A COMPARATIVE-ANALYSIS OF SEVERAL TRANSFORMATIONS FOR ENHANCEMENT AND SEGMENTATION OF MAGNETIC-RESONANCE IMAGE SCENE SEQUENCES

We compare the performance of the eigenimage filter to that of several other filters, applied to magnetic resonance image (MRI) scene sequences for images enhancement and segmentation. Comparisons are made with principal component analysis, matched, modified-matched, maximum contrast, target point, ratio, log-ratio, and angle image filters. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, analytical expressions for SNRs and CNRs of filtered images are derived, and CPV by a linear filter is studied. Properties of filters are illustrated through their applications to simulated and acquired MRI sequences of a phantom study and a clinical case; advantages and weaknesses are discussed. Our conclusion is that the eigenimage filter is the optimal linear filter that achieves SDF and CPV simultaneously.