Microscopic cell segmentation by parallel detection and fusion algorithm

Automatic cell segmentation and tracking in optical microscope images plays a very important role in the study the behaviour of lymphocytes. The variable image contrasts, and especially variable cell densities are major factors to affect the successful cell detection rates. In this paper, two inner and outer cell contours edge detection based cell segmentation algorithms are proposed and used in parallel. Then a detection fusion algorithm is proposed to combine the two detection results and increase the probability of cell detection. Experimental results are used to demonstrate that these algorithms are robust to variations in both image contrast and cell densities. We show that the proposed fusion algorithm can increase cell detection rate significantly to above 90% with the false detection rate about 5%.