGRT devices used in external radiotherapy: 2D-MV mode for measuring patient position during the treatment using high energy images, 2D-kV mode (low energy images) and 3D Cone Beam Computed Tomography (CBCT) MV or kV mode, used for patient positioning before treatment. Automated analysis of the Winston and Lutz test was also proposed. This test is used for the evaluation of the mechanical aspects of treatment machines on which additional constraints are carried out due to the on-board imagers additional weights. Finally, a technique of generating digital phantoms in order to assess the performance of the proposed software tools is described. Software tools dedicated to an automatic quality control of IGRT devices allow reducing by a factor of 100 the time spent by the medical physics team to analyze the results of controls while improving their accuracy by using objective and reproducible analysis and offering traceability through generating automatic monitoring reports and statistical studies. (author) [fr

Mapping Fire Severity Using Imaging Spectroscopy and Kernel Based Image Analysis

Science.gov (United States)

Prasad, S.; Cui, M.; Zhang, Y.; Veraverbeke, S.

2014-12-01

Improved spatial representation of within-burn heterogeneity after wildfires is paramount to effective land management decisions and more accurate fire emissions estimates. In this work, we demonstrate feasibility and efficacy of airborne imaging spectroscopy (hyperspectral imagery) for quantifying wildfire burn severity, using kernel based image analysis techniques. Two different airborne hyperspectral datasets, acquired over the 2011 Canyon and 2013 Rim fire in California using the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) sensor, were used in this study. The Rim Fire, covering parts of the Yosemite National Park started on August 17, 2013, and was the third largest fire in California's history. Canyon Fire occurred in the Tehachapi mountains, and started on September 4, 2011. In addition to post-fire data for both fires, half of the Rim fire was also covered with pre-fire images. Fire severity was measured in the field using Geo Composite Burn Index (GeoCBI). The field data was utilized to train and validate our models, wherein the trained models, in conjunction with imaging spectroscopy data were used for GeoCBI estimation wide geographical regions. This work presents an approach for using remotely sensed imagery combined with GeoCBI field data to map fire scars based on a non-linear (kernel based) epsilon-Support Vector Regression (e-SVR), which was used to learn the relationship between spectra and GeoCBI in a kernel-induced feature space. Classification of healthy vegetation versus fire-affected areas based on morphological multi-attribute profiles was also studied. The availability of pre- and post-fire imaging spectroscopy data over the Rim Fire provided a unique opportunity to evaluate the performance of bi-temporal imaging spectroscopy for assessing post-fire effects. This type of data is currently constrained because of limited airborne acquisitions before a fire, but will become widespread with future spaceborne sensors such as those on

Two-dimensional DFA scaling analysis applied to encrypted images

Science.gov (United States)

Vargas-Olmos, C.; Murguía, J. S.; Ramírez-Torres, M. T.; Mejía Carlos, M.; Rosu, H. C.; González-Aguilar, H.

2015-01-01

The technique of detrended fluctuation analysis (DFA) has been widely used to unveil scaling properties of many different signals. In this paper, we determine scaling properties in the encrypted images by means of a two-dimensional DFA approach. To carry out the image encryption, we use an enhanced cryptosystem based on a rule-90 cellular automaton and we compare the results obtained with its unmodified version and the encryption system AES. The numerical results show that the encrypted images present a persistent behavior which is close to that of the 1/f-noise. These results point to the possibility that the DFA scaling exponent can be used to measure the quality of the encrypted image content.

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

Science.gov (United States)

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

Micro-computer system for quantitative image analysis of damage microstructure

International Nuclear Information System (INIS)

Kohyama, A.; Kohno, Y.; Satoh, K.; Igata, N.

1984-01-01

Quantitative image analysis of radiation induced damage microstructure is very important in evaluating material behaviors in radiation environment. But, quite a few improvement have been seen in quantitative analysis of damage microstructure in these decades. The objective of this work is to develop new system for quantitative image analysis of damage microstructure which could improve accuracy and efficiency of data sampling and processing and could enable to get new information about mutual relations among dislocations, precipitates, cavities, grain boundaries, etc. In this system, data sampling is done with X-Y digitizer. The cavity microstructure in dual-ion irradiated 316 SS is analyzed and the effectiveness of this system is discussed. (orig.)

A Proposal on the Quantitative Homogeneity Analysis Method of SEM Images for Material Inspections

Energy Technology Data Exchange (ETDEWEB)

Kim, Song Hyun; Kim, Jong Woo; Shin, Chang Ho [Hanyang University, Seoul (Korea, Republic of); Choi, Jung-Hoon; Cho, In-Hak; Park, Hwan Seo [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2015-05-15

A scanning electron microscope (SEM) is a method to inspect the surface microstructure of materials. The SEM uses electron beams for imaging high magnifications of material surfaces; therefore, various chemical analyses can be performed from the SEM images. Therefore, it is widely used for the material inspection, chemical characteristic analysis, and biological analysis. For the nuclear criticality analysis field, it is an important parameter to check the homogeneity of the compound material for using it in the nuclear system. In our previous study, the SEM was tried to use for the homogeneity analysis of the materials. In this study, a quantitative homogeneity analysis method of SEM images is proposed for the material inspections. The method is based on the stochastic analysis method with the information of the grayscales of the SEM images.

A Proposal on the Quantitative Homogeneity Analysis Method of SEM Images for Material Inspections

International Nuclear Information System (INIS)

Kim, Song Hyun; Kim, Jong Woo; Shin, Chang Ho; Choi, Jung-Hoon; Cho, In-Hak; Park, Hwan Seo

2015-01-01

A scanning electron microscope (SEM) is a method to inspect the surface microstructure of materials. The SEM uses electron beams for imaging high magnifications of material surfaces; therefore, various chemical analyses can be performed from the SEM images. Therefore, it is widely used for the material inspection, chemical characteristic analysis, and biological analysis. For the nuclear criticality analysis field, it is an important parameter to check the homogeneity of the compound material for using it in the nuclear system. In our previous study, the SEM was tried to use for the homogeneity analysis of the materials. In this study, a quantitative homogeneity analysis method of SEM images is proposed for the material inspections. The method is based on the stochastic analysis method with the information of the grayscales of the SEM images

Wave-equation Migration Velocity Analysis Using Plane-wave Common Image Gathers

KAUST Repository

Guo, Bowen; Schuster, Gerard T.

2017-01-01

Wave-equation migration velocity analysis (WEMVA) based on subsurface-offset, angle domain or time-lag common image gathers (CIGs) requires significant computational and memory resources because it computes higher dimensional migration images

Quantitative image analysis in sonograms of the thyroid gland

Energy Technology Data Exchange (ETDEWEB)

Catherine, Skouroliakou [A' Department of Radiology, University of Athens, Vas.Sophias Ave, Athens 11528 (Greece); Maria, Lyra [A' Department of Radiology, University of Athens, Vas.Sophias Ave, Athens 11528 (Greece)]. E-mail: mlyra@pindos.uoa.gr; Aristides, Antoniou [A' Department of Radiology, University of Athens, Vas.Sophias Ave, Athens 11528 (Greece); Lambros, Vlahos [A' Department of Radiology, University of Athens, Vas.Sophias Ave, Athens 11528 (Greece)

2006-12-20

High-resolution, real-time ultrasound is a routine examination for assessing the disorders of the thyroid gland. However, the current diagnosis practice is based mainly on qualitative evaluation of the resulting sonograms, therefore depending on the physician's experience. Computerized texture analysis is widely employed in sonographic images of various organs (liver, breast), and it has been proven to increase the sensitivity of diagnosis by providing a better tissue characterization. The present study attempts to characterize thyroid tissue by automatic texture analysis. The texture features that are calculated are based on co-occurrence matrices as they have been proposed by Haralick. The sample consists of 40 patients. For each patient two sonographic images (one for each lobe) are recorded in DICOM format. The lobe is manually delineated in each sonogram, and the co-occurrence matrices for 52 separation vectors are calculated. The texture features extracted from each one of these matrices are: contrast, correlation, energy and homogeneity. Primary component analysis is used to select the optimal set of features. The statistical analysis resulted in the extraction of 21 optimal descriptors. The optimal descriptors are all co-occurrence parameters as the first-order statistics did not prove to be representative of the images characteristics. The bigger number of components depends mainly on correlation for very close or very far distances. The results indicate that quantitative analysis of thyroid sonograms can provide an objective characterization of thyroid tissue.

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

Science.gov (United States)

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

2015-09-01

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

Cascaded image analysis for dynamic crack detection in material testing

Science.gov (United States)

Hampel, U.; Maas, H.-G.

Concrete probes in civil engineering material testing often show fissures or hairline-cracks. These cracks develop dynamically. Starting at a width of a few microns, they usually cannot be detected visually or in an image of a camera imaging the whole probe. Conventional image analysis techniques will detect fissures only if they show a width in the order of one pixel. To be able to detect and measure fissures with a width of a fraction of a pixel at an early stage of their development, a cascaded image analysis approach has been developed, implemented and tested. The basic idea of the approach is to detect discontinuities in dense surface deformation vector fields. These deformation vector fields between consecutive stereo image pairs, which are generated by cross correlation or least squares matching, show a precision in the order of 1/50 pixel. Hairline-cracks can be detected and measured by applying edge detection techniques such as a Sobel operator to the results of the image matching process. Cracks will show up as linear discontinuities in the deformation vector field and can be vectorized by edge chaining. In practical tests of the method, cracks with a width of 1/20 pixel could be detected, and their width could be determined at a precision of 1/50 pixel.

Image analysis for remote examination of fuel pins

International Nuclear Information System (INIS)

Cook, J.H.; Nayak, U.P.

1982-01-01

An image analysis system operating in the Wing 9 Hot Cell Facility at Los Alamos National Laboratory provides quantitative microstructural analyses of irradiated fuels and materials. With this system, fewer photomicrographs are required during postirradiation microstructural examination and data are available for analysis much faster. The system has been used successfully to examine Westinghouse Advanced Reactors Division experimental fuel pins

Issues in Quantitative Analysis of Ultraviolet Imager (UV) Data: Airglow

Science.gov (United States)

Germany, G. A.; Richards, P. G.; Spann, J. F.; Brittnacher, M. J.; Parks, G. K.

1999-01-01

The GGS Ultraviolet Imager (UVI) has proven to be especially valuable in correlative substorm, auroral morphology, and extended statistical studies of the auroral regions. Such studies are based on knowledge of the location, spatial, and temporal behavior of auroral emissions. More quantitative studies, based on absolute radiometric intensities from UVI images, require a more intimate knowledge of the instrument behavior and data processing requirements and are inherently more difficult than studies based on relative knowledge of the oval location. In this study, UVI airglow observations are analyzed and compared with model predictions to illustrate issues that arise in quantitative analysis of UVI images. These issues include instrument calibration, long term changes in sensitivity, and imager flat field response as well as proper background correction. Airglow emissions are chosen for this study because of their relatively straightforward modeling requirements and because of their implications for thermospheric compositional studies. The analysis issues discussed here, however, are identical to those faced in quantitative auroral studies.

A virtual laboratory for medical image analysis

NARCIS (Netherlands)

Olabarriaga, Sílvia D.; Glatard, Tristan; de Boer, Piter T.

2010-01-01

This paper presents the design, implementation, and usage of a virtual laboratory for medical image analysis. It is fully based on the Dutch grid, which is part of the Enabling Grids for E-sciencE (EGEE) production infrastructure and driven by the gLite middleware. The adopted service-oriented

In Vivo Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis

Directory of Open Access Journals (Sweden)

Niall Colgan

2017-11-01

Full Text Available Background: Non-invasive characterization of the pathological features of Alzheimer's disease (AD could enhance patient management and the development of therapeutic strategies. Magnetic resonance imaging texture analysis (MRTA has been used previously to extract texture descriptors from structural clinical scans in AD to determine cerebral tissue heterogeneity. In this study, we examined the potential of MRTA to specifically identify tau pathology in an AD mouse model and compared the MRTA metrics to histological measures of tau burden.Methods: MRTA was applied to T2 weighted high-resolution MR images of nine 8.5-month-old rTg4510 tau pathology (TG mice and 16 litter matched wild-type (WT mice. MRTA comprised of the filtration-histogram technique, where the filtration step extracted and enhanced features of different sizes (fine, medium, and coarse texture scales, followed by quantification of texture using histogram analysis (mean gray level intensity, mean intensity, entropy, uniformity, skewness, standard-deviation, and kurtosis. MRTA was applied to manually segmented regions of interest (ROI drawn within the cortex, hippocampus, and thalamus regions and the level of tau burden was assessed in equivalent regions using histology.Results: Texture parameters were markedly different between WT and TG in the cortex (E, p < 0.01, K, p < 0.01, the hippocampus (K, p < 0.05 and in the thalamus (K, p < 0.01. In addition, we observed significant correlations between histological measurements of tau burden and kurtosis in the cortex, hippocampus and thalamus.Conclusions: MRTA successfully differentiated WT and TG in brain regions with varying degrees of tau pathology (cortex, hippocampus, and thalamus based on T2 weighted MR images. Furthermore, the kurtosis measurement correlated with histological measures of tau burden. This initial study indicates that MRTA may have a role in the early diagnosis of AD and the assessment of tau pathology using

Phase Image Analysis in Conduction Disturbance Patients

International Nuclear Information System (INIS)

Kwark, Byeng Su; Choi, Si Wan; Kang, Seung Sik; Park, Ki Nam; Lee, Kang Wook; Jeon, Eun Seok; Park, Chong Hun

1994-01-01

It is known that the normal His-Purkinje system provides for nearly synchronous activation of right (RV) and left (LV) ventricles. When His-Purkinje conduction is abnormal, the resulting sequence of ventricular contraction must be correspondingly abnormal. These abnormal mechanical consequences were difficult to demonstrate because of the complexity and the rapidity of its events. To determine the relationship of the phase changes and the abnormalities of ventricular conduction, we performed phase image analysis of Tc-RBC gated blood pool scintigrams in patients with intraventricular conduction disturbances (24 complete left bundle branch block (C-LBBB), 15 complete right bundle branch block (C-RBBB), 13 Wolff-Parkinson-White syndrome (WPW), 10 controls). The results were as follows; 1) The ejection fraction (EF), peak ejection rate (PER), and peak filling rate (PFR) of LV in gated blood pool scintigraphy (GBPS) were significantly lower in patients with C-LBBB than in controls (44.4 ± 13.9% vs 69.9 ± 4.2%, 2.48 ± 0.98 vs 3.51 ± 0,62, 1.76 ± 0.71 vs 3.38 ± 0.92, respectively, p<0.05). 2) In the phase angle analysis of LV, Standard deviation (SD), width of half maximum of phase angle (FWHM), and range of phase angle were significantly increased in patients with C-LBBB than in controls (20.6 + 18.1 vs S.6 + I.8, 22. 5 + 9.2 vs 16.0 + 3.9, 95.7 + 31.7 vs 51.3 + 5.4, respectively, p<0.05). 3) There was no significant difference in EF, PER, PFR between patients with the WolffParkinson-White syndrome and controls. 4) Standard deviation and range of phase angle were significantly higher in patients with WPW syndrome than in controls (10.6 + 2.6 vs 8.6 + 1.8, p<0.05, 69.8 + 11.7 vs 51.3 + 5 4, p<0.001, respectively), however, there was no difference between the two groups in full width of half maximum. 5) Phase image analysis revealed relatively uniform phase across the both ventriles in patients with normal conduction, but markedly delayed phase in the left ventricle

Quantifying biodiversity using digital cameras and automated image analysis.

Science.gov (United States)

Roadknight, C. M.; Rose, R. J.; Barber, M. L.; Price, M. C.; Marshall, I. W.

2009-04-01

Monitoring the effects on biodiversity of extensive grazing in complex semi-natural habitats is labour intensive. There are also concerns about the standardization of semi-quantitative data collection. We have chosen to focus initially on automating the most time consuming aspect - the image analysis. The advent of cheaper and more sophisticated digital camera technology has lead to a sudden increase in the number of habitat monitoring images and information that is being collected. We report on the use of automated trail cameras (designed for the game hunting market) to continuously capture images of grazer activity in a variety of habitats at Moor House National Nature Reserve, which is situated in the North of England at an average altitude of over 600m. Rainfall is high, and in most areas the soil consists of deep peat (1m to 3m), populated by a mix of heather, mosses and sedges. The cameras have been continuously in operation over a 6 month period, daylight images are in full colour and night images (IR flash) are black and white. We have developed artificial intelligence based methods to assist in the analysis of the large number of images collected, generating alert states for new or unusual image conditions. This paper describes the data collection techniques, outlines the quantitative and qualitative data collected and proposes online and offline systems that can reduce the manpower overheads and increase focus on important subsets in the collected data. By converting digital image data into statistical composite data it can be handled in a similar way to other biodiversity statistics thus improving the scalability of monitoring experiments. Unsupervised feature detection methods and supervised neural methods were tested and offered solutions to simplifying the process. Accurate (85 to 95%) categorization of faunal content can be obtained, requiring human intervention for only those images containing rare animals or unusual (undecidable) conditions, and

Image-based RSA: Roentgen stereophotogrammetric analysis based on 2D-3D image registration.

Science.gov (United States)

de Bruin, P W; Kaptein, B L; Stoel, B C; Reiber, J H C; Rozing, P M; Valstar, E R

2008-01-01

Image-based Roentgen stereophotogrammetric analysis (IBRSA) integrates 2D-3D image registration and conventional RSA. Instead of radiopaque RSA bone markers, IBRSA uses 3D CT data, from which digitally reconstructed radiographs (DRRs) are generated. Using 2D-3D image registration, the 3D pose of the CT is iteratively adjusted such that the generated DRRs resemble the 2D RSA images as closely as possible, according to an image matching metric. Effectively, by registering all 2D follow-up moments to the same 3D CT, the CT volume functions as common ground. In two experiments, using RSA and using a micromanipulator as gold standard, IBRSA has been validated on cadaveric and sawbone scapula radiographs, and good matching results have been achieved. The accuracy was: |mu |RSA but higher than in vivo standard RSA. Because IBRSA does not require radiopaque markers, it adds functionality to the RSA method by opening new directions and possibilities for research, such as dynamic analyses using fluoroscopy on subjects without markers and computer navigation applications.

Image-Analysis Based on Seed Phenomics in Sesame

Directory of Open Access Journals (Sweden)

Prasad R.

2014-10-01

Full Text Available The seed coat (testa structure of twenty-three cultivated (Sesamum indicum L. and six wild sesame (s. occidentale Regel & Heer., S. mulayanum Nair, S. prostratum Retz., S. radiatum Schumach. & Thonn., S. angustifolium (Oliv. Engl. and S. schinzianum Asch germplasm was analyzed from digital and Scanning Electron Microscopy (SEM images with dedicated software using the descriptors for computer based seed image analysis to understand the diversity of seed morphometric traits, which later on can be extended to screen and evaluate improved genotypes of sesame. Seeds of wild sesame species could conveniently be distinguished from cultivated varieties based on shape and architectural analysis. Results indicated discrete ‘cut off values to identify definite shape and contour of seed for a desirable sesame genotype along with the con-ventional practice of selecting lighter colored testa.

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

Science.gov (United States)

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.

Application of computer intensive data analysis methods to the analysis of digital images and spatial data

DEFF Research Database (Denmark)

Windfeld, Kristian

1992-01-01

Computer-intensive methods for data analysis in a traditional setting has developed rapidly in the last decade. The application of and adaption of some of these methods to the analysis of multivariate digital images and spatial data are explored, evaluated and compared to well established classical...... into the projection pursuit is presented. Examples from remote sensing are given. The ACE algorithm for computing non-linear transformations for maximizing correlation is extended and applied to obtain a non-linear transformation that maximizes autocorrelation or 'signal' in a multivariate image....... This is a generalization of the minimum /maximum autocorrelation factors (MAF's) which is a linear method. The non-linear method is compared to the linear method when analyzing a multivariate TM image from Greenland. The ACE method is shown to give a more detailed decomposition of the image than the MAF-transformation...

MELDOQ - astrophysical image and pattern analysis in medicine: early recognition of malignant melanomas of the skin by digital image analysis. Final report

International Nuclear Information System (INIS)

Bunk, W.; Pompl, R.; Morfill, G.; Stolz, W.; Abmayr, W.

1999-01-01

Dermatoscopy is at present the most powerful clinical method for early detection of malignant melanomas. However, the application requires a lot of expertise and experience. Therefore, a quantitative image analysis system has been developed in order to assist dermatologists in 'on site diagnosis' and to improve the detection efficiency. Based on a very extensive dataset of dermatoscopic images, recorded in a standardized manner, a number of features for quantitative characterization of complex patterns in melanocytic skin lesions has been developed. The derived classifier improved the detection rate of malignant and benign melanocytic lesions to over 90% (sensitivity =91.5% and specificity =93.4% in the test set), using only six measures. A distinguishing feature of the system is the visualization of the quantified characteristics that are based on the dermatoscopic ABCD-rule. The developed prototype of a dermatoscopic workplace consists of defined procedures for standardized image acquisition and documentation, components of a necessary data pre-processing (e.g. shading- and colour-correction, removal of artefacts), quantification algorithms (evaluating asymmetry properties, border characteristics, the content of colours and structural components) and classification routines. In 2000 an industrial partner will begin marketing the digital imaging system including the specialized software for the early detection of skin cancer, which is suitable for clinicians and practitioners. The primary used nonlinear analysis techniques (e.g. scaling index method and others) can identify and characterize complex patterns in images and have a diagnostic potential in many other applications. (orig.) [de

Image acquisition and analysis for beam diagnostics, applications of the Taiwan photon source

International Nuclear Information System (INIS)

Liao, C.Y.; Chen, J.; Cheng, Y.S.; Hsu, K.T.; Hu, K.H.; Kuo, C.H.; Wu, C.Y.

2012-01-01

Design and implementation of image acquisition and analysis is in proceeding for the Taiwan Photon Source (TPS) diagnostic applications. The optical system contains screen, lens, and lighting system. A CCD camera with Gigabit Ethernet interface (GigE Vision) will be a standard image acquisition device. Image acquisition will be done on EPICS IOC via PV channel and analysis the properties by using Matlab tool to evaluate the beam profile (sigma), beam size position and tilt angle et al. The EPICS IOC integrated with Matlab as a data processing system is not only could be used in image analysis but also in many types of equipment data processing applications. Progress of the project will be summarized in this report. (authors)

Tolerance of image enhancement brightness and contrast in lateral cephalometric digital radiography for Steiner analysis

Science.gov (United States)

Rianti, R. A.; Priaminiarti, M.; Syahraini, S. I.

2017-08-01

Image enhancement brightness and contrast can be adjusted on lateral cephalometric digital radiographs to improve image quality and anatomic landmarks for measurement by Steiner analysis. To determine the limit value for adjustments of image enhancement brightness and contrast in lateral cephalometric digital radiography for Steiner analysis. Image enhancement brightness and contrast were adjusted on 100 lateral cephalometric radiography in 10-point increments (-30, -20, -10, 0, +10, +20, +30). Steiner analysis measurements were then performed by two observers. Reliabilities were tested by the Interclass Correlation Coefficient (ICC) and significance tested by ANOVA or the Kruskal Wallis test. No significant differences were detected in lateral cephalometric analysis measurements following adjustment of the image enhancement brightness and contrast. The limit value of adjustments of the image enhancement brightness and contrast associated with incremental 10-point changes (-30, -20, -10, 0, +10, +20, +30) does not affect the results of Steiner analysis.

Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

Science.gov (United States)

El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

2010-08-01

Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

High-speed image analysis reveals chaotic vibratory behaviors of pathological vocal folds

Energy Technology Data Exchange (ETDEWEB)

Zhang Yu, E-mail: yuzhang@xmu.edu.c [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Shao Jun [Shanghai EENT Hospital of Fudan University, Shanghai (China); Krausert, Christopher R. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States); Zhang Sai [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Jiang, Jack J. [Shanghai EENT Hospital of Fudan University, Shanghai (China); Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States)

2011-01-15

Research highlights: Low-dimensional human glottal area data. Evidence of chaos in human laryngeal activity from high-speed digital imaging. Traditional perturbation analysis should be cautiously applied to aperiodic high speed image signals. Nonlinear dynamic analysis may be helpful for understanding disordered behaviors in pathological laryngeal systems. - Abstract: Laryngeal pathology is usually associated with irregular dynamics of laryngeal activity. High-speed imaging facilitates direct observation and measurement of vocal fold vibrations. However, chaotic dynamic characteristics of aperiodic high-speed image data have not yet been investigated in previous studies. In this paper, we will apply nonlinear dynamic analysis and traditional perturbation methods to quantify high-speed image data from normal subjects and patients with various laryngeal pathologies including vocal fold nodules, polyps, bleeding, and polypoid degeneration. The results reveal the low-dimensional dynamic characteristics of human glottal area data. In comparison to periodic glottal area series from a normal subject, aperiodic glottal area series from pathological subjects show complex reconstructed phase space, fractal dimension, and positive Lyapunov exponents. The estimated positive Lyapunov exponents provide the direct evidence of chaos in pathological human vocal folds from high-speed digital imaging. Furthermore, significant differences between the normal and pathological groups are investigated for nonlinear dynamic and perturbation analyses. Jitter in the pathological group is significantly higher than in the normal group, but shimmer does not show such a difference. This finding suggests that the traditional perturbation analysis should be cautiously applied to high speed image signals. However, the correlation dimension and the maximal Lyapunov exponent reveal a statistically significant difference between normal and pathological groups. Nonlinear dynamic analysis is capable of

High-speed image analysis reveals chaotic vibratory behaviors of pathological vocal folds

International Nuclear Information System (INIS)

Zhang Yu; Shao Jun; Krausert, Christopher R.; Zhang Sai; Jiang, Jack J.

2011-01-01

Research highlights: → Low-dimensional human glottal area data. → Evidence of chaos in human laryngeal activity from high-speed digital imaging. → Traditional perturbation analysis should be cautiously applied to aperiodic high speed image signals. → Nonlinear dynamic analysis may be helpful for understanding disordered behaviors in pathological laryngeal systems. - Abstract: Laryngeal pathology is usually associated with irregular dynamics of laryngeal activity. High-speed imaging facilitates direct observation and measurement of vocal fold vibrations. However, chaotic dynamic characteristics of aperiodic high-speed image data have not yet been investigated in previous studies. In this paper, we will apply nonlinear dynamic analysis and traditional perturbation methods to quantify high-speed image data from normal subjects and patients with various laryngeal pathologies including vocal fold nodules, polyps, bleeding, and polypoid degeneration. The results reveal the low-dimensional dynamic characteristics of human glottal area data. In comparison to periodic glottal area series from a normal subject, aperiodic glottal area series from pathological subjects show complex reconstructed phase space, fractal dimension, and positive Lyapunov exponents. The estimated positive Lyapunov exponents provide the direct evidence of chaos in pathological human vocal folds from high-speed digital imaging. Furthermore, significant differences between the normal and pathological groups are investigated for nonlinear dynamic and perturbation analyses. Jitter in the pathological group is significantly higher than in the normal group, but shimmer does not show such a difference. This finding suggests that the traditional perturbation analysis should be cautiously applied to high speed image signals. However, the correlation dimension and the maximal Lyapunov exponent reveal a statistically significant difference between normal and pathological groups. Nonlinear dynamic

Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

Science.gov (United States)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

Analysis of bubbly flow using particle image velocimetry

Energy Technology Data Exchange (ETDEWEB)

Todd, D.R.; Ortiz-Villafuerte, J.; Schmidl, W.D.; Hassan, Y.A. [Texas A and M University, Nuclear Engineering Dept., College Stagion, TX (United States); Sanchez-Silva, F. [ESIME, INP (Mexico)

2001-07-01

The local phasic velocities can be determined in two-phase flows if the phases can be separated during analysis. The continuous liquid velocity field can be captured using standard Particle Image Velocimetry (PIV) techniques in two-phase flows. PIV is now a well-established, standard flow measurement technique, which provides instantaneous velocity fields in a two-dimensional plane of finite thickness. PIV can be extended to three dimensions within the plane with special considerations. A three-dimensional shadow PIV (SPIV) measurement apparatus can be used to capture the dispersed phase flow parameters such as velocity and interfacial area. The SPIV images contain only the bubble images, and can be easily analyzed and the results used to separate the dispersed phase from the continuous phase in PIV data. An experimental system that combines the traditional PIV technique with SPIV will be described and sample data will be analyzed to demonstrate an advanced turbulence measurement method in a two-phase bubbly flow system. Also, a qualitative error analysis method that allows users to reduce the number of erroneous vectors obtained from the PIV measurements will be discussed. (authors)

Analysis of bubbly flow using particle image velocimetry

International Nuclear Information System (INIS)

Todd, D.R.; Ortiz-Villafuerte, J.; Schmidl, W.D.; Hassan, Y.A.; Sanchez-Silva, F.

2001-01-01

The local phasic velocities can be determined in two-phase flows if the phases can be separated during analysis. The continuous liquid velocity field can be captured using standard Particle Image Velocimetry (PIV) techniques in two-phase flows. PIV is now a well-established, standard flow measurement technique, which provides instantaneous velocity fields in a two-dimensional plane of finite thickness. PIV can be extended to three dimensions within the plane with special considerations. A three-dimensional shadow PIV (SPIV) measurement apparatus can be used to capture the dispersed phase flow parameters such as velocity and interfacial area. The SPIV images contain only the bubble images, and can be easily analyzed and the results used to separate the dispersed phase from the continuous phase in PIV data. An experimental system that combines the traditional PIV technique with SPIV will be described and sample data will be analyzed to demonstrate an advanced turbulence measurement method in a two-phase bubbly flow system. Also, a qualitative error analysis method that allows users to reduce the number of erroneous vectors obtained from the PIV measurements will be discussed. (authors)

Image-analysis techniques for investigation localized corrosion processes