Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

• Published in: Computers in biology and medicine Vol. 180; p. 109011
• Main Authors: Mostafa, Reham R., Khedr, Ahmed M., Aghbari, Zaher AL, Afyouni, Imad, Kamel, Ibrahim, Ahmed, Naveed
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2024; Elsevier Limited
• Subjects: Adaptive algorithms; Algorithms; Brain; Computational neuroscience; Computed tomography; Crayfish optimization algorithm; Differential evolution; Effectiveness; Evolutionary computation; Hemorrhage; Humans; Hypercubes; Image analysis; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Internal Medicine; Knee; Latin hypercube sampling; Magnetic Resonance Imaging - methods; Medical imaging; Medical personnel; Neuroimaging; Optimization; Other; Parameters; Performance evaluation; Quadratic interpolation strategy; Quantitative analysis; Rank tests; Signal to noise ratio; Two dimensional analysis; Wavelet function; Differential evolution; Image segmentation; Wavelet function; Crayfish optimization algorithm; Quadratic interpolation strategy
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109011

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems. •The development of HADECO, a hybrid global optimization algorithm that combines the crayfish optimization algorithm (COA) and differential evolution (DE), to avoid local optima and enhance solution quality.•A comprehensive comparison and analysis of HADECO and similar algorithms based on the IEEE CEC’22 benchmarks, demonstrating the strong optimization capability of HADECO.•A HADECO-based multi-threshold image segmentation (MTIS) method is proposed to boost both efficiency and segmentation accuracy.•Application of the HADECO-based MTIS method for segmenting medical images (MRI for knee and CT scan for ICH) at both low and high threshold levels, with experimental results validating the effectiveness of the proposed method.•Comparative analyses are conducted against similar methods within the MTIS framework, using three distinct metrics to assess the segmentation results, demonstrating that the proposed method can improve the quality of segmentation.