DETECTION OF BRAIN TUMOUR USING A HYBRID SEGMENTATION MODEL BASED ON WATERSHED AND FLUID VECTOR FLOW

Medical Image segmentation deals with segmentation of tumor in CT and MR images for improved quality in medical diagnosis. Fluid Vector Flow (FVF) enhances the concave object extraction capability. We propose a new approach that we call the “fluid vector flow” active contour model to address problems of insufficient capture range and poor convergence for concavities. This paper intends to combine watershed algorithm with FVF snake model to reduce the computational complexity, to improve the insensitiveness to noise, and capture range. Specifically, the image will be segmented firstly through watershed algorithm and then the edges produced will be the initial contour of FVF model. This enhances the tumor boundaries and tuning the regulating parameters of the FVF snake mode by coupling the smoothness of the edge map obtained due to watershed algorithm. Superiority of the proposed work is observed in terms of capture range, concave object extraction capability, sensitivity to noise, computational complexity, and segmentation accuracy