Disc bulge diagnostic model in axial lumbar MR images using Intervertebral disc Descriptor (IdD)

Abstract

One of the common types of lumbar disc disease is bulging which cause low back pain, tingling, and numbness. An automatic diagnostic system to detect the disc pathology would be helpful to the radiologist. A computer aided diagnostic system is proposed to identify the disc bulge in axial lumbar spine MR images. A new EM based segmentation method is applied to segment the Intervertebral Disc (IVD) from the axial slice of T2-weighted MRI. After segmentation, the features are extracted by executing Histogram of Oriented Gradients (HOG) and a novel feature descriptor called as Intervertebral disc Descriptor (IdD). The features obtained are trained by Support Vector Machine(SVM). In this work, T2-weighted axial slices of lumbar MR images for 93 patients are used for evaluation. The proposed framework is trained, tested and validated on 675 clinical axial MR images of 93 patients, in which 184 are normal, 55 are herniated, and 436 are bulged images. On applying the proposed system, an accuracy of 92.78% is obtained for classifying normal and bulge and compared with different classifiers such as k-nn, decision trees and feed forward neural network. This model produces high accuracy, sensitivity, specificity, and f-score to detect bulge in the MRI. The model built with SVM produces a better result when compared with k-nn, decision trees and feed forward neural network. Also, the same model can be applied to detect other disc pathologies such as desiccation and degeneration.