Clustering based Segmentation of MR Images for the Delineation and Monitoring of Multiple Sclerosis Progression

Abstract

This paper presents a clustering-based method for the detection of Multiple Sclerosis (MS) lesions, by including anatomical information, brain geometry and lesion features, while volume quantification is performed. The proposed method utilizes Fluid Attenuated Inversion Recovery (FLAIR) images for the delineation of the plaques and brain atrophy estimation. The methodology includes five steps: (i) image preprocessing, (ii) image segmentation utilizing the K-means clustering algorithm, (iii) post processing for elimination of false positives, (iv) delineation and visualization of the MS lesions, and (v) brain atrophy estimation. It is implemented in two different datasets; (a) a dataset of 3D FLAIR MR Images, acquired in 30 MS patients, and (b) a dataset of 15 FLAIR MR Images, provided by the MICCAI Challenge 2016. A sensitivity 73.80%, and 71.52% was achieved for the two datasets, respectively. Brain atrophy was determined only on the first dataset, since follow up scans are available.