BNIMS: Block-based Non-iterative Mean-shift Segmentation algorithm for Medical Images

Abstract


 
 
 This paper proposed a novel Block based Mean Shift Image Segmentation Algorithm to significantly reduce the computation and improve the segmentation accuracy for high resolution Medical Image. One of the challenging tasks in the image analysis and computer vision area is to correctly classify the pixels as there are no crisp borders among entities in an image. In this proposed methodology, it is observed that the computational complexity of the procedure is diminished by combining the pixels of an image of size MXN into non overlapping image blocks of size 3x3 by eliminating the iterative way of the mean shift procedure. This proposed algorithm shrinks the size of the image by one third of its original image for the computational purpose and then equalizes the number of computations for each new image pixel by constructing links between pixels using their first mean-shift vectors without any iteration process. The accurateness and effectiveness of the proposed methodology is matched with the existing Iterative Mean Shift Algorithm by accomplishing the empherical experiments on the Medical Images (Pathologies Buccales and Eye Retina) composed along with the similarity measures.