Adaptive Segmentation Model for Images with Intensity Inhomogeneity based on Local Neighborhood Contrast

Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing,especially in medical image processing and analysis.Some local region-based models work well on handling intensity inhomogeneity,but they are always sensitive to contour initialization and high noise.In this paper,we present an adaptive segmentation model for images with intensity inhomogeneity in the form of partial differential equation.Firstly,a global intensity fitting term and a local intensity fitting term are constructed by employing the global and local image information,respectively.Secondly,a tradeoff function is defined to adjust adaptively the weight between two fitting terms,which is based on the neighborhood contrast of image pixel.Finally,a weighted regularization term related to local entropy is used to ensure the smoothness of evolution curve.Meanwhile,a distance regularization term is added for stable level set evolution.Experimental results show that the proposed model without initial contour can segment inhomogeneous images stably and effectively,which thereby avoiding the influence of contour initialization on segmentation results.Besides,the proposed model works better on noise images comparing with two relevant segmentation models.