The immune cell image must be exactly segmented first in order to realize cell's parameter measurement and get a right analysis conclusion. In this paper

an effective immune cell image segmentation algorithm based on mathematical morphology is presented. In order to get better segmentation results in addition to the morphology based watershed growth algorithm the histogram potential information is involved

which means

the image spectral information is combined with spacial information. How to get the exact segmentation result is a major issue for immune cell analysis. Watershed growth combines the basic idea of region growth and edge detection and has the advantages of both the method. Using the method

single pixel width

connected and closed object boundary can be detected automatically

which is necessary for cell image segmentation. But obtaining an effective and credible marker is a crucial step of watershed segmentation. By involving the histogram potential function

the markers suitable for watershed segmentation can be clearly improved. By this method

a segmentation result quite consistent with human vision can be gotten

and both the segmentation speed and repeatability meet the medical clinic need

and the analysis conclusion accords with clinic diagnoses.