A Comparison of Mean Models and Clustering Techniques for Vertebra Detection and Region Separation from C-Spine X-Rays

In Computer Aided Diagnosis (CAD) tools, vertebra localization and detection are the essential steps for the diagnosis of cervical spine injuries. The accurate localization leads to accurate treatment, which is more challenging in case of poor contrast and noisy radiographs. This paper targets c-spine radiographs for the localization of vertebra using different vertebra templates, vertebra detection at each level using two different clustering techniques and gives a comparison between them. Moreover it separates the regions for each individual c-spine. It takes the poor contrast x-ray as input, enhance the contrast and detect the edges of enhanced image. After the edge detection, manually selected Region of Interest (ROI) helps in getting the edges of area covering C3 – C7 only. These edges along with 4 different template models of vertebra are used for the localization by Generalized Hough Transform (GHT). The results obtained are analyzed visually for the best localization template. Then, on voted points obtained after pruning, two clustering techniques Fuzzy C Means and K-Means are applied separately, to form clusters and centroids for each vertebra. Another part of this paper is to separate vertebra regions. For this, intervertebral points are calculated and then along these points, centroids are rotated using Affine Transformation. It gives parallel lines to vertebrae and joining them gives region for each vertebra. The comparison and testing of proposed technique has been performed using dataset ‘NHANES II’ publicly accessible at ‘The National Library of Medicine’, total 150 cervical spine scans are used securing accuracies 93:76%, 84:21% and 83:1% for FCM, K-Means and region separation, respectively.