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An unsupervised strategy for biomedical image segmentation

Authors Odriguez R, Hernández R

Published 13 September 2010 Volume 2010:3 Pages 67—73

DOI https://doi.org/10.2147/AABC.S11918

Review by Single-blind

Peer reviewer comments 3

Roberto Rodríguez1, Rubén Hernández2
1Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, Mexico

Abstract: Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.

Keywords: segmentation, mean shift, unsupervised segmentation, entropy

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