Immune Cell Image Segmentation Based on Mathematical Morphology
基于数学形态学的免疫细胞图象分割

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.