Stochastic Modeling for Medical Image Analysis: 1st Edition (Hardback) book cover

Stochastic Modeling for Medical Image Analysis

1st Edition

By Ayman El-Baz, Georgy Gimel’farb, Jasjit S. Suri

CRC Press

284 pages | 188 Color Illus.

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pub: 2015-11-19
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Stochastic Modeling for Medical Image Analysis provides a brief introduction to medical imaging, stochastic modeling, and model-guided image analysis.

Today, image-guided computer-assisted diagnostics (CAD) faces two basic challenging problems. The first is the computationally feasible and accurate modeling of images from different modalities to obtain clinically useful information. The second is the accurate and fast inferring of meaningful and clinically valid CAD decisions and/or predictions on the basis of model-guided image analysis.

To help address this, this book details original stochastic appearance and shape models with computationally feasible and efficient learning techniques for improving the performance of object detection, segmentation, alignment, and analysis in a number of important CAD applications.

The book demonstrates accurate descriptions of visual appearances and shapes of the goal objects and their background to help solve a number of important and challenging CAD problems. The models focus on the first-order marginals of pixel/voxel-wise signals and second- or higher-order Markov-Gibbs random fields of these signals and/or labels of regions supporting the goal objects in the lattice.

This valuable resource presents the latest state of the art in stochastic modeling for medical image analysis while incorporating fully tested experimental results throughout.

Table of Contents

Medical Imaging Modalities

Magnetic Resonance Imaging

Computed Tomography

Ultrasound Imaging

Nuclear Medical Imaging (Nuclide Imaging)

Bibliographic and Historical Notes

From Images to Graphical Models

Basics of Image Modeling

Pixel/Voxel Interactions and Neighborhoods

Exponential Families of Probability Distributions

Appearance and Shape Modeling

Bibliographic and Historical Notes

IRF Models: Estimating Marginals

Basic Independent Random Fields

Supervised and Unsupervised Learning

Expectation-Maximization to Identify Mixtures

Gaussian Linear Combinations versus Mixtures

Bibliographic and Historical Notes

Markov-Gibbs Random Field Models: Estimating Signal Interactions

Generic Kth-Order MGRFs

Common Second- and Higher-Order MGRFs

Learning Second-Order Interaction Structures

Bibliographic and Historical Notes

Applications: Image Alignment

General Image Alignment Frameworks

Global Alignment by Learning an Appearance Prior

Bibliographic and Historical Notes

Segmenting Multimodal Images

Joint MGRF of Images and Region Maps

Experimental Validation

Bibliographic and Historical Notes

Performance Evaluation and Validation

Segmenting with Deformable Models

Appearance-Based Segmentation

Shape and Appearance-Based Segmentation

Bibliographic and Historical Notes

Segmenting with Shape and Appearance Priors

Learning a Shape Prior

Evolving a Deformable Boundary

Experimental Validation

Bibliographic and Historical Notes

Cine Cardiac MRI Analysis

Segmenting Myocardial Borders

Wall Thickness Analysis

Experimental Results

Bibliographic and Historical Notes

Sizing Cardiac Pathologies

LV Wall Segmentation

Identifying the Pathological Tissue

Quantifying the Myocardial Viability

Performance Evaluation and Validation

Bibliographic and Historical Notes

About the Authors

Ayman El-Baz, PhD, associate professor, Department of Bioengineering, University of Louisville, Kentucky, USA

Georgy Gimel’farb, professor of computer science, University of Auckland, New Zealand

Jasjit S. Suri, PhD, MBA, CEO, Global Biomedical Technologies, Inc., Roseville, California, USA

Subject Categories

BISAC Subject Codes/Headings:
MEDICAL / Biotechnology
SCIENCE / Physics