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Proceedings Paper

A new general method of 3D model generation for active shape image segmentation
Author(s): Seong-Jae Lim; Jayaram K. Udupa; Andre Souza; Yong-Yeon Jeong; Yo-Sung Ho; Drew A. Torigian
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Paper Abstract

For 3D model-based approaches, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently remains an open problem. In this paper, we propose a novel, general method for the generation of 3D statistical shape models. Given a set of training 3D shapes, 3D model generation is achieved by 1) building the mean model from the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean model, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. Previous 3D modeling efforts all had severe limitations in terms of the object shape, geometry, and topology. The proposed method is very general without such assumptions and is applicable to any data set.

Paper Details

Date Published: 15 March 2006
PDF: 8 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61444B (15 March 2006); doi: 10.1117/12.653751
Show Author Affiliations
Seong-Jae Lim, Gwangju Institute of Science and Technology (South Korea)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Andre Souza, Univ. of Pennsylvania (United States)
Yong-Yeon Jeong, Chonnam National Univ. Medical School (South Korea)
Yo-Sung Ho, Gwangju Institute of Science and Technology (South Korea)
Drew A. Torigian, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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