Segmentation Model for Noisy and Intensity Inhomogeneity Images via Logarithmic Density Function

Abstract

This manuscript is devoted to the study of a new image segmentation model for noisy and intensity inhomogeneity images based on logarithmic density function. Local image information is necessary for inhomogeneous images but at the same time, it is defective for noisy images as a consequence local information misguide the motion of active contour. However, the logarithmic function in our new proposed model is capable to capture minute details in images, while ignoring the noise in it which makes it robust in such kinds of images. Comparing with local Chan-Vese Model our new proposed model gives better performance treating noisy and intensity inhomogeneity images. Finally, experiments on some noisy and intensity inhomogeneity images show the robustness of our new proposed model.