Supervised leukocyte segmentation in tissue images using multi-objective optimization technique


Automated leukocytes segmentation in skin section images can be utilized by various researchers in animal experimentation for testing anti-inflammatory drugs and estimating dermatotoxicity of various toxic agents. However, complex morphological structure of skin section degrades the performance of leukocytes segmentation due to the extraction of vast number of artifacts/noise along with leukocytes. Rare works have been done to reduce such artifacts. Therefore, in this paper, a supervised methodology for leukocytes segmentation from the images of inflamed mice skin sections is introduced. The method is based on threshold based binary classifier to reduce the artifacts. The optimum values of thresholds are calculated using multi-objective optimization technique, non-dominated sorting genetic algorithm-II (NSGA-II) and receiver operating characteristic (ROC) curve. The experimental results confirm that the proposed method is prompt and precise to segment the leukocytes in highly variable images.