A new multiphase soft segmentation with adaptive variants

Authors:
Hongyuan Wang;Fuhua Chen;Yunmei Chen
Affiliations:
School of Information Science & Engineering, Changzhou University, Changzhou, China;Department of Natural Science & Mathematics, West Liberty University, West Liberty, WV;Department of Mathematics, University of Florida, Gainesville, FL
Venue:
Applied Computational Intelligence and Soft Computing
Year:
2013

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Abstract

Soft segmentation is more flexible than hard segmentation. But the membership functions are usually sensitive to noise. In this paper, we propose amultiphase soft segmentationmodel for nearly piecewise constant images based on stochastic principle, where pixel intensities are modeled as random variables with mixed Gaussian distribution. The novelty of this paper lies in three aspects. First, unlike some existingmodels where the mean of each phase is modeled as a constant and the variances for different phases are assumed to be the same, the mean for each phase in the Gaussian distribution in this paper is modeled as a product of a constant and a bias field, and different phases are assumed to have different variances, which makes the model more flexible. Second, we develop a bidirection projected primal dual hybrid gradient (PDHG) algorithm for iterations of membership functions. Third, we also develop a novel algorithm for explicitly computing the projection from RK to simplex ΔK-1 for any dimension K using dual theory, which is more efficient in both coding and implementation than existing projection methods.