Abstract
segmentation plays a vital role in medical image processing and computer vision. In case of medical scan images geometric level set functions perform accurate segmentation in good no of cases but develops irregularities during concave region evolution. These irregularities cause numerical errors and eventually destroy the stability of the evolution. In this paper, a new variational formulation known as distance regularization has a unique forwardandbackward (FAB) diffusion e ffect is used for the analysis of medical brain image scans which perform accurate segmentation in case of concavities. This method also eliminates the need of the costly rein itialization procedure. This method shows reliable and good convergence to the object boundaries with speed in case of concavities.
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