Alvina Goh1, Christophe Lenglet2, Mariappan Nadar3
1Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; 2Center for Magnetic Resonance Research & Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA; 3Siemens Corporate Research, Princeton, NJ, USA
This paper focuses on the segmentation and classification of multiple sclerosis lesions in magnetic resonance images. As MRI is the primary tool used in the diagnosis of multiple sclerosis, there is substantial interest in developing an algorithm that will detect lesions from such images. We present a flexible framework in which segmentation and classification are integrated. We assume that we are given a training set of MRI images which contains manually labeled regions of MS lesions. The algorithm we used combines two effective techniques from the computer vision literature: graph-based bottom-up methods and top-down generative models.