Improving the segmentation of digital images by using a modified Otsu’s between-class variance

Image segmentation is a critical stage in the analysis and pre-processing of images. It comprises dividing the pixels according to threshold values into several segments depending on their intensity levels. Selecting the best threshold values is the most challenging task in segmentation. Because of their simplicity, resilience, reduced convergence time, and accuracy, standard multi-level thresholding (MT) approaches are more effective than bi-level thresholding methods. With increasing thresholds, computer complexity grows exponentially. A considerable number of metaheuristics were used to optimize these problems. One of the best image segmentation methods is Otsu’s between-class variance. It maximizes the between-class variance to determine image threshold values. In this manuscript, a new modified Otsu function is proposed that hybridizes the concept of Otsu’s between class variance and Kapur’s entropy. For Kapur’s entropy, a threshold value of an image is selected by maximizing the entropy of the object and background pixels. The proposed modified Otsu technique combines the ability to find an optimal threshold that maximizes the overall entropy from Kapur’s and the maximum variance value of the different classes from Otsu. The novelty of the proposal is the merging of two methodologies. Clearly, Otsu’s variance could be improved since the entropy (Kapur) is a method used to verify the uncertainty of a set of information. This paper applies the proposed technique over a set of images with diverse histograms, which are taken from Berkeley Segmentation Data Set 500 (BSDS500). For the search capability of the segmentation methodology, the Arithmetic Optimization algorithm (AOA), the Hybrid Dragonfly algorithm, and Firefly Algorithm (HDAFA) are employed. The proposed approach is compared with the existing state-of-art objective function of Otsu and Kapur. Qualitative experimental outcomes demonstrate that modified Otsu is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge, and image segmentation quality.