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

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 r...

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Bibliographic Details
Published in:arXiv.org 2018-04
Main Authors: Saba Heidari Gheshlaghi, Madani, Abolfazl, Suratgar, AmirAbolfazl, Faraji, Fardin
Format: Article
Language:English
Subjects:
Algorithms
Brain
Clustering
Edge detection
Image segmentation
Lesions
Magnetic resonance imaging
Medical imaging
Multiple sclerosis
Parameter identification
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1804.03282