A Novel Global Threshold-Based Active Contour Model
Nuseiba M. Altarawneh, Suhuai Luo, Brian G Regan, Changming Sun · 2014
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 by using 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 Arabiccharacters images with satisfactory results