Nuseiba M. Altarawneh¹, SuhuaiLuo¹, Brian Regan¹, ChangmingSun², ¹The University of Newcastle, Australia and ²CSIRO Computational Informatics, Australia

In this paper, we propose a novel global threshold-based active contour model which employs a new edge-stopping function that controls the direction of the evolution and stops the evolving contour at weak or blurred edges. The model is implemented using selective binary and Gaussian filtering regularized level set (SBGFRLS) method. The method has a selective local or global segmentation property. 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 and afford the evolution more stability. The contour could be initialized anywhere inside the image to extract object boundaries. The proposed method performs well when the intensities inside and outside the object are homogenous. Our method is tested on synthetic, medical and Arabic-characters images with satisfactory results

Imagesegmentation, Active contour,Geodesic active contour,C-V model,Level set method, ZAC model.