Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease

Essam H. Houssein, Nada Abdalkarim, Kashif Hussain, Ebtsam Mohamed

Research output: Contribution to journalArticlepeer-review


Liver-related diseases significantly contribute to global mortality rates. Accurate segmentation of liver disease from CT scans is essential for early diagnosis and treatment selection, particularly in computer-aided diagnosis (CAD) systems. To address challenges posed by inconsistent liver presence and unclear boundaries, an enhanced Snake Optimization (SO) algorithm is proposed that integrates with opposition-based learning (OBL) called (SO-OBL), proving effective in global optimization and multilevel image segmentation. Experiments using CEC’2022 test functions compare SO-OBL with eleven recent and state-of-the-art metaheuristic algorithms, demonstrating its superior performance. Additionally, an advanced liver disease segmentation model based on SO-OBL incorporates an optimized multilevel thresholding technique, leveraging Otsu’s function. Notable segmentation metric results, including FSIM = 0.947, SSIM = 0.941, PSNR = 24.876, MSE = 236.88, and execution time = 0.281, underscore the model’s efficiency and potential for accurate diagnosis in CAD systems.
Original languageEnglish
Pages (from-to)107922
JournalComputers in Biology and Medicine
Early online date4 Jan 2024
Publication statusPublished - 6 Jan 2024

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