A unified approach for lesion segmentation on MRI of multiple sclerosis.

Accurate determination of lesion volumes on brain MR images is hampered by the presence of a large number of false positive and negative classifications. A strategy that combines parametric and nonparametric techniques is developed and implemented for minimizing the false classifications. Initially, CSF and lesions are segmented using Parzen window classifier. Image processing, morphological operations, and ratio map of proton density (PD) and T2 weighted images are used for minimizing false positives. Lesions are delineated using fuzzy connectedness principle. Contextual information was used for minimizing false negative lesion classifications. Gray and white matter classification is realized using HMRF-EM algorithm.