Integrated spatial fuzzy clustering with variational level set method for MRI brain image segmentation

Advancements in medical imaging technologies have raised many effective analytical procedures. The promptness in the acquisition and resolution enhancements of the imaging modalities have given a lot more information to the physicians in a less intrusive way about their patients. Active contours are used to the path, segment and make images of an atomic structure matching. To do like that molding functions, that are resulting in the image data and proceeding information about size, shape, and location of this structure are considered. A part of active contour family is invoked from the level set method. The major hindrances of the level setting methods are the loading of controlling constraints and time complexity. The proposed method follows Spatial Kernel Fuzzy C-Means (SKFCM) and Variational Level Set Method (VLSM) to avoid all these imperfections. SKFCM is related to standard Fuzzy C-Means algorithm which makes uses of Gaussian RBF kernel function as a distance metric that incorporates spatial information. The VLSM uses the energy function to govern and scale down the exact processing time that will address the time complexity. The proposed system is a hybrid of both SKFCM and VLSM combined approach.