Multi-Scale B-Spline Level Set Segmentation Based On Gaussian Kernel Equalization

Images with weak contrast, overlapped noise and texture of the object and background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its level set formulation consists edge based term, region-based term and shape constraint term. The edge-based term is constructed using a newly defined edge stopping function. The region-based term is derived from parameter-free Gaussian probability density function (pdf) and multiple Gaussian kernel are used to gray equalization. The shape constraint term is used to constrain contour evolution at different scales of image pyramid. For an intrinsic smoothing segmentation contours, the level set function is explicitly represented by B-spline basis functions. Finally, a convolution is used during the energy minimization. Experimental results on synthetic and real images validate the robustness and high accuracy boundaries detection for low contrast, noise and texture images.