巨核细胞图像分割方法的研究

随着现代医学研究的飞速发展，显微图像定量分析已经广泛应用到临床诊断、病理分析、癌变分级分类等越来越多的医学领域之内。巨核细胞是骨髓切片图像中个体较大的细胞，形状不规则，对其进行有效提取和分割对骨髓成份统计分析、血液疾病诊断等都有重要意义。该文根据数学形态学的知识，利用直方图势函数提取标记点，并将这些标记点作为种子点来对梯度图进行Watershed变换，进而实现骨髓切片中巨核细胞的有效分割。该方法是一种谱信息与空间信息相结合的分割方法，对分割结果与传统算法的对比分析表明，改进后的算法在分割的完整性和一致性上具有更好的效果。

Research on Image Segmentation of Megalinker

With the fast development of modern medicine, the quantitative analysis of micrograph has been widely used in the fields of clinical diagnoses, pathology analysis, cancer classification, etc. There is megalinkers with enormous volume and irregular shapes on bone marrow slice. It counts a great deal of analysis of the components in bone marrow, diagnose of blood illnesses and etc by effective detection and segmentation. An effective image segmentation algorithm of megalinker based on mathematical morphology is presented in this paper. In order to get better segmentation results, histogram potential, which contains image spectral information, is combined with watershed growth algorithm. Compared with traditional arithmetic, the result shows that the improved method is better in keeping integrality and consistency of image, image segmentation, threshold edge detection.