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Proceedings Paper

Curvature and shape variance based landmark tagging methods for building statistical object models
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Paper Abstract

Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the model, however, we need an annotated training set of shapes wherein corresponding landmarks are identified in every shape. Manual positioning of landmarks is a tedious, time consuming, and error prone task, and almost impossible in the 3D space. In an attempt to overcome some of these drawbacks, we have devised several automatic methods under two approaches: c-scale based and shape variance based. The c-scale based methods use the concept of local curvature to find landmarks on the mean shape of the training set. These landmarks are then propagated to all the shapes of the training set to establish correspondence in a local-to-global manner. The variance-based method is guided by the strategy of equalization of the shape variance contained in the training set for selecting landmarks. The main premise here is that this strategy itself takes care of the correspondence issue and at the same time deploys landmarks very frugally and optimally considering shape variations. The desired landmarks are positioned around each contour so as to equally distribute the total variance existing in the training set in a global-to-local manner. The methods are evaluated on 40 MRI foot data sets and compared in terms of compactness. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced methods of annotation, and the variance equalization method tops the list.

Paper Details

Date Published: 13 March 2009
PDF: 12 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726129 (13 March 2009); doi: 10.1117/12.812452
Show Author Affiliations
Sylvia Rueda, The Univ. of Nottingham (United Kingdom)
Jayaram K. Udupa, Medical Imaging Processing Group, Univ. of Pennsylvania (United States)
Li Bai, The Univ. of Nottingham (United Kingdom)

Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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