Normalized cuts in 3-D for spinal MRI segmentation

Segmentation of medical images has become an indispensable process to perform quantitative analysis of images of human organs and their functions. Normalized Cuts (NCut) is a spectral graph theoretic method that readily admits combinations of different features for image segmentation. The computational demand imposed by NCut has been successfully alleviated with the Nystro/spl uml/m approximation method for applications different than medical imaging. In this paper we discuss the application of NCut with the Nystro/spl uml/m approximation method to segment vertebral bodies from sagittal T1-weighted magnetic resonance images of the spine. The magnetic resonance images were preprocessed by the anisotropic diffusion algorithm, and three-dimensional local histograms of brightness was chosen as the segmentation feature. Results of the segmentation as well as limitations and challenges in this area are presented.

[1]  Jitendra Malik,et al.  Spectral Partitioning with Indefinite Kernels Using the Nyström Extension , 2002, ECCV.

[2]  Joachim M. Buhmann,et al.  Non-parametric similarity measures for unsupervised texture segmentation and image retrieval , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Sharmila Majumdar,et al.  Normalized cuts for spinal MRI segmentation , 2002 .

[4]  Jitendra Malik,et al.  Extracting Global Structure from Gene Expression Profiles , 2002 .

[5]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  P. Perona.  Anisotropic diffusion processes in early vision , 1989, Sixth Multidimensional Signal Processing Workshop,.

[7]  Jitendra Malik,et al.  Textons, contours and regions: cue integration in image segmentation , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  David A. Clausi,et al.  Image segmentation using MRI vertebral cross-sections , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[9]  Jitendra Malik,et al.  Anisotropic Diffusion , 1994, Geometry-Driven Diffusion in Computer Vision.

[10]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Yongmin Kim,et al.  A methodology for evaluation of boundary detection algorithms on medical images , 1997, IEEE Transactions on Medical Imaging.

[12]  Jitendra Malik,et al.  Efficient spatiotemporal grouping using the Nystrom method , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  L L Wald,et al.  Phased array detectors and an automated intensity‐correction algorithm for high‐resolution MR imaging of the human brain , 1995, Magnetic resonance in medicine.