基于稀疏轮廓点模型的彩色重叠细胞图像分割

关涛; 周东翔; 樊玮虹; 刘云辉

doi:10.7544/issn1000-1239.2015.20140324

基于稀疏轮廓点模型的彩色重叠细胞图像分割

¹(国防科学技术大学电子科学与工程学院长沙 410073)
²(香港中文大学机械与自动化工程学系香港 999077) (gt_mike2003@126.com)

基金项目: 国家自然科学基金项目(61375032)

详细信息

中图分类号: TP391
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出版历程
- 发布日期: 2015-06-30

Segmentation of Color Overlapping Cells Image Based on Sparse Contour Point Model

¹(College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073)
²(Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 999077)

摘要

摘要: 根据对细胞轮廓结构的分析，提出了描述细胞轮廓特征的稀疏轮廓点模型.该模型将细胞轮廓划分为强轮廓和弱轮廓2部分，并用一系列稀疏的轮廓点来近似表示细胞轮廓.在该模型的基础上，算法综合了彩色图像处理与灰度图像处理的方法，首先提取细胞与背景交界处的强轮廓作为基准轮廓.然后提出了一种环形动态轮廓搜索算法，沿着基准轮廓搜索位于重叠细胞区域的弱轮廓.经过轮廓搜索算法获得细胞的稀疏轮廓点组成初始轮廓，采用GVF(gradient vector flow) Snake模型进行细胞的精确分割.通过对2个细胞图像数据集的图像样本进行的分割实验验证了算法对于分割单细胞图像、同种颜色重叠细胞图像和不同颜色重叠细胞图像的准确性.
- 医学图像处理 /
- 重叠细胞图像 /
- 彩色图像分割 /
- 稀疏轮廓点模型 /
- GVF Snake模型
Abstract: Based on the analysis of cell contour structure, a sparse contour point model, which can describe the characteristics of the cell contour, is proposed in this paper. In the sparse contour point model, cell contour is divided into 2 parts, namely light contour and dark contour, respectively; and then the cell contour is approximately described as a set of sparse contour points. Based on this model, the color and grayscale image segmentation techniques are combined to locate the basic contour, which lies between the cell and the background. Then, a circular dynamic contour searching method is proposed to search for the dark contour that lies in the overlapping cell region along the basic contour. Contour points located by the searching method are arranged to construct the initial contour of the gradient vector flow (GVF) Snake model. Then the GVF Snake model is performed to obtain the final accurate segmentation result of the cell image. Various cell images containing single cell, overlapping cells of similar colors and overlapping cells of different colors have been tested to show the validity and effectiveness of the proposed method. The proposed techniques are useful for the development of automatic cervical cell image analysis systems.
- medical image processing /
- overlapping cell images /
- color image segmentation /
- sparse contour point model /
- gradient vector flow (GVF) Snake model

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