A computer-based system for the discrimination between normal and degenerated menisci from Magnetic Resonance Images

Meniscal myxoid degeneration (MMD) represents a type of degenerative lesion, characterized by histological alterations of the meniscus. In the context of magnetic resonance (MR) imaging evaluation of MMD, the incidence of the condition is indicated by the presence of high intensity signal within the meniscus, while normal menisci are depicted as of homogeneously low intensity. In the present study, a computer based system is proposed for the automatic discrimination between normal and degenerated menisci, employing texture analysis of MR images. The sample of the study consisted of 55 MR images of the knee, corresponding to an equal number of individuals, who were subjected to MR scans. Following a specific protocol T1-weighted sagittal images of the knee joint were acquired, employing a system operating at 1.5 T. The depicted menisci were graded by consensus of two experienced radiologists, employing the scale proposed by Lotysch et al. Accordingly, 15 menisci were characterized as normal (Grade 0) and 40 as degenerated (20 of Grade 1 and 20 of Grade 2). Employing custom developed software a region of interest (ROI), corresponding to the posterior horn of the medial meniscus, was automatically determined on each MR image, on the basis of the region growing segmentation approach. Utilizing custom developed algorithms, a number of textural features, evaluating aspects of spatial variations of pixel intensities, were generated from the segmented ROIs. The calculated features were utilized in the design of a classification system, based on the Bayes classifier. The latter discriminated successfully 49 out of 55 menisci, accomplishing an overall accuracy of 89.1% (specificity accuracy 80%, sensitivity accuracy 92.5%). The proposed system may be of value as a decision support system for the diagnosis of MMD.