Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields

Abstract

Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and progress monitoring of MS is performed by the aid of T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying results. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues and may also reduce the execution time, while the GMMF classifier is robust to noise and also computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results without requiring user assistance nor anatomical prior information.