Points of interest detection in cervical spine radiographs by polygonal approximation

In this paper, we introduce a robust approach to detect points of interest in cervical spine radiographs. The perspective of this work is to segment the vertebrae on X-Ray images for the analysis of the vertebral mobility. In previous work, we proposed a segmentation technique based on Active Shape Model. The extraction and the detection of the vertebra corners can contribute to the automatic initialization of the Active Shape Model search and can give valuable information about the spine curvature. Here, we present the benefits of the polygonal approximation dedicated to the points of interest detection. The methodology developed here is composed of 3 stages: a contrast limited adaptive histogram equalization, a Canny edge detection filter and an edge polygonal approximation. The first histogram equalization step is a pretraitment needed to improve the image quality in order to perform a better contour detection. The Canny operator detects the edges in the radiograph which are used as an input to the polygonal approximation. The edges become segment lines whose intersections define corners. We compare the results obtained with our approach based on the polygonal approximation to results coming from the Harris corner detector.

[1]  Timothy F. Cootes,et al.  Active Shape Models - 'smart snakes' , 1992, BMVC.

[2]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[3]  Shang-Hong Lai,et al.  A statistical learning appproach to vertebra detection and segmentation from spinal MRI , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  Jack Koplowitz,et al.  Corner detection for chain coded curves , 1995, Pattern Recognit..

[5]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[6]  Alexander Wong,et al.  Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Ronald M. Summers,et al.  Level set based vertebra segmentation for the evaluation of Ankylosing Spondylitis , 2006, SPIE Medical Imaging.

[8]  P P Smyth,et al.  Vertebral shape: automatic measurement with active shape models. , 1999, Radiology.

[9]  Shang-Hong Lai,et al.  Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI , 2009, IEEE Transactions on Medical Imaging.

[10]  H. Sari-Sarraf,et al.  Hierarchical segmentation of cervical and lumbar vertebrae using a customized generalized Hough transform and extensions to active appearance models , 2004, 6th IEEE Southwest Symposium on Image Analysis and Interpretation, 2004..

[11]  Aly A. Farag,et al.  A Novel 3D Segmentation of Vertebral Bones from Volumetric CT Images Using Graph Cuts , 2009, ISVC.

[12]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[13]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[14]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[15]  Urs Ramer,et al.  An iterative procedure for the polygonal approximation of plane curves , 1972, Comput. Graph. Image Process..

[16]  L. Rodney Long,et al.  Use of shape models to search digitized spine X-rays , 2000, Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000.

[17]  Asif Masood,et al.  Dominant point detection by reverse polygonization of digital curves , 2008, Image Vis. Comput..

[18]  Hamed Sari-Sarraf,et al.  Customized Hough transform for robust segmentation of cervical vertebrae from X-ray images , 2002, Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation.

[19]  Kwan-Yee Kenneth Wong,et al.  Tracking Lumbar Vertebrae in Digital Videofluoroscopic Video Automatically , 2004, MIAR.

[20]  Christopher J. Taylor,et al.  Automatic Measurement of Vertebral Shape using Active Shape Models , 1996, BMVC.

[21]  Mohammed Benjelloun,et al.  A New Semi-automatic Approach for X-ray Cervical Images Segmentation using Active Shape Model , 2010, BIOSIGNALS.

[22]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Hong Shen,et al.  Localized priors for the precise segmentation of individual vertebras from CT volume data. , 2008, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[24]  Mohammed Benjelloun,et al.  A New Approach for Cervical Vertebrae Segmentation , 2007, CIARP.

[25]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .