Microscopic Cell Detection Based on Multiple Cell Image Segmentations and Fusion Algorithms

Automatic cell segmentation in phase contrast microscopy images play a very important role in the study the behavior of lymphocytes, such as cell motility, cell deformation, and cell population dynamics etc. In this paper, we have developed a set of algorithms for the microscopy image cell segmentation, in which three pairs of edge detection (Sobel, Prewitt and Laplace) based cell segmentation algorithms are developed in parallel to increase the probability of cell detection. Then, an hierarchical model is proposed and used in decision fusion that combine the three pair of detection results to increase the probability of final cell detection. After that, a false removal algorithm is proposed to remove false detections that may occur in the fusion process. The distance and watershed transforms have also been used to separate the connected cells. Experimental results have proved that these algorithms are pretty robust to variable microscopy image data, and variable cell densities, and with the proposed fusion and false removal algorithms, the cell detection rate has increased significantly to above 97% with the false detection rate about 7%.