A Novel Medical Image Segmentation Method using Dynamic Programming

Authors:
Yan Zhang;Bogdan J. Matuszewski;Lik-Kwan Shark
Affiliations:
University of Central Lancashire, Preston, U.K.;University of Central Lancashire, Preston, U.K.;University of Central Lancashire, Preston, U.K.
Venue:
MEDIVIS '07 Proceedings of the International Conference on Medical Information Visualisation - BioMedical Visualisation
Year:
2007

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Abstract

A novel method is proposed to segment objects in medical images whose boundaries can be described as closed curves. Based on an image with the enhanced boundary of an object of interest, the segmentation method consists of three key steps, namely, the polar transformation, dynamic programming and curve fitting. A 3D object in volumetric data can be segmented on a slice-by-slice basis by only specifying one point inside the 3D object of interest as the pole for the polar transformation. The method is also shown to be able to segment objects with very weak boundaries.