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.

椎间盘退变是一种常见的腰椎间盘疾病。椎间盘退变导致腰痛，这是由于椎间盘（IVD）受伤而引起的。提出了一种从T2加权矢状MR图像诊断退行性椎间盘的自动诊断系统。提出了一种基于全自动期望值最大化（EM）的新IVD分割方法，以从矢状中段MR图像分割腰椎IVD。然后，从分割的IVD中提取基本强度，不变矩，Gabor特征的混合体。使用支持向量机（SVM）分类器将IVD分为退化性或非退化性。该系统针对93例患者的93幅临床矢状MR图像进行了培训，测试和评估。估计优化的超参数。针对数据集对提出的模型进行了测试和验证，其准确性为92.47％。进行了基于患者的分析，得出的准确度为92.86％。还使用k-NN，决策树，线性判别分析（LDA）和前馈神经网络等其他分类器对所提出模型的性能进行了分析。与最先进的方法相比，该方法的性能优于其他方法。该系统可以用作诊断退化性光盘的第二种方法。