Vertebra Localization Using Shape Based Analysis and Unsupervised Clustering from X-ray Images

Abstract

Accurate vertebral detection in X-ray images is a challenging task mainly due to low contrast and noisy set of image data. For the diagnosis of spinal disorders such as cervical spine trauma and whiplash, the detection and segmentation of vertebra are the fundamental tasks. The first step in detection process is the vertebra localization. In this paper, we propose a new method for the cervical vertebra localization problem. The proposed method contributes a novel composition of a mean model matching using the Generalized Hough Transform (GHT) and unsupervised clustering technique. To detect edges and enhance image contrast, preprocessing is performed on the input X-ray images. After manually selecting region of the interest (ROI), we use a separately generated geometric mean model as a template. A modified GHT is then used for the localization of vertebra followed by Fuzzy c-Means (FCM) clustering technique to obtain centroids of targeted five vertebras (C3 − C7). The proposed method secured localization accuracy of 96.88% when tested on 50 X-ray images of publically available database ‘NHANESII’.