عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Most people experience low back pain at least once in their lifetime. Lumbar disc herniation is one of the major causes of low back pain. Treatment methods for disc herniation are very diverse. So, diagnose the exact size of herniation and it`s location can greatly helps specialists in choosing the best treatment methods. In this research, an automated method for diagnosing lumbar disc herniation using MR images is proposed. To achieve this goal, 130 MR images was collected . In the proposed method, using three algorithms, namely region growing, OTSU and active contour, the intervertebral discs and their boundary were precisely separated from the background of the image. In the next step, after extracting the most significant features of the image, images were divided into healthy and unhealthy classes by SVM classifier with 89.9% accuracy. Classification accuracy also compared with other classifiers such as KNN, ensemble, decision trees, and finally determined, SVM classifier has the highest accuracy in classification.