Spatial context-based optimal multilevel energy curve thresholding for image segmentation using soft computing techniques

Abstract

Image segmentation using multilevel thresholding (MT) is one of the leading methods. Although, as most techniques are based on the image histogram to be segmented, MT approaches only include the occurrence frequency of particular intensity range disregarding each spatial information. Energy curve-based contextual information can help to improve the quality of the thresholded image as it computes not only the value of the pixel but also its vicinity. Therefore, the energy curve is intended to carry spatial information into a curve with the same properties as the histogram. In this paper, classical Otsu’s method (between-class variance) is combined with energy curve for multilevel thresholding to perform segmentation of colored images. The energy curve-based Otsu (Energy-Otsu) uses an exhaustive search process to determine the optimal threshold values. However, due to the multidimensionality and multimodal nature of the color images, it becomes challenging and highly complex to obtain optimal thresholds. Therefore, the cuckoo search (CS) algorithm is coupled with Otsu thresholding criteria to perform MT over the energy curve. The proposed Energy-Otsu-CS method produces better-segmented results as compared to other well-known optimization algorithms such as differential evolution, particle swarm optimization, firefly algorithm, bacterial foraging optimization, and artificial bee colony algorithm. The proposed approach is examined intensively regarding quality, and the numerical parameter analysis is presented to compare the segmented results of the algorithms against closely related current approaches such as gray-level co-occurrence matrix and Renyi’ entropy-based thresholding approaches.