Vertebra Localization Using Shape Based Analysis and Unsupervised Clustering from X-Ray Images
Keywords:
Generalized Hough transform, Fuzzy c-Means, Vertebra localization, Shape based analysis, Unsupervised clusteringAbstract
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’.
References
J. Levy and D. Zieve. (April 06,2016). Imaging and Radiology. Available: http://slu.adam.com/content.aspx?productId=617&pid=1&gid=007451
L. A. Nadalo, et al. (April 06, 2016). Thoracic Spinal Trauma Imaging. Available: http://emedicine.medscape.com/article/397896-overview#a1
A. Ngrdi and G. Vrbov. (June 24, 2016)."Anatomy and Physiology of the Spinal Cord," in Transplantation of Neural Tissue Into the Spinal Cord, A. Nogradi, Ed., 1st ed: Springer. Available: https://books.google.com.pk/books?id=E3AkpCqhHP8C
L. Jasmin. (April 24, 2016). Spinal Cord Trauma. Available: http://www.nytimes.com/health/guides/disease/spinal-cord-trauma/overview.html
T. F. Cootes and C. J. Taylor, "Statistical Models of Appearance for Computer Vision," University of Manchester, Manchester, UK, 2004.
D. H. Ballard, "Generalizing the hough transform to detect arbitrary shapes," Pattern Recognition, vol. 13, no. 2, pp. 183-194, 1981.
R. S. Alomari, et al., "Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model," IEEE Trans. Med. Imag., vol. 30, no. 1, pp. 1-10, 2011.
M. A. Larhmam, et al., "Vertebra identification using template matching modelmp and K-means clustering," Int. J. Comp. Assisted Radiol. Surg., vol. 9, no. 2, pp. 177-187, 2014.
T. Klinder, et al., "Automated model-based vertebra detection, identification, and segmentation in CT images," Medi. Image Anal., vol. 13, no. 3, pp. 471-482, 2009.
M. Benjelloun and S. Mahmoudi, "X-ray image segmentation for vertebral mobility analysis," Int. J. Comput. assisted Radiol. Surg., vol. 2, no. 6, pp. 371-383, 2008.
F. Lecron, et al., "Heterogeneous computing for vertebra detection and segmentation in X-ray images," J. Biomed. Imag., vol. 2011, no. 5, pp. 5:1-5:12, 2011.
M. A. Larhmam, et al., "Semi-automatic detection of cervical vertebrae in X-ray images using generalized hough transform," in Proc. 2012 3rd Int. Conf. Image Process. Theory, Tools and Applicat. (IPTA), 2012, pp. 396-401.
J. Yao, et al., "Automated spinal column extraction and partitioning," in Proc. 3rd IEEE Int. Symp. Biomed. Imag.: Nano to Macro, 2006, pp. 390-393.
M. Benjelloun and S. Mahmoudi, "Spine localization in X-ray images using interest point detection," J. Digit. Imag., vol. 22, no. 3, pp. 309-318, 2009.
M. Benjelloun, et al., "A framework of vertebra segmentation using the active shape model-based approach," J. Biomed. Imag., vol. 2011, no. 9, pp. 1-14, 2011.
X. Dong and G. Zheng, "Automated vertebra identification from X-ray images," in Proc. Int. Conf. Image Anal. Recog., 2010, pp. 1-9.
S. Casciaro and L. Massoptier, "Automatic vertebral morphometry assessment," in Proc. 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2007, pp. 5571-5574.
F. Lecron, et al., "Fully automatic vertebra detection in X-ray images based on multiclass SVM," in SPIE Medical Imaging, Int. Soc. Optics Photonics, 2012, pp. 83142D- 83142D.
J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well Separated Clusters'," Cybernetics Sys., vol. 3, no. 3, pp. 32 - 57, 1973.
J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms: Springer Science & Business Media, 2013.
NIH. (Jan 27th, 2016). NHANES II X-ray Images. Available: https://archive.nlm.nih.gov/proj/ftp/ftp.php
T. Fawcett, "An introduction to ROC analysis," Pattern Recog. Lett., vol. 27, no. 8, pp. 861-874, 2006.
M. Benjelloun, et al., "Template Matching Approaches Applied to Vertebra Detection," in Advances in Image Segmentation, P.-G. P. Ho, Ed., 1st ed: InTech, 2012.
