Kidney segmentation in MR images using active contour model driven by fractional-based energy minimization

Abstract

In the field of diagnosis and treatment planning of kidney-related diseases, accurate kidney segmentation is challenging due to intensity inhomogeneity caused by imperfections during image acquisition process. This study presents a model, which consists of a novel fractional energy minimization for segmenting human kidney organ from MR images. Unlike existing active contour models (Chan–Vese), which uses gradient-based energy minimization that is sensitive to inhomogeneous intensity values, we propose a novel fractional Mittag–Leffler’s function for energy minimization, a technique more suitable to cope with the mentioned challenges. The proposed model exploits the special property of fractional calculus in maintaining high-frequency contour features while enhancing the low-frequency detail of texture in smooth area. Experimental results performed on complex kidney images using the proposed method show that the proposed model outperforms the existing models in terms of sensitivity, accuracy, Jaccard index and Dice coefficient.