基于中心定位算法的细胞双光子显微图像分割

细胞双光子显微图像中存在边界模糊不清、噪声严重以及背景复杂等问题,使用现有方法提取边缘的结果不够理想。为此,提出了一种有效定位细胞核、提取边缘的新方法。采取由粗到精的分割策略,逐步提取出细胞核的边缘:首先,用C-均值聚类算法对图像进行分类,大致分出细胞核、细胞质和细胞间质三部分;然后,对分类结果的Canny边缘图进行圆形度计算,准确定位细胞核;最后,采用改进的水平集方法提取出细胞核的边缘。实验结果表明:对于背景复杂、干扰较多的细胞双光子显微图像,该方法可以精确地定位细胞核,所提取的细胞核边缘也较为精确。

Segmentation of cell two-photon microscopic image based on center location algorithm

Complex background, critical noise and fuzzy boundary made the performance of the available cell image segmentation methods disappointing. Thus, a new method that can locate and detect nucleus effectively was proposed in this paper. A coarse-to-fine segmentation strategy was adopted to extract the edge of nucleus gradually. First, by using C-means clustering algorithm, the image was divided to three parts: nucleus, cytoplasm and cell intercellular substance. Second, the center of cell was located by calculating the circularity of Canny edge image. Finally, a reformed level set evolution was introduced to extract the edge of nucleus. The experimental results show that, nucleus can be located accurately; even if the cell image has a complex background and is disturbed by much stuff. Moreover, the edge of nucleus extracted by this method has a higher accuracy.