A new combination active contour model for segmenting texture image with low contrast and high illumination variations

Abstract

This paper presents a novel combined energy functional based on edge and region information for active contour model, which can be applied to segment textured images containing low contrast and high illumination variations. In the proposed method, the edge features are calculated based on the phase-based approaches derived from the monogenic signal, which are robust to illumination variations in the image. These feature values are used in an edge energy functional to assist the active contour evolving towards the true object boundaries. To extract the region features, at first, we compute the normalized accumulated short-term autocorrelation (NASTA) values for the image, which suppress background clutter and enhance dissimilarities between objects and background. Next, a local cumulative distribution function (LCDF) of NASTA is calculated for every pixel in a local window around it and is used as a region feature for that pixel. Then, the obtained features are employed to define a new localized region-based energy functional that can correctly segment regions with same intensity mean and variance. The proposed edge and region energy terms are integrated and a regularization term is added to them to form our combined active contour. Experimental results indicate remarkable advantages of proposed method comparing to existing combination models.