A comparative evaluation of segmentation methods for dendritic cells identification from microscopic images

Public health is one of the major concerns at the world level. Toxicology is an extremely challenging issue regarding that toxic substances are harmful to human health. In fact, toxicology studies are indispensable to evaluate the toxic effects on humans. Currently, a new evaluation technique based on the analysis of dendritic cells in vitro has been found by researchers. This analysis that remains purely visual is a tedious process, subjective and time-consuming. Therefore, an assessment tool for the analysis of toxic impact using automatic processing techniques by image analysis can be greatly useful for expert biologists. The foremost aim of this paper is to propose two segmentation approaches of dendritic cells from microscopic images and to present a comparative evaluation of them. The first suggested algorithm is based on automatic thresholding and mathematical morphology, while the second one combines the k-means clustering, thresholding and mathematical morphology based operations. For validation purposes, four performance measures were used to assess the obtained segmentation results with the ground truth images, elaborated by expert. Quantitatively, results show that the two suggested algorithms are efficient in identifying dendritic cells from 26 gray-scale images with a segmentation accuracy of 99.00 % and 99.37%, respectively.