Magnetic resonance cholangiopancreatography image enhancement for automatic disease detection

To sufficiently improve magnetic resonance cholangiopancreatography (MRCP) quality to enable reliable computer-aided diagnosis (CAD).

A set of image enhancement strategies that included filters (i.e. Gaussian, median, Wiener and Perona-Malik), wavelets (i.e. contourlet, ridgelet and a non-orthogonal noise compensation implementation), graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow, and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise, artifacts, partial volume effect and large inter- and intra-patient image intensity variations, all of which pose problems in application development. Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them, with the purpose of biliary disease detection.

The results varied as expected since each algorithm capitalized on different characteristics of the images. For denoising, the Perona-Malik and contourlet approaches were found to be the most suitable. In terms of extraction of the significant biliary structures and removal of background, the thresholding approaches performed well. The interactive scheme performed the best, especially by using the strengths of the graph-cut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection.

Tests show promising results for some techniques, but not others, as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.