Active contour driven by weighted hybrid signed pressure force for image segmentation

Abstract

This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF.