A Novel Diagonal Class Entropy-Based Multilevel Image Thresholding Using Coral Reef Optimization

In the normal image thresholding methods based on two-dimensional histogram, the edge information of the regions is not maintained because of the local averaging activity used. Moreover, the computation time increases with the increase in the level of thresholds. This paper focusses on retaining more edge information by calculating the image entropy along the diagonal regions of the gray level co-occurrence matrix inspired from the partitioned design structure matrix, which is a novel idea. In addition, the key to our success is the theoretical investigation of a novel diagonal class entropy (DCE) concept that utilizes the minimum area for computation. The benefits of the proposed method are: 1) improved results; 2) efficient to preserve more precise shape of the edges; and 3) the computation time decreases with the increase in the threshold levels. The optimal thresholds are obtained by minimizing the DCE using coral reef optimization (CRO). A first hand fitness function for multilevel image thresholding is derived. The fight for space and the efficient reproduction characteristics of the CRO makes it attractive for this application. Benchmark images from the Berkley segmentation dataset are taken to experiment. Our results are compared with other state-of-the-art thresholding methods. The results obtained are encouraging and may set the path for further investigation in the domain of multilevel thresholding.