Segmentation of Multiple Sclerosis lesion in brain MR images using Fuzzy C-Means

TL;DR

This paper presents a novel method combining modified fuzzy C-means clustering and Canny edge detection to improve segmentation of multiple sclerosis lesions in brain MRI images.

Contribution

It introduces a modified fuzzy C-means algorithm integrated with Canny edge detection, establishing a relationship between MS lesions and edges for better segmentation.

Findings

Enhanced accuracy in MS lesion segmentation

Established a relationship between lesions and edges

Derived conditions for optimal clustering parameters

Abstract

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.