Combined image enhancement, feature extraction, and classification protocol to improve detection and diagnosis of rotator-cuff tears on MR imaging.

Purpose: The diagnosis of most rotator cuff tears (RCTs) relies upon magnetic resonance (MR) imaging, but direct capture of MR images without enhanced image processing leads to poor image contrast and potential misdiagnosis. Therefore, we developed a 2-stage model for the detection and diagnosis of injury of the supraspinatus tendon.

Methods: The first stage used coupled weighted histogram separation (WHS) to improve image enhancement, and the second stage extracted suspicious texture, features of both spatial and spectral domains, and sequential floating forward selection (SFFS) selected features conducive to classification of RCTs.