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Other titles in the Advances in Pattern Recognition series:
Advanced Algorithmic Approaches to Medical Image Segmentation (Advances in Pattern Recognition,)by J. Suri
Synopses & Reviews
Medical imaging is an important topic which is generally recognised as key to better diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound.This book focuses primarily on state-of-the-art model-based segmentation techniques which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors based in both industry and academia and presents a host of new material including algorithms for:- brain segmentation applied to MR;- neuro-application using MR; - parametric and geometric deformable models for brain segmentation;- left ventricle segmentation and analysis using least squares and constrained least squares models for cardiac X-rays; - left ventricle analysis in echocardioangiograms;- breast lesion detection in digital mammograms;detection of cells in cell images.As an overview of the latest techniques, this book will be of particular interest to students and researchers in medical engineering, image processing, computer graphics, mathematical modelling and data analysis. It will also be of interest to researchers in the fields of mammography, cardiology, pathology and neurology.
Medical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.
Table of Contents
Principles of Image Generation.- Segmentation in Echocardiographic Images.- Cardiac Boundary Segmentation.- Brain Segmentation Techniques.- Segmentation for Multiple Sclerosis Lesion.- Finite Mixture Models.- MR Spectroscopy.- Fast WM/GM Boundary Estimation.- Digital Mammography Segmentation.- Cell Image Segmentation for Diagnostic Pathology.- Future in Segmentation.- Bibliography.- Index.
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