Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing

• Published in: Journal of bionics engineering Vol. 20; no. 5; pp. 2240 - 2275
• Main Authors: Chen, Jiaochen, Cai, Zhennao, Chen, Huiling, Chen, Xiaowei, Escorcia-Gutierrez, José, Mansour, Romany F., Ragab, Mahmoud
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Biochemical Engineering; Bioinformatics; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Chronic conditions; Engineering; Entropy (Information theory); Image processing; Image quality; Image segmentation; Kidneys; Lupus nephritis; Morbidity; Nephritis; Pathology; Quality assessment; Research Article; Risk factors; Search algorithms; Systemic lupus erythematosus; Swarm intelligence algorithms; 2D Rényi entropy; Multi-threshold image segmentation; Bionic algorithm; Cuckoo search algorithm; Renal pathology
• ISSN: 1672-6529; 2543-2141; 2543-2141
• DOI: 10.1007/s42235-023-00365-7

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 Adaptive β-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.