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Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing 被引量:1
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作者 Jiaochen Chen Zhennao Cai +4 位作者 Huiling Chen Xiaowei Chen José Escorcia-Gutierrez Romany F.Mansour Mahmoud Ragab 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第5期2240-2275,共36页
Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopa... Lupus Nephritis(LN)is a significant risk factor for morbidity and mortality in systemic lupus erythematosus,and nephropathology is still the gold standard for diagnosing LN.To assist pathologists in evaluating histopathological images of LN,a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images.This method is based on an improved Cuckoo Search(CS)algorithm that introduces a Diffusion Mechanism(DM)and an Adaptiveβ-Hill Climbing(AβHC)strategy called the DMCS algorithm.The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset.In addition,the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images.Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution.According to the three image quality evaluation metrics:PSNR,FSIM,and SSIM,the proposed image segmentation method performs well in image segmentation experiments.Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images. 展开更多
关键词 Multi-threshold image segmentation 2D Rényi entropy Renal pathology Cuckoo search algorithm Swarm intelligence algorithms Bionic algorithm
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