Classification of leukocyte images using K-Means Clustering based on geometry features

Information about counts and percentages of each type of leukocytes in blood is much needed to diagnose patients' illness. To gain that information, some functional enhancements had been applied to the optical microscopes so that they could produce digital images. Output images from these engineered microscopes were then extracted to get feature values from each image. These feature values then became input sets to the method of K-Means Clustering so that the leukocyte images could be classified according to each own cluster. Generally, the leukocyte classification process is conducted through four phases, which are image pre-processing, leukocyte segmentation, feature extraction, and leukocyte classification. Leukocyte types which were classified in this research were neutrophil, lymphocyte, monocyte, and eosinophil. Experiments were conducted using five kinds of features, which are normalized area, circularity, eccentricity, normalized parameter, and solidity, and by varying their types and their significant influences. The purpose of these trials were to determine which feature types would result in the highest value of accuracy and the effects of adding these respective features to the resulted accuracy. Based on the conducted classification results, it was found that the highest accuracy value was reached by circularity feature, which was 67%, meanwhile the lowest accuracy value was produced by the eccentricity feature, which was 43%. In this research, it was concluded that the accuracy value is ultimately determined by selecting the correct feature type rather than adding more features.