Fuzzy k-plane clustering method with local spatial information for segmentation of human brain MRI image

Abstract

Human brain MRI images are complex, and matters present in the brain exhibit non-spherical shape. There exits uncertainty in the overlapping structure of brain tissue, i.e. a lack of distinctness in the class definition. Soft clustering methods can efficiently handle the uncertainty, and plane-based clustering methods are found to be more efficient for non-spherical shape data. Fuzzy k-plane clustering (FkPC) method is a soft plane-based clustering algorithms that can handle the uncertainty in medical images, but its performance degraded in the presence of noise. In this research work, we incorporated local spatial information in the FkPC clustering method to handle the noise present in the image. This spatial regularization term included in the proposed FkPC_S method refines the membership value of noisy pixel with the help of immediate neighbour pixels information. To show the effectiveness of the proposed FkPC_S method, extensive experiments are performed on one synthetic image and two publicly available human brain MRI datasets. The performance of the proposed method is compared with 10 related methods in terms of average segmentation accuracy and dice score. The experiments result shows that the proposed FkPC_S method is superior in comparison with 10 related methods in the presence of noise. Statistically significance difference and superior performance of the proposed method in comparison with other methods are also found using Friedman test.