Kernel picture fuzzy clustering with spatial neighborhood information for MRI image segmentation

Image segmentation task becomes complex in the presence of vague boundary structure and spatially distributed noise. In the literature, fuzzy set and its extension-based clustering methods are utilized to address vagueness for the image segmentation problem. In this work, picture fuzzy set theoretic clustering method has been proposed to enhance MRI image segmentation that is robust to the vague boundary structure, nonlinearity and spatially distributed noise. Most of the related work for handling noise in segmentation process is majorly based on smoothing the image, which results in the loss of fine structure. Moreover, the nonlinearity present in the data leads to inaccurate segmentation result. In this work, we have defined the optimization problem for clustering the pixel intensity values for image segmentation using the picture fuzzy set theoretic approach, which handle the vagueness. Also, we have included a spatial neighborhood information term in the optimization problem of the proposed method that avoids smoothing and preserves fine details in the segmentation process. Further, kernel distance measure is utilized to capture the nonlinear structure present in image in the proposed optimization problem. The experiments are carried out on a synthetic image dataset and two publicly available brain MRI datasets. The comparison with the state-of-the-art methods shows that the proposed picture fuzzy clustering method provides better segmentation performance in terms of average segmentation accuracy and Dice score.