Saba Heidari Gheshlaghi¹, Abolfazl Madani², AmirAbolfazl Suratgar³ and Fardin Faraji⁴, ^1,3Amirkabir University of Technology (Tehran Polytechnic), Iran, ²South Tehran Branch Islamic Azad University (IAU), Iran and ⁴Arak University of Medical Sciences, Iran

Magnetic resonance imaging (MRI) can support and substitute clinical information in the diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm for MS lesion segmentation. We revisit the modification of properties of fuzzy c means algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply canny contraction principle, and establish a relationship between MS lesions and edge detection. For the special case of FCM, we derive a sufficient condition for fixed lesions, allowing identification of them as (local) minima of the objective function.

Multiple Sclerosis, MRI, T2, fuzzy c-means (FCM), Canny