Unsupervised multiphase segmentation: a phase balancing model

Authors:
Berta Sandberg;Sung Ha Kang;Tony F. Chan
Affiliations:
Adel Research, Inc., Los Angeles, CA and Department of Mathematics, University of California, Los Angeles;School of Mathematics, Georgia Institute of Technology, Atlanta, GA;Mathematics and Computer Science Departments, Hong Kong University of Science and Technology, Hong Kong and Department of Mathematics, University of California, Los Angeles
Venue:
IEEE Transactions on Image Processing
Year:
2010

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Abstract

Variational models have been studied for image segmentation application since the Mumford-Shah functional was introduced in the late 1980s. In this paper, we focus on multiphase segmentation with a new regularization term that yields an unsupervised segmentation model. We propose a functional that automatically chooses a favorable number of phases as it segments the image. The primary driving force of the segmentation is the intensity fitting term while a phase scale measure complements the regularization term.We propose a fast, yet simple, brute-force numerical algorithm and present experimental results showing the robustness and stability of the proposed model.