A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach

We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising.

[1]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[2]  Alejandro F. Frangi,et al.  Active shape model segmentation with optimal features , 2002, IEEE Transactions on Medical Imaging.

[3]  Timothy F. Cootes,et al.  Linking Sequences of Active Appearance Sub-Models via Constraints: An Application in Automated Vertebral Morphometry , 2003, BMVC.

[4]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[5]  Marcel Worring,et al.  Watersnakes: Energy-Driven Watershed Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[7]  Max A. Viergever,et al.  A discrete dynamic contour model , 1995, IEEE Trans. Medical Imaging.

[8]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

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

[10]  Mohammed Benjelloun,et al.  Semi-Automatic Vertebra Segmentation , 2010 .

[11]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[12]  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..

[13]  Li Bai,et al.  Landmark selection for shape model construction via equalization of variance , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[15]  Mohammed Benjelloun,et al.  Spine Localization in X-ray Images Using Interest Point Detection , 2009, Journal of Digital Imaging.

[16]  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.

[17]  Marleen de Bruijne,et al.  Image segmentation by shape particle filtering , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[18]  Mohammed Benjelloun,et al.  X-ray image segmentation for vertebral mobility analysis , 2008, International Journal of Computer Assisted Radiology and Surgery.

[19]  Marleen de Bruijne,et al.  Image segmentation by shape particle filtering , 2004, ICPR 2004.

[20]  Timothy F. Cootes,et al.  Statistical models of appearance for computer vision , 1999 .

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

[22]  Mohammed Benjelloun,et al.  Vertebra Edge Detection Using Polar Signature , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[23]  Tim Cootes,et al.  Automatic segmentation of lumbar vertebrae on digitised radiographs using linked active appearance models , 2006 .

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

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

[26]  Cristian Lorenz,et al.  Automated model-based vertebra detection, identification, and segmentation in CT images , 2009, Medical Image Anal..

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

[28]  Suhuai Luo.  Automated Medical Image Segmentation Using a New Deformable Surface Model , 2006 .

[29]  Luis Rueda,et al.  Progress in pattern recognition, image analysis and applications : 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007 Viña del Mar-Valparaiso, Chile, November 13-16, 2007 : proceedings , 2007 .

[30]  Mohammed Benjelloun,et al.  Vertebral Mobility Analysis Using Anterior Faces Detection , 2007, ACIVS.