Unsupervised hierarchical image segmentation with level set and additive operator splitting

Authors:
M. Jeon;M. Alexander;W. Pedrycz;N. Pizzi
Affiliations:
Institute for Biodiagnostics, National Research Council, 435 Ellice Avenue, Winnipeg, MB, Canada R3B 1Y6;Institute for Biodiagnostics, National Research Council, 435 Ellice Avenue, Winnipeg, MB, Canada R3B 1Y6;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada;Institute for Biodiagnostics, National Research Council, 435 Ellice Avenue, Winnipeg, MB, Canada R3B 1Y6
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

This paper presents an unsupervised hierarchical segmentation method for multi-phase images based on a single level set (2-phase) method and the semi-implicit additive operator splitting (AOS) scheme which is stable, fast, and easy to implement. The method successively segments image subregions found at each step of the hierarchy using a decision criterion based on the variance of intensity across the current subregion. The segmentation continues until a specified number of levels has been reached. The segmentation information for sub-images at each stage is stored in a tree data structure, and is used for reconstructing the segmented images. The method avoids the complicated governing equations of the multi-phase segmentation approach, and appears to converge in fewer iterations. The method can easily be parallelized because the AOS scheme decomposes the equations into a sequence of one dimensional systems.