出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper, we propose a novel global threshold-based active contour model which employs a newedge-stopping function that controls the direction of the evolution and stops the evolving contour atweak or blurred edges. The model is implemented using selective binary and Gaussian filteringregularized level set (SBGFRLS) method. The method has a selective local or global segmentationproperty. It selectively penalizes the level set function to be a binary function. This is followed byusing a Gaussian function to regularize it. The Gaussian filters smooth the level set function andafford the evolution more stability. The contour could be initialized anywhere inside the image toextract object boundaries. The proposed method performs well when the intensities inside andoutside the object are homogenous. Our method is tested on synthetic, medical and Arabiccharactersimages with satisfactory results
关键词：Imagesegmentation; Active contour;Geodesic active contour;C-V model;Level set method; ZAC;model.
