Polymorphous Bovine Somatic Cell Recognition Based on Feature Fusion.

Microscopic images of bovine milk somatic cells are used to classify neutrophils, epithelial cells, macrophages and lymphocytes. Using pattern recognition technology, the problem of classification and recognition is solved from different nature, levels and spaces. The proposed RKSGA-SVM algorithm is used to realize somatic cell image recognition. First, color, morphological and texture features of four types of cells are extracted separately, including geometric and moment invariant features. Second, ReliefF algorithm is used to calculate the weights of all features. According to preset cumulative contribution rate, the preliminary feature set is obtained. Third, redundant features are eliminated by Kolmogorov–Smirnov (KS) test, and the high-level optimization is obtained. The selected feature sets have remarkable distinguishing ability. Finally, the weighted optimal feature sets are obtained by weighted coefficient method on the advanced optimal feature sets. The overall accuracy of RKSGA-SVM algorithm is 99.00%, and Kappa coefficient is 0.987. The proposed algorithm has the advantages of balancing classification accuracy, eliminating redundancy and reducing feature dimension. On the premise of ensuring high classification accuracy, the feature set can reduce feature dimension and the amount of data calculation, improve operation efficiency and save storage space. Experiments show that the feature selection method proposed in this paper is feasible and more suitable for extracting feature sets in the process of somatic cell classification. [ABSTRACT FROM AUTHOR]