Multi-scale level set image segmentation combined of gradient and region information
Xili Wang · Computer Engineering and Applications Journal · 2010
This paper proposes a multi-scale level set algorithm for image segmentation which combines of gradient and re-gion information.An energy function is constructed which combines of gradient and region information and gets a hybrid Chan-Vese model,which constructs an edge detection function based on wavelet high-frequency components in gradient con-straint term and applies region term of Chan-Vese model in region constraint term.Then use variational method to solve and eliminate the re-initialization procedure.The original image is firstly transformed into the wavelet domain to get a coarse ap-proximation,and an approximation contour is obtained on the coarse approximation by the hybrid Chan-Vese model.The ap-proximation contour is interpolated into the original-scale contour.Then the original-scale contour is taken as an initial level set function and the next active contour evolution which applies the Chan-Vese model of eliminating re-initialization is per-formed on the original image to get the real contour.Experimental results show that this method has higher evolution efficien-cy and quality than traditional methods in the condition of equal model parameters.