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Biomedical Image Analysis:
Segmentation
Copyright © 2009 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in
any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in
printed reviews, without the prior permission of the publisher.
Biomedical Image Analysis: Segmentation
Scott T. Acton and Nilanjan Ray
www.morganclaypool.com
ISBN: 9781598290202
ISBN: 9781598290219
paperback
ebook
DOI: 10.2200/S00133ED1V01Y200807IVM009
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON IMAGE, VIDEO AND MULTIMEDIA PROCESSING #9
Lecture #9
Series Editor: Alan C. Bovik, University of Texas, Austin
Series ISSN
ISSN: 1559-8136 print
ISSN: 1559-8144 electronic
Biomedical Image Analysis:
Segmentation
Scott T. Acton
University of Virginia
Nilanjan Ray
University of Alberta
SYNTHESIS LECTURES ON IMAGE, VIDEO AND MULTIMEDIA
PROCESSING #9
iv
ABSTRACT
The sequel to the popular lecture book entitled
Biomedical Image Analysis: Tracking,
this book on
Biomedical Image Analysis: Segmentation
tackles the challenging task of segmenting biological and
medical images. The problem of partitioning multidimensional biomedical data into meaningful
regions is perhaps the main roadblock in the automation of biomedical image analysis. Whether
the modality of choice is MRI, PET, ultrasound, SPECT, CT, or one of a myriad of microscopy
platforms, image segmentation is a vital step in analyzing the constituent biological or medical tar-
gets. This book provides a state-of-the-art, comprehensive look at biomedical image segmentation
that is accessible to well-equipped undergraduates, graduate students, and research professionals in
the biology, biomedical, medical, and engineering fields. Active model methods that have emerged
in the last few years are a focus of the book, including parametric active contour and active surface
models, active shape models, and geometric active contours that adapt to the image topology. Ad-
ditionally,
Biomedical Image Analysis: Segmentation
details attractive new methods that use graph
theory in segmentation of biomedical imagery. Finally, the use of exciting new scale space tools in
biomedical image analysis is reported.
KeywoRdS
image segmentation, computer vision, image processing, medical imaging, biological
imaging, active contours, graph theory, scale space, level set analysis

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