基于圆形约束快速水平集的原生质体细胞分割

原生质体细胞的显微图像具有边界模糊、内部分布不均匀的特点,利用传统分割方法较难取得理想分割效果.针对原生质体细胞的圆形特点,在快速水平集分割算法中加入圆形先验知识,提出一种新的基于圆形约束的快速水平集模型.为解决多个原生质体细胞分割问题,首先对图像进行预分割,然后利用多个水平集表示的圆形约束快速模型进行再分割.对传统快速水平集进行改进得到一种基于直方图统计的快速水平集模型,利用该模型进行预分割可以取得较好的效果.对多个不粘连细胞和多个粘连细胞,分别采用八链码跟踪法和随机霍夫圆检测法对预分割后的目标区域进行分裂.实验结果表明,本文快速水平集算法可以有效地解决单个及多个原生质体细胞分割问题.

Protoplasm somatic cells segmentation based on circle dependent fast level-set segmentation

The microscopic image of protoplasm somatic cells typically has blurred boundaries and inhomogeneous object regions.Therefore, it is difficult to segment the cells using traditional methods. First, because the protoplasm cell is round, the circular prior knowledge is added to the fast level set method and then a new circle dependent fast level set segmentation method is proposed. Then, to solve the problem of segmentation for multi protoplasm somatic cells, the pre-segmentation is used before using the fast multi-level set method based on circle information. Furthermore, a new fast level-set method, which is based on the histogram, is proposed to get a better result for the pre-segmentation. The eight-chain code tracking method and the randomized Hough transform for circle detection are used to divide object region resulting from pre-segmentation respectively for multi-cells and multi-clustered cells. Finally, experimental results show that the new method proposed in this paper can deal well with the problem of segmentation for protoplasm somatic cells.