Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations

Abstract Distribution-based imaging is a promising methodology mainly to differentiate suspicious regions from surrounding tissues by applying a distribution to the images vertically or horizontally, ideally in both directions. The methodology is very useful for contouring and highlighting desired regions even under near-zero contrast conditions; it also leads to flexible segmentation of the lesions by parametric kernels and provides robust results when supported by solid post-segmentation protocols. Given these benefits, what we propose in this research is a specialized fuzzy 2-means algorithm enhanced by parametric distribution-based imaging framework to offer novel solutions for multiple-sclerosis (MS) identification and segmentation from flair MRI images. The interchangeable distributions employed in this research are Rayleigh, Weibull, Gamma, Exponential and Chi-square, which all are mathematically transmuted from Nakagami distribution. The Nakagami m-parameter is defining the shape of the distributions unless a special parameter exists; while the highlighted areas are segmented by fuzzy 2-means. All parameters are optimized using a set of MICCAI 2016 MS lesion segmentation challenge taken by Siemens Verio 3T scanner and 0.8245 dice score is achieved by Nakagami-Gamma. However, when the optimized framework is tested by other 4 sets with same resolution and size properties, the highest average dice score 0.7113 is obtained by Nakagami–Rayleigh; while Nakagami-Gamma transmutation is resulted in 0.7112 dice score with significantly better sensitivity.