Degenerative disc disease diagnosis from lumbar MR images using hybrid features

Abstract

Disc degeneration is a common type of lumbar disc disease. Disc degeneration leads to low back pain, and it is caused due to injury in Intervertebral Disc (IVD). An automatic diagnostic system to diagnose degenerative discs from T2-weighted sagittal MR image is proposed. A fully automated Expectation-Maximization (EM)-based new IVD segmentation is proposed to segment the lumbar IVD from mid-sagittal MR image. Then, a hybrid of basic intensity, invariant moments, Gabor features are extracted from segmented IVDs. The IVDs are classified as degenerative or non-degenerative using Support Vector Machine (SVM) classifier. The proposed system is trained, tested and evaluated for 93 clinical sagittal MR images of 93 patients. The optimized hyperparameters are estimated. The proposed model is tested and validated for the dataset and obtained an accuracy of 92.47%. The patient-based analysis was performed and obtained an accuracy of 92.86%. The performance analysis of the proposed model with other classifiers like k-NN, decision tree, Linear Discriminant Analysis (LDA) and Feedforward neural network is also analyzed. This proposed method outperforms when compared with state-of-the-art methods. This system can be used as a second opinion in diagnosing degenerative discs.