PRACTICAL METHOD FOR IMAGE SEGMENTATION USING MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS COMBINED WITH GRAY–SCOTT MODEL

In this paper, a novel image segmentation method (MF-GS-DFA) using multifractal detrended fluctuation analysis (MF-DFA) combined with the Gray–Scott model is proposed, which is an improvement and extension of MF-DFA. Experimental results show that MF-GS-DFA retains high-precision details in segmenting natural and medical images and effectively extracts the contours and fine structures of target objects, overcoming the noise-sensitive limitations of MF-DFA. In addition, our proposed method also shows strong adaptability and robustness in processing images with complex backgrounds and rich texture features, providing a novel solution for biomedical engineering.