基于数学形态学的免疫细胞图象分割

为了实现对免疫细胞图象的分析，首先要对该种图象进行正确分割，针对这一要求，提出了一种有效的免疫细胞图象分割方法，该方法是根据数学形态学的知识，利用直方图势池数来提取标记点，并将这些标记点作为种子点来对梯度图进行Watershed变换，进而实现了细胞图象的分割。该方法是一种谱信息与空间信息相结合的分割方法，根据实验结果和分析可见，该方法不仅解决了细胞在参数测量前的精确分割问题，同时，为水域分割的关键步骤－种子点的选取找到了一种有效而可靠的方法，实践表明，分割的结果与 目视感受相一致，且其分割速度及可重复性都达到了医学临床的要求。

Immune Cell Image Segmentation Based on Mathematical Morphology

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.