Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation

Abstract

The segmentation of epithelial layers from oral histopathology images plays a crucial role for early detection of oral cancer disease. As a result, more accurate segmentation of this layer is of the utmost importance for a Computer Aided Diagnosis (CAD) system. Therefore, this paper presents a superpixel image-based clustering technique using an improved Nature-Inspired Optimization Algorithm (NIOA), called the Cooperative Search (CS) algorithm. Here, superpixel image is utilized to construct a faster and noise-resistant oral histopathology image clustering method. The designed CS, on the other hand, is based on two popular NIOAs, the Aquila Optimizer (AO) and the Particle Swarm Optimizer (PSO), has better exploration-exploitation abilities. This CS has been used in the image clustering field to circumvent the problem of local optima trapping. On the other hand, a comparative investigation of three popular superpixel strategies with the proposed CS optimizer has been conducted to determine the optimal superpixel strategy for epithelial layer segmentation from clean as well as noisy oral histopathology images. Finally, the optimal superpixel strategy with CS has been tested with other state-of-the-art image segmentation techniques in the epithelial layer segmentation domain. The results obtained by the proposed segmentation technique are 98.82%, 96.70%, 97.60%, and 95.25%, in terms of average Accuracy, MCC, Dice, and Jaccard, respectively, which all are better than the cutting-edge segmentation methods. The proposed segmentation model is also evaluated over the leaf segmentation model and achieved better results visually and numerically. The optimization efficiency of the CS has also been measured over CEC2019 mathematical benchmark test functions and produced competitive results compared to other tested NIOAs.