Computational Analysis of Vertebral Body for Compression Fracture Using Texture and Shape Features

The primary goal in this paper is to automate radiological measurements of Vertebral Body (VB) in Magnetic Resonance Imaging (MRI) spinal scans. It starts by preprocessing the images, then detect and localize the VB regions, next segment and label VBs and finally classify each VB into three cases as being normal or fractured in case 1, benign or malignant in case 2 and normal, benign or malignant in case 3. The task is accomplished by extracting and combining distinct features of VB such as boundary, gray levels, shape and texture features using various Machine Learning techniques. The class balance deficit dataset towards normal and fractures is balanced by data augmentation which provides an enriched dataset for the learning system to perform precise differentiation between classes. On a clinical spine dataset, the method is tested and validated on 535 VBs for segmentation attaining an average accuracy 94.59% and on 315 VBs for classification with an average accuracy of 96.07% for case 1, 93.23% for case 2 and 92.3% for case 3.