Noisy image segmentation utilizing entropy-adaptive fractional differential-driven active contours

Abstract

The central challenge in noisy image segmentation is how to effectively suppress or remove noise while preserving important features, thereby achieving accurate image segmentation. Active contour models are widely utilized in these tasks. Nevertheless, they are unable to remove high noise while segmenting images with weak edges. In order to mitigate the adverse effects of non-uniformity while preserving the details of the image on image segmentation, a novel approach is introduced: the adaptive fractional differential active contour image segmentation method. This method aims to address the aforementioned problem. Our methods adaptively define the fractional order using the proposed entropy, which enhances the edge extraction ability of image entropy in the presence of image intensity inhomogeneity and noise, different orders are applied to different pixels. The introduced entropy demonstrates resilience against significant noise, thereby enhancing the model’s capacity to accurately and seamlessly delineate boundaries. Empirical evaluations conducted on various test images substantiate the model’s efficacy in addressing intensity inhomogeneity and achieving exceptional segmentation accuracy.