细胞图像分割是医学图像处理领域的研究热点之一。传统的细胞图像分割算法多是基于灰度图像的分割，图像中的颜色信息利用得不充分。在深入分析细胞图像颜色特征的基础上，提出了基于色差向量场分析细胞图像颜色变化规律的方法，相比于经典的彩色空间（HSV、YIQ、CIEL*a*b*），这种方法更能够突出图像中的主体细胞与非细胞区域的差异，而且针对大量图像的普适性更好。然后基于细胞图像的色差向量场，提出了一种循环匹配的分割方法，同时采用色差强度对分割结果进行了进一步的修正。通过对实际采集的彩色细胞图像样本的分割实验验证，该算法比RGVF Snake算法的分割结果更可靠，准确率可以达到95.2%，而且能够实现不同颜色重叠细胞图像的分割。

Cell image segmentation is one of the hot topics in medical image processing. Most of the classical algorithms for cell image segmentation are based on grayscale images, which results in loss of color information in images. Based on analyzing the characteristics of the color cell images, we present a color difference vector field to model the color feature of cell images. In the color difference vector field, the difference between cell region and non-cell region is more distinct compared with other classical color spaces, such as HSV, YIQ and CIEL*a*b* spaces. Furthermore, this method is more robust for a large number of cell images. Based on the color difference vector field, a sequential match method is proposed for segmentation of cell images. In order to obtain more accurate results, the color difference strength is used to refine the segmentation results. Various color cell images containing overlapped cells have been tested to show the validity and effectiveness of the proposed method. The accuracy of the proposed method reaches 95.2%, which is higher than that of the RGVF Snake method.