Quantum-Behaved Particle Swarm Optimization for the Segmentation of Kidney Stone CT Images

Image segmentation is a crucial element of image processing that divides an image into distinct regions based on pixel intensity, facilitating detailed analysis and interpretation of various image components. Conventional segmentation techniques often struggle with challenges such as local minima entrapment and premature convergence, especially in complex pixel search spaces, which can also be computationally intensive as the number of threshold levels increases. To overcome these limitations, a Quantum-behaved Particle Swarm Optimization (QPSO) approach is employed for multi-level thresholding. By integrating quantum mechanics principles with classical Particle Swarm Optimization, QPSO introduces quantum behaviors that enhance the algorithm’s exploration capabilities, allowing for a more thorough search of the solution space and reducing the risk of premature convergence to local optima. Meanwhile, Kapur’s entropy method is applied to segment images into distinct regions based on optimal pixel values, thereby enhancing segmentation precision. The algorithm’s performance is rigorously evaluated using kidney stone (Nephrolithiasis) datasets from Kaggle, with segmentation quality assessed through metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Feature Similarity Index Measure (FSIM), optimal thresholds, and best fitness values. Statistical reliability is ensured through the Wilcoxon signed-rank test and the Friedman ranking test. Experimental results demonstrate that QPSO significantly outperforms other algorithms, achieving an SSIM of 0.96, FSIM of 0.98, and PSNR of 29.80, highlighting its efficacy in addressing complex image segmentation challenges.