神经元干细胞序列图像中分裂子细胞的精确分割算法

Precise Segmentation of Cleavage of the Neuron Stem Cells in Time Lapse Image Sequences

针对神经元干细胞序列图像中存在的细胞分裂现象,为了实现对于分裂子细胞的特征提取、识别和跟踪,对于分裂出的微小目标——子细胞,提出了一种能够精确保留其形状特征的分割算法。采用了基于最大熵的模糊阈值分割方法,应用遗传算法确定最大模糊熵准则下的最优参数。利用加权距离变换、区域标注和形态学运算,实现了对分割结果中的干扰区域的吞没,以及对于分割图中欠分割目标的分离。仿真结果通过与硬分割比较,表明该算法在序列图像的追踪处理中对于需要特征提取的特定目标,能够实现最大限度保留该目标形状特征的精确分割。

To study the cleavage of neuron stem cells in time lapse image sequences and realize their features abstraction, identification and tracking, a precise segmentation algorithm that can preserve the shape of division cells is presented in this paper. The fuzzy threshold segmentation is based on Zadth＇s maximum entropy. The optimal parameters of the maximum fuzzy entropy are decided by genetic algorithm. Region merging and splitting of the under-segmentation objects of the result of fuzzy segmentation are realized by weighted distance transform, region labeling and some operations on morphology. By comparison with some results of fuzzy and hard segmentation, this algorithm can implement the precise segmentation that is necessary for some specified objects in automatic identification and tracking of neuron stem cells.