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

Abstract: 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