Medical Image Segmentation and Registration Using a Grid based Dynamic Implicit Geometry of Level Set Method

Abstract

Medical image segmentation and registration of 2D and 3D image data is important in diagnosis, treatment planning, functional studies and computer guided therapies. Nowadays in medical diagnosis 3D medical model of human organ is created using multiple slices 2D image of CT / MRI. 3D medical model has many utilities includes surgery plan design, defect area inspection, personalized implant design, physical model fabrication and drawback of overlapping found in 2D image is overcome by 3D modeling. In this paper Region of interest (ROI) is extracted from level set technique and a grid based dynamic implicit geometry is used for evaluating and registration of segmented data. Later the registered slice generates the surface and finally forms the 3D model.