Segmentation of spinal cord from computed tomography images based on level set method with Gaussian kernel

Abstract

In the human body, organs segmentation is the most imperative issues in therapeutic applications. The challenges are connected with medicinal image segmentation and low complexity between required organ and incorporating tissues. There exist a wide range of methodologies for how a segmentation problem can be comprehended. These methods want to have a spot specific region of individual bones. The particular part remains a test for spinal cord segmentation. As a result of the beforehand expressed downsides of the current spinal cord segmentation procedures, this paper proposes a modified spatial fuzzy C clustering with level set segmentation method to incorporate Neumann Boundary Condition, a third function, called by the level set evolution. Neumann Boundary Condition is utilized to specify the normal derivative of the function present on any surface. The proposed method gives better results of segmentation of the spinal cord organs. The execution of the proposed method proves its superiority in term of accuracy as compared with the other methods.