Title :
Normalized cuts in 3-D for spinal MRI segmentation
Author :
Carballido-Gamio, Julio ; Belongie, Serge J. ; Majumdar, Sharmila
Author_Institution :
Joint Graduate Group in Bioeng., Univ. of California, San Francisco, CA, USA
Abstract :
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 Nyström approximation method for applications different than medical imaging. In this paper we discuss the application of NCut with the Nyström 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.
Keywords :
biomedical MRI; bone; image segmentation; orthopaedics; Nystrom approximation method; anisotropic diffusion algorithm; human organs; medical image segmentation; normalized cuts; sagittal Tl-weighted magnetic resonance images; spectral graph theoretic method; spinal MRI segmentation; three-dimensional local brightness histrograms; vertebral bodies; Anisotropic magnetoresistance; Approximation methods; Biomedical imaging; Histograms; Humans; Image analysis; Image segmentation; Magnetic resonance; Magnetic resonance imaging; Performance analysis; Algorithms; Anatomy, Cross-Sectional; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Spine; Subtraction Technique;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2003.819929