A New Method for Segmentation of Multiple Sclerosis (MS) Lesions on Brain MR Images

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. In this study we applied gaussian mixture model (GMM) to segment MS lesions in MR images. Usually، GMM is optimized using expectation-maximization (EM) algorithm. One of the drawbacks of this optimization method is that، it does not convergence to optimal maximum or minimum. Starting from different initial points and saving best result، is a strategy which is used to reach the near optimal. This approach is time consuming and we used another way to initiate the EM algorithm. Also، FAST- Trimmed Likelihood Estimator (FAST-TLE) algorithm was applied to determine which voxels should be rejected. The automatically segmentation outputs were scored by two specialists and the results show that our method has capability to segment the MS lesions with Dice similarity coefficient (DSC) score of 0. 82.