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This paper present a segmentation of vertebral bodies from spinal MR images based on neighborhood informa- tion Gauss weighted and local contraction. First use a cut-off window (55) around each pixel and stack the gray values inside the window into a vector, adopt the Gaussian kernel function to incorporate local spatial information, an adaptive local scaling parameter is used to refine the segmentation rather than a fixed scaling parameter to avoid the manually tuned parameter. Finally, the built affinity is introduced into the segmentation process by using a graph-based method to achieve the complete target. Extensively experiments show that the present method can segment the vertebral bodies smoothly and clearly, and it has stronger anti-noise property and higher segmentation precision than the conventional methods. The robust and accurate result of segmentation should serve image registration and the analysis of spinal de- formities. It is a general method for segmenting object that can develop to segment other tissues and organs.

Research on Segmentation of Vertebral Bodies from Spinal MR Images based on Gauss Weighted and Local Contraction