Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bio-inspired 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 & density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bio-inspired 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 k-means clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bio-inspired clustering technique known as “Teacher-Learner Based Optimization”. In contrast to most of the well-known clustering techniques, TLBO doesn't 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 well-known clustering techniques.