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1. Introduction
Automatic image segmentation is an important and challenging problem in computer vision and medical image analysis. The objective of image segmentation is to separate objects of interest from a given image based on different attributes such as shape, color, intensity, or texture. In recent years, several techniques have been reported for this purpose including graph cut [1, 2], improved watershed transform [3], suppressed fuzzy c-means [4], supervised fuzzy clustering [5], multithreshold based on differential evolution [6], and active contour model (ACM), which has been applied in different areas such as intravascular ultrasound images [7], automatic urban buildings [8], and natural images [9], to name a few.
The Active Contour Model is an energy-minimizing spline curve composed of discrete control points called snaxels. The curve is attracted towards features as edges of a target object through the evaluation of internal and external forces. The classical implementation of ACM is prone to be trapped into local minima problem and it is also highly sensitive to initialization of the control points because they require being close to the target object; otherwise failure of convergence will occur.
Since ACM was introduced by [10], many researchers have suggested adapting different techniques to work together with ACM in order to overcome its shortcomings. The suggested improvements of the classical ACM including the introduction of prior knowledge such as active shape models [11], shape prior applied on human cerebellum [12], ACM based on level set method [13], population-based methods such as genetic algorithms [14], differential evolution [15],...