Multiple Sclerosis Diagnosis with Fuzzy C-Means (original) (raw)

2018, Computer Science & Information Technology

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.