自适应H-minima的改进分水岭堆叠细胞分割方法

针对传统分水岭方法初始分割结果存在过分割问题，本文提出自适应 的改进堆叠细胞分割方法。该方法利用不同 值 变换抑制种子噪声；并以对应候选种子为中心，分别采用改进K-均值算法合并初始分割区域，产生候选分割结果； 然后，基于形状先验定义圆度指标FuzzyR,并将堆叠细胞平均圆度作为评价函数，自适应提取各堆叠区域最优 值，实现正确分割。实验结果证明，针对于人工合成和真实堆叠细胞图像，本算法均能有效抑制过分割、减少欠分割，分割性能显著提高。

Clustered cells segmentation using modified watershed method based adaptive H-minima

For addressing the issue of over-segmentation in the watershed initial segmentation, this paper proposed a clustered cells segmentation method based on adaptive H-minima transform in watershed framework. Using corresponding candidate seeds as clustering initial centers , which were reserved seeds after noise seeds suppression with different h value H-minima transform , this method firstly produced some candidate segmentation results through modified K-means region merging algorithms; Then, it adaptively chose the optimal h value and produced the optional segmentation result for each clustered cells region using maximal average roundness of candidate segmentation results as optimization objective. Experiments on a variety of synthetic and real microscopic cell images shown that the proposed method can efficiently restrain over-segmentation and decrease under-segmentation. This method yielded more accurate segmentation rate than the state-of-the-art watershed-based segmentation methods