Automatic ROI Detection in Lumbar Spine MRI

Abstract

Low back pain (LBP) is one of the most common diseases affecting a large number of people. Diagnosis and treatment of LBP require quick, accurate imaging methods. Magnetic resonance imaging (MRI) is effective in distinguishing between vertebra, intervertebral disc and spinal cord, and thus is used frequently in spinal cord injury (SCI) diagnosis. This paper proposes a fully automated approach to detecting region of interest (ROI) using T2-weighted MRI images. Our dataset included the cases of 100 patients who suffered from LBP. In total, 2000 axial and 1200 sagittal ROI were marked in the Lumbar spine. Extracted ROIs were used in the cascade classifier learner. In this method, ROI detection consists of two processes. First the ROIs are specified using the cascade classifier, and then via a process, non-regions of interest (NROIs) are discarded. Histogram of Oriented Gradient (HOG) was used as the feature descriptor in each stage of the Cascade classifier. This method does not require background knowledge of input images and it is reliable regardless of the images size, contrast and clinical abnormally of cases. The quantitative and qualitative evaluation results of the proposed ROI detector were 83% and above 94%, respectively.