Improved PSO Based Multi-level Thresholding for Cancer Infected Breast Thermal Images Using Otsu

In this paper, an Improved Particle Swarm Optimization (IPSO) algorithm based bi-level and multi-level thresholding is proposed to segment the cancer infected breast thermal images using Otsu's function. In the proposed image segmentation work, histogram of the image is analyzed and the optimal thresholds are attained by maximizing Otsu's between class variance function. The performance of IPSO based segmentation process is demonstrated by consideringthermograms, being compared with state-of-the-art alternatives, such as Particle Swarm Optimization (PSO) and Darwinian PSO (DPSO). The proposed image segmentation procedure is directly implemented on RGB images. The performance assessment between algorithms is carried out using parameters, such as objective function, PSNR, SSIM and CPU time. The results confirm that, IPSO shows an overall enhancement over the alternatives, illustrating a tradeoff betweenCPU time and performance measure values.