An Efficient Cell Segmentation Algorithm Based on Unsupervised Clustering and Morphology In traditional Chinese medicine processing domain, the xe2x80x98firepowerxe2x80x99 and xe2x80x98proper roastingxe2x80x99 are vital for medicinal efficacy of Chinese herbs. We take phellodendron microscopic images to determine if the roasting is appropriate according to some features of crystal fiber and stone cell, these features include color, geometrical appearance, and so on. The first step of feature analysis is to segment objects accurately. In the paper, we propose an efficient unsupervised algorithm to segment stone cell and crystal fiber in phellodendron microscopic images automatically without any humanu0027s intervene. Firstly, the superpixels is adopted, then the method extracts features such as RGB, Sobel, Harris and so on, clustering superpixels patches according to the characteristic of phellodendron, lastly morphological operations are implemented to produce segmented stone cell and crystal fiber. The experimental evaluation shows that the proposed algorithm can segment objects automatically with about 87% precision and 93% recall.

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.