Abstract
In the paper, we present an efficient method to solve the piecewise constant Mumford-Shah (M-S) model for two-phase image segmentation within the level set framework. A clustering algorithm is used to find approximately the intensity means of foreground and background in the image, and so the M-S functional is reduced to the functional of a single variable (level set function), which avoids using complicated alternating optimization to minimize the reduced M-S functional. Experimental results demonstrated some advantages of the proposed method over the well-known Chan-Vese method using alternating optimization, such as robustness to the locations of initial contour and the high computation efficiency.
Highlights
Image segmentation is one of the most important and critical tasks towards high-level vision modelling and analysis
In this paper, following the Chan-Vese (C-V) method, we propose an efficient method for minimizing the piecewise constant M-S functional
In order to test the sensitivity of the C-V method to contour initialization, we demonstrate the case of three real images with five different initial contours, as shown in Figures 3, 4, 5
Summary
Image segmentation is one of the most important and critical tasks towards high-level vision modelling and analysis. In [20], Bresson et al propose a global minimization of the active contour model based on the piecewise constant M-S model, in which the dual formulation is to be applied in minimization of the model and present a fast algorithm. These methods allow to compute high-quality solutions of the piecewise constant M-S functional. In this paper, following the Chan-Vese (C-V) method, we propose an efficient method for minimizing the piecewise constant M-S functional.
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