Vertebra identification using template matching modelmp and K-means clustering

• Published in: International journal for computer assisted radiology and surgery Vol. 9; no. 2; pp. 177 - 187
• Main Authors: Larhmam, Mohamed Amine, Benjelloun, Mohammed, Mahmoudi, Saïd
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2014
• Subjects: Algorithms; Cluster Analysis; Computer Imaging; Computer Science; Health Informatics; Humans; Imaging; Medicine; Medicine & Public Health; Pattern Recognition and Graphics; Radiography; Radiology; Review Article; Spinal Diseases - diagnostic imaging; Spine - diagnostic imaging; Surgery; Vision; Vertebra segmentation; Generalized Hough transform; means clustering; Medical image analysis
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-013-0927-2

Purpose Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement. Methods Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K -means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment. Results The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5  % of the 330 tested cervical vertebrae. Conclusions An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.