Handbook of biomedical image analysis

著者

    • Suri, Jasjit S.
    • Wilson, David L. (David Lynn)
    • Laxminarayan, Swamy

書誌事項

Handbook of biomedical image analysis

edited by Jasjit S. Suri, David L. Wilson , Swamy Laxminarayan

（Topics in biomedical engineering international book series）

Kluwer Academic/Plenum Publishers, c2005

  • set
  • v. 1
  • eISBN : v. 1
  • v. 2
  • eISBN : v. 2
  • v. 3
  • eISBN : v. 3

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注記

Includes bibliographical references

v. 1, Segmentation models pt. A

v. 2, Segmentation models pt. B

v. 3. Registration models

内容説明・目次

巻冊次

v. 1 ISBN 9780306485503

内容説明

Handbook of Biomedical Image Analysis: Segmentation Models (Volume I) is dedicated to the segmentation of complex shapes from the field of imaging sciences using different mathematical techniques. This volume is aimed at researchers and educators in imaging sciences, radiological imaging, clinical and diagnostic imaging, physicists covering different medical imaging modalities, as well as researchers in biomedical engineering, applied mathematics, algorithmic development, computer vision, signal processing, computer graphics and multimedia in general, both in academia and industry . Key Features: - Principles of intra-vascular ultrasound (IVUS) - Principles of positron emission tomography (PET) - Physical principles of magnetic resonance angiography (MRA). - Basic and advanced level set methods - Shape for shading method for medical image analysis - Wavelet transforms and other multi-scale analysis functions - Three dimensional deformable surfaces - Level Set application for CT lungs, brain MRI and MRA volume segmentation - Segmentation of incomplete tomographic medical data sets - Subjective level sets for missing boundaries for segmentation

目次

A Basic Model for IVUS Image Simulation.- Quantitative Functional Imaging with Positron Emission Tomography: Principles and Instrumentation.- Advances in Magnetic Resonance Angiography and Physical Principles.- Recent Advances in the Level Set Method.- Shape From Shading Models.- Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration.- Improving the Initialization, Convergence, and Memory Utilization for Deformable Models.- Level Set Segmentation of Biological Volume Datasets.- Advanced Segmentation Techniques.- A Region-Aided Color Geometric Snake.- Co-Volume Level Set Method in Subjective Surface Based Medical Image Segmentation.
巻冊次

eISBN : v. 1 ISBN 9780306485510

内容説明

Handbook of Biomedical Image Analysis: Segmentation Models (Volume I) is dedicated to the segmentation of complex shapes from the field of imaging sciences using different mathematical techniques. This volume is aimed at researchers and educators in imaging sciences, radiological imaging, clinical and diagnostic imaging, physicists covering different medical imaging modalities, as well as researchers in biomedical engineering, applied mathematics, algorithmic development, computer vision, signal processing, computer graphics and multimedia in general, both in academia and industry. The key features are: principles of intra-vascular ultrasound (IVUS); principles of positron emission tomography (PET); physical principles of magnetic resonance angiography (MRA); basic and advanced level set methods; shape for shading method for medical image analysis; wavelet transforms and other multi-scale analysis functions; three dimensional deformable surfaces; level Set application for CT lungs, brain MRI and MRA volume segmentation; segmentation of incomplete tomographic medical data sets; and, subjective level sets for missing boundaries for segmentation.
巻冊次

v. 2 ISBN 9780306486050

内容説明

With rapid advancements in technology, body imaging or components thereof, have become ubiquitous in medicine. While the biomedical devices such as the MRI, CT, X-rays, Ultrasound, PET/SPECT and Microscopy etc, provide us with high resolution images, the challenges that have continued to confront us with, lie in the interpretation of the vast amounts of data generated by these devices. Biomedical applications are the 'bottom-line' essentials in the diagnostic world. It is this diagnostic interpretation feature that forms the core niche for these books and will serve the needs of a broad spectrum of audience including researchers, research clinicians, and students. Together the three volumes will illustrate the role of the fusion of registration and segmentation systems for complete biomedical applications therapy delivery benefiting the biomedical doctors, clinical researchers, radiologists and others.

目次

Model-Based Brain Tissue Classification.- Supervised Texture Classification for Intravascular Tissue Characterization.- Medical Image Segmentation: Methods and Applications in Functional Imaging.- Automatic Segmentation of Pancreatic Tumors in Computed Tomography.- Computerized Analysis and Vasodilation Parameterization in Flow-Mediated Dilation Tests from Ultrasonic Image Sequences.- Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images.- Automatic Analysis of Color Fundus Photographs and Its Application to the Diagnosis of Diabetic Retinopathy.- Segmentation Issues in Carotid Artery Atherosclerotic Plaque Analysis with MRI.- Accurate Lumen Identification, Detection, and Quantification in MR Plaque Volumes.- Hessian-Based Multiscale Enhancement, Description, and Quantification of Second-Order 3-D Local Structures from Medical Volume Data.- A Knowledge-Based Scheme for Digital Mammography.- Simultaneous Fuzzy Segmentation of Medical Images.- Computer-Aided Diagnosis of Mammographic Calcification Clusters: Impact of Segmentation.- Computer-Supported Segmentation of Radiological Data.
巻冊次

eISBN : v. 2 ISBN 9780306486067

内容説明

With rapid advancements in technology, body imaging or components thereof, have become ubiquitous in medicine. While the biomedical devices such as the MRI, CT, X-rays, Ultrasound, PET/SPECT and Microscopy etc, provide us with high resolution images, the challenges that have continued to confront us with, lie in the interpretation of the vast amounts of data generated by these devices. Biomedical applications are the OCybottom-lineOCO essentials in the diagnostic world. It is this diagnostic interpretation feature that forms the core niche for these books and will serve the needs of a broad spectrum of audience including researchers, research clinicians, and students. Together the three volumes will illustrate the role of the fusion of registration and segmentation systems for complete biomedical applications therapy delivery benefiting the biomedical doctors, clinical researchers, radiologists and others."
巻冊次

v. 3 ISBN 9780306486074

内容説明

Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid registration algorithm using B-spline transformations. We present and evaluate different methods to solve the cor- spondence problem in atlas based registration. An image can be segmented by registering it to an individual atlas, an average atlas, or multiple atlases. When registering to multiple atlases, combining the individual segmentations into a ?nalsegmentationcanbeachievedbyatlasselection,ormulticlassi?erdecision fusion. Wedescribeallthesemethodsandevaluatethesegmentationaccuracies that they achieve by performing experiments with electronic phantoms as well as by comparing their outputs to a manual gold standard. The present work is focused on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and strain measurement. The approach combines well-established prin- ples of nonlinear continuum mechanics with forces derived directly from thr- dimensional image data to achieve registration. The general approach does not require the de?nition of landmarks, ?ducials, or surfaces, although it can - commodate these if available. Representative problems demonstrate the robust and ?exible nature of the approach. Three-dimensional registration methods are introduced for registering MRI volumes of the pelvis and prostate. The chapter ?rst reviews the applications, xi xii Preface challenges,and previous methods of image registration in the prostate.

目次

Medical Image Registration: Theory, Algorithm, and Case Studies in Surgical Simulation, Chest Cancer, and Multiple Sclerosis.- State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities.- Three-Dimensional Rigid and Non-Rigid Image Registration for the Pelvis and Prostate.- Stereo and Temporal Retinal Image Registration by Mutual Information Maximization.- Quantification of Brain Aneurysm Dimensions from CTA for Surgical Planning of Coiling Interventions.- Inverse Consistent Image Registration.- A Computer-Aided Design System for Segmentation of Volumetric Images.- Inter-Subject Non-Rigid Registration: An Overview with Classification and the Romeo Algorithm.- Elastic Registration for Biomedical Applications.- Cross-Entropy, Reversed Cross-Entropy, and Symmetric Divergence Similarity Measures for 3D Image Registration: A Comparative Study.- Quo Vadis, Atlas-Based Segmentation?.- Deformable Image Registration with Hyperelastic Warping.- Future of Image Registration.
巻冊次

eISBN : v. 3 ISBN 9780306486081

内容説明

With rapid advancements in technology, body imaging or components thereof, have become ubiquitous in medicine. While the biomedical devices such as the MRI, CT, X-rays, Ultrasound, PET/SPECT and Microscopy etc, provide us with high-resolution images, the challenges that have continued to confront us with, lie in the interpretation of the vast amounts of data generated by these devices. Biomedical applications are the 'bottom-line' essentials in the diagnostic world. It is this diagnostic interpretation feature that forms the core niche for these books and will serve the needs of a broad spectrum of audience including researchers, research clinicians, and students. Together the three volumes will illustrate the role of the fusion of registration and segmentation systems for complete biomedical applications therapy delivery benefiting the biomedical doctors, clinical researchers, radiologists and others.
巻冊次

set ISBN 9780387231266

内容説明

With rapid advancements in technology, body imaging or components thereof, have become ubiquitous in medicine. While the biomedical devices such as the MRI, CT, X-rays, Ultrasound, PET/SPECT and Microscopy etc, provide us with high resolution images, the challenges that have continued to confront us with, lie in the interpretation of the vast amounts of data generated by these devices. Biomedical applications are the ‘bottom-line’ essentials in the diagnostic world. It is this diagnostic interpretation feature that forms the core niche for these books and will serve the needs of a broad spectrum of audience including researchers, research clinicians, and students.  Together these three volumes of the Handbook of Biomedical Image Analysis, Volume 1 - Segementation Part A; Volume 2-Segmentation Part B; and Volume 3 - Registration, illustrate the role of the fusion of registration and segmentation systems for complete biomedical applications therapy delivery benefiting the biomedical doctors, clinical researchers, radiologists and others. Each volume in this set features a CD-ROM containing pedagogical material and numerous color illustrations.  

目次

Volume 1: A Basic Model for IVUS Image Simulation.- Quantitative Functional Imaging with Positron Emission Tomography.- Advances in Magnetic Resonance Angiography and Physical Principles.- Recent Advances in the Level Set Method - Shape from Shading Models.- Wavelets in Medical Image Processing - Improving the Initialization, Convergence, and Memory Utilization for Defomable Models.- Level Set Segmentation of Biological Volume Database.- Advanced Segmentation (Level Set) Techniques.- A Regional-aided Color Geometric Snake.- Co-Volume Level Set Method in Subjective Surface Based Medical Image Segmentation. Volume 2: Model-Based Brain Tissue Classification.- Supervised Texture Classification for Intravascular Tissue Characterization.- Medical Image Segmentation: Methods and Applications in Functional Imaging.- Automatic Segmentation of Pancreatic Tumors in Computed Tomography.- Computerized Analysis and Vasodilation Parameterization in Flow-Mediated Dilation Tests from Ultrasonic Image Sequences.- Statistical and Adaptive Approaches for Optimal Segmentation in Medical Images.- Automatic Analysis of Color Fundus Photographs and its Application to the Diagnosis of Diabetic Retinopathy.- Segmentation Issues in Carotid Artery Atherosclerotic Plague Analysis with MRI.- Accurate Lumen Identification, Detection, and Quantification in MR Plague Volumes.- Hessian-based Multiscale Enhancement, Description, and Quantification of Second-Order 3D Local Structures from Medical Volume Data.- A Knowledge-Based Scheme for Digital Mammography - Simultaneous Fuzzy Segmentation of Medical Images.- Computer Aided Diagnosis of Mammographic Calcification Clusters: Impact of Segmentation.- Computer Supported Segmentation of Radiological Data. Volume 3: Medical Image Registration: Theory Algorithms, and Case Studies.- State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities.- Three-Dimensional Rigid and Non-Rigid Image Restriction for the Pelvis and Prostate.- Stereo and Temporal Retinal Image Registration by Mutual Information Maximization.- Quantification of Brain Aneurysm Dimensions from CTA for Surgical Planning of Coiling Intervention.- Inverse Consistent Image Registration.- A Computer-Aided Design System for Segmentation of Volumetric Images.- Inter-subject Non-Rigid Registration: an Overview with Classification and the Romeo Algorithm.- Elastic Registration for Biomedical Applications.- Cross-entropy, reversed cross-entropy, and symmetric divergence similarity measures for 3D image registration: a comparative study.- Quo Vadis, Atlas-Based Segmentation?

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