Advanced Algorithmic Approaches to Medical Image Segmentation

Name: Advanced Algorithmic Approaches to Medical Image Segmentation
SKU: 2428126
Price: 744.00 NZD
Availability: InStock

State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology

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Hardback

Series:

Advances in Computer Vision and Pattern Recognition

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Description

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.

Release date NZ

December 11th, 2001

Audiences

Postgraduate, Research & Scholarly
Professional & Vocational

Contributors

Edited by S.Kamaledin Setarehdan
Edited by Sameer Singh

Illustrations

XXVII, 636 p.

Pages

636

Series

Advances in Computer Vision and Pattern Recognition

Dimensions

155x235x38

ISBN-13

9781852333898

Product ID

2428126

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Advanced Algorithmic Approaches to Medical Image Segmentation

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