Biomedical Image Analysis: Statistical and Variational Methods

by Aly A. Farag

Overview

Ideal for classroom use and self-study, this book explains the implementation of the most effective modern methods in image analysis, covering segmentation, registration and visualisation, and focusing on the key theories, algorithms and applications that have emerged from recent progress in computer vision, imaging and computational biomedical science. Structured around five core building blocks - signals, systems, image formation and modality; stochastic models; computational geometry; level set methods; and tools and CAD models - it provides a solid overview of the field. Mathematical and statistical topics are presented in a straightforward manner, enabling the reader to gain a deep understanding of the subject without becoming entangled in mathematical complexities. Theory is connected to practical examples in x-ray, ultrasound, nuclear medicine, MRI and CT imaging, removing the abstract nature of the models and assisting reader understanding.

Product Details

ISBN-13:	9780521196796
Publisher:	Cambridge University Press
Publication date:	10/30/2014
Edition description:	New Edition
Pages:	496
Product dimensions:	7.01(w) x 9.92(h) x 1.06(d)

About the Author

Aly A. Farag is a Professor of Electrical and Computer Engineering, and founding Director of the Computer Vision and Image Processing Laboratory, at the University of Louisville. His research interests centre around object modelling with biomedical applications, and his more recent biomedical inventions have led to the development of improved methods for tubular object modelling, virtual colonoscopies and lung nodule detection and classification based on CT scans, real-time monitoring of vital signs from thermal imaging, and image-based reconstruction of the human jaw.

Table of Contents

1. Overview of biomedical image analysis; Part I. Signals and Systems, Image Formation, and Image Modality: 2. Overview of two-dimensional signals and systems; 3. Biomedical imaging modalities; Part II. Stochastic Models: 4. Random variables; 5. Random processes; 6. Basics of random fields; 7. Probability density estimation by linear models; Part III. Computational Geometry: 8. Basics of topology and computational geometry; 9. Geometric features extraction; Part IV. Variational Calculus and Level Set Methods: 10. Variational approaches and level sets; Part V. Image Analysis Tools: 11. Segmentation – statistical approach; 12. Segmentation – variational approach; 13. Basics of registration; 14. Variational methods for shape registrations; 15. Statistical models of shape and appearance.