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It is of key importance to be able to evaluate the temporal changes seen in multiple sclerosis (MS) lesions in terms of location, shape, and area for estimating MS progression. The purpose of our study was to develop an automated method for detecting potential MS regions based on three types of brain magnetic resonance (MR) images: T1- and T2-weighted images, and fluid attenuated inversion-recovery (FLAIR) images. The brain regions were segmented based on a tri-linear interpolation technique and k-mean clustering technique. True positive regions and false positive regions were classified from three types of MR images using a support vector machine (SVM). We applied our proposed method to 60 slices of 20 MS cases. As a result, the sensitivity for detection of MS regions was 81.8%, with 14.1% false positives per true positive. This method should prove useful for the diagnosis of multiple sclerosis.

Development of a Computer-aided Diagnostic System for Detecting Multiple Sclerosis Using Magnetic Resonance Images