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’.
