AUTOMATIC CONTRAST ENHANCEMENT OF WHITE MATTER LESIONS IN FLAIR MRI

This work concerns the development of a novel contrast enhancement algorithm for FLAIR-weighted cerebral MRI with white matter lesions (WML). The proposed method utilizes both a robust estimate of edge magnitude and intensity values to discriminate between pathological and non-pathological information. These two features are combined through several transformations, such that WML are highlighted, and normal appearing white/gray matter are suppressed. The technique utilizes information solely computed from each image and thus adapts to the input image's characteristics. The results show a significant improvement of the contrast between white matter lesions and other brain tissue (average contrast improvement of 41.1%). To demonstrate the robustness of such an enhancement scheme for WML analysis, a threshold-based segmenter is applied, which extracts the WML with good results.