Local Dispersion Optimized LoG and Image Fitting for Intensity Inhomogeneous Image Segmentation
Md. Prantikuzzaman, C. M. A. Rahman, Md. Moinul Islam · 2022
Region-based active contour models (ACM) have shown better performance compared to the edge-based models in numerous aspects like noise tolerance, inhomogeneity and background complexity correction, initialization robustness and speed of curve evolution. However, the fusion of edge and region based methods has circumvented the performance of the edge and region based methods working independently. Hence, this paper reveals a new technique to optimize Laplacian of Gaussian (LoG), the popular second order edge detector by local dispersion based edge estimation. The local dispersion computed image assists the optimization process to smooth the region without object boundary and simultaneously enhances the boundaries. The optimized LoG is added with the local image fitting (LIF) energy functional inside a variational framework with suitable scaling parameters. Thus an efficient fusion of region based energy and optimized edge energy performs with greater efficiency in terms of segmentation accuracy, noise tolerance, initial contour placement, iteration time and complex background suppression.