Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

• An algorithm for multiple sclerosis lesion detection in brain MRI is presented.
• An initial segmentation of brain tissues is performed by a modified EM algorithm.
• Lesions are located as outliers of the grey matter FLAIR intensity distribution.
• 45 cases from three different scanner machines were used to evaluate the algorithm.
• The results show a moderate agreement between manual and automatic detections.

Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.