Shape modeling via local curvature scale

  • Authors:

  • Sylvia Rueda;Jayaram K. Udupa;Li Bai

  • Affiliations:

  • Collaborative Medical Image Analysis and Grid, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, United Kingdom;Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Fourth Floor, Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA;Collaborative Medical Image Analysis and Grid, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, United Kingdom

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2010

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Abstract

Segmentation and modeling of organs using model-based approaches require a priori information which is often given by manually tagging landmarks on a training set of shapes. This is a tedious, time-consuming, and error prone task. To overcome some of these drawbacks, focusing on 2D shapes, we devised an automatic method based on the notion of curvature scale - a new local scale concept. This shape descriptor is used to automatically locate mathematical landmarks on the mean of the shapes in the training set, which are then propagated to the training shapes. Altogether 12 different strategies are described and are evaluated in different combinations in terms of compactness on two data sets - 40 CT images of the liver and 40 MR images of the talus bone of the foot. The results show that, for the same number of landmarks, the proposed methods are more compact than manual and equally spaced annotations.