Hyperheuristic method for multilevel thresholding image segmentation Abstract   In digital image processing, one of the most relevant tasks is to classify pixels depending on their intensity level. To perform this process there exist different traditional methods as Otsu or Kapur, such methods are used to compute the thresholds that divide the histogram of the image into different groups. These methods are easy to implement for a single threshold; however, the computational effort is affected when more thresholds are required. Therefore, different metaheuristic based approaches have been proposed, but each of them has its properties and limitations. So, this paper introduces an alternative concept to the image segmentation which is called hyperheuristic that at each iteration determines the optimal execution sequence of metaheuristic algorithms that provides the optimal thresholds. The proposed method consists of two layers, in the first layer, the genetic algorithm (GA) is used to determine the execution sequence of the metaheuristic algorithms. While the second layer contains the set of four metaheuristic algorithms that executed in a specific order, assigned by the current solution of GA, to update the threshold population. In order to evaluate the performance of the proposed approach, it has been tested over a set of benchmark images and the results provide a good performance in terms of quality of segmentation. Moreover, experimental comparisons support that the proposed hyperheuristic is able to find more accurate solutions than other algorithms.

Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.