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作 者:白华[1,2] 张凤凤 张诚 赵军发[1,2] 严淑琳 张建中[3,4] 韩之波
机构地区:[1]天津工业大学电子与信息工程学院,天津300387 [2]天津市光电检测技术与系统重点实验室,天津300387 [3]细胞产品国家工程研究中心,天津300457 [4]天津昂赛细胞基因工程有限公司,天津300457
出 处:《中国细胞生物学学报》2017年第11期1444-1451,共8页Chinese Journal of Cell Biology
基 金:国家自然科学基金(批准号:61201106;61307094);天津市应用基础与前沿技术研究计划项目(批准号:14JCQNJC01800);天津市光电传感器与传感网络技术重点实验室开放项目资助的课题~~
摘 要:细胞生长汇合度是细胞体外培养环节中的重要参数。目前的细胞汇合度评估方法主要依靠人工,其结果效率低、精度差,且主观性强。图像处理技术具有快速、准确、自动化程度高等优点,在生物医学领域应用日益广泛,用来分析细胞汇合度可以在很大程度上提高检测效率和客观准确性。该文提出的贴壁细胞生长汇合度自动分析算法首先运用灰度形态学的高低帽变换组合与背景减除相结合的预处理增强图像对比度,然后运用改进的K-Means聚类算法将前景和背景进行粗略地区分,最后通过面积滤波和二值形态学的算法组合对图像进行后处理,实现前景和背景的良好区分并计算细胞汇合度。实验结果显示,该算法准确性较高,可有效替代传统人工方法对细胞汇合度进行自动检测。The cell confluence is an important parameter in cell culture in vitro. Currently, the assessment of this parameter is usually carried out by human, and thus there are many defects such as inefficiency, poor precision and low reliability. Image processing technology is a powerful tool that has been widely used in biomedical field owing to its characteristics of high-speed, high-veracity and high-automation. Therefore, the use of this technique for cell confluence analysis can greatly improve the detection efficiency and objective accuracy. An image processing method for automatic analysis of adherent cell confluence is presented in this paper. Firstly, the cell microscopic images were preprocessed by the top-bottom hat of gray morphology and background subtraction. Secondly, an improved K-Means clustering algorithm was used to coarsely distinguish between foreground and background. Then, an algorithm combining area filtering and binary morphology was adopted to optimize segmentation effect, which could obtain a good segmentation between cellular area and background, and finally calculated the cell confluence. Experimental results showed that the algorithm had a high accuracy and couldeffectively replace the traditional artificial method to automatically detect the cell confluence.
关 键 词:间充质干细胞 细胞汇合度 数学形态学 改进的K-Means算法 图像处理
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