© 2016 Elsevier B.V. The segmentation of images into different meaningful classes is an important task for automatic image analysis technique. The finite Gaussian mixture model is one of the popular models for parametric model based image segmentation. However, the normality assumption of this model induces certain limitations as a single representative value is considered to represent each class. In this regard, the paper presents a new clustering algorithm, termed as rough-probabilistic clustering, integrating judiciously the merits of rough sets and a new probability distribution, called stomped normal (SN) distribution. The intensity distribution of a class is represented by SN distribution, where each class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of any image is modeled as a mixture of finite number of SN distributions. The expectation–maximization algorithm is used to estimate the parameters of each class. Incorporating hidden Markov random field framework into rough-probabilistic clustering, a new method is proposed for accurate and robust segmentation of images. The performance of the proposed segmentation approach, along with a comparison with related methods, is demonstrated on a set of HEp-2 cell images, and synthetic and real brain MR images for different bias fields and noise levels.
Applied Soft Computing Journal
558 - 576