Mode of access: World Wide Web.

System requirements: Adobe Acrobat reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 101-106).

Introduction -- Parametric active contours -- Overview -- What is a parametric active contour -- Active contour evolution -- Algorithm KWT -- Snake external forces -- Gradient vector flow -- Vector field convolution active contours -- Inverse problem approach to active contour initialization -- Feature-weighted snakes -- Area-weighted snakes -- Correlation-weighted snakes -- Snakes with special parameterization -- Spline snakes: open, closed, and clamped B-splines -- Gradient descent method for spline snake computation -- Segmenting leukocytes via spline snakes -- Rigid contour snakes -- Active contours in a Bayesian framework -- Overview -- Bayesian framework for active contour -- An introduction to the Bayesian framework -- A case study: mouse heart segmentation -- Active models with shape priors -- Active shape models -- Training of ASMs -- Generalized snakes (Gsnakes) -- Training of Gsnakes -- Segmentation using Gsnakes -- Geometric active contours -- Overview -- Level sets and geometric active contours -- Binary flow -- Area-weighted binary flow -- Active contours without edges -- Cell detection using a variational approach -- Segmentation with graph algorithms -- Overview -- Shortest path snakes -- Binary labeling with graph cut -- Binary labeling with minimum cut -- Pixel labeling with normalized cut -- Scale-space image filtering for segmentation -- Overview -- Scale space -- Anisotropic diffusion -- Speckle reducing anisotropic diffusion -- Locally monotonic diffusion -- Locally monotonic segmentation -- Morphological local monotonicity -- Inclusion filters -- Acknowledgments -- References -- Author biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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 targets. 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. Additionally, 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.

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Title from PDF t.p. (viewed on March 9, 2009).