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Proceedings Paper

Analysis of the feasibility of using active shape models for segmentation of gray-scale images
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Paper Abstract

Active Shape Models (ASM) have been used extensively to segment images where the objects of interest show little to moderate shape variability across a training set. It is well known that the efficacy of this technique relies heavily on the quality of the training set and the initialization of the mean shape on the target image. However, little has been said about the validity of the assumptions under which the two core components of ASM, i.e. the shape model and the gray level model, are built. We explore these assumptions and test their validity with respect to both shape and gray level models. In this study, we use different training sets of real and synthetic gray scale images and investigate the reasons for their success or failure in the context of shape and gray level modeling. We show that the shape model performance is not affected by small changes in the distribution of the shapes. Furthermore, we show that a reason for segmentation failure is the lack of features in the mean profiles of gray level values that causes localization errors even under ideal conditions.

Paper Details

Date Published: 9 May 2002
PDF: 12 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467101
Show Author Affiliations
Gilberto Zamora, Texas Tech Univ. (United States)
Hamed Sari-Sarraf, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)
L. Rodney Long, NLM/National Institute of Health (United States)


Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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