Energy curve based enhanced smell agent optimizer for optimal multilevel threshold selection of thermographic breast image segmentation

Abstract

Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu’s between-class variance and Kapur’s entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method’s efficacy.