基于形态学和机器学习的固体核径迹图像优化识别算法

doi:10.13491/j.issn.1004-714X.2022.03.006

中国辐射卫生

2022, Vol. 31

Issue (3): 290-295 DOI: 10.13491/j.issn.1004-714X.2022.03.006

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基于形态学和机器学习的固体核径迹图像优化识别算法

张子扬¹, 范胜男¹, 李梦雪¹, 周文珊², 邓君¹

1. 中国疾病预防控制中心辐射防护与核安全医学所, 北京 100088;
2. 湖北省疾病预防控制中心, 湖北武汉 430079

Optimal recognition algorithm for solid nuclear track images based on morphology and machine learning

ZHANG Ziyang¹, FAN Shengnan¹, LI Mengxue¹, ZHOU Wenshan², DENG Jun¹

1. National Institute for Radiological Protection, China Center for Disease Control and Prevention, Beijing 100088 China;
2. Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079 China

摘要
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摘要目的基于机器学习方法，提出一种固体核径迹图像的计算机识别算法，实现核径迹的自动、快速和准确识别，提高固体径迹图像分析效率。方法首先利用形态学方法扫描143张含有径迹的图像，确定疑似径迹位置并截取1 250张素材图。选取素材的50%为训练集、30%为验证集，训练机器学习模型。另选素材的20%为测试集，测试模型识别效果。算法代码基于MATLAB软件编写并训练。结果建立的固体径迹识别算法识别能力较强，测试集识别准确度可达84.8%。算法构建的机器学习模型程序能跟随训练数据量的投入不断进化，准确度进一步提升。结论本算法在图像形态学基础上结合机器学习对径迹识别算法进行了研究，较好地实现固体径迹的自动识别。未来将加大模型的数据投入，优化算法，提高识别准确度，以期为图像径迹自动识别提供更精确高效的方法。

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	张子扬
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	邓君

关键词 ：固体核径迹, 图像识别, 机器学习

收稿日期: 2021-12-26

PACS:

TL815

通讯作者: 邓君，E-mail：dengjun@nirp.chinacdc.cn E-mail: dengjun@nirp.chinacdc.cn

作者简介: 张子扬（1995—），男，河南人，研究实习员，从事辐射剂量与图像分析工作。E-mail：zhangziyang@nirp.chinacdc.cn

引用本文:

张子扬, 范胜男, 李梦雪, 周文珊, 邓君. 基于形态学和机器学习的固体核径迹图像优化识别算法[J]. 中国辐射卫生, 2022, 31(3): 290-295.
ZHANG Ziyang, FAN Shengnan, LI Mengxue, ZHOU Wenshan, DENG Jun. Optimal recognition algorithm for solid nuclear track images based on morphology and machine learning. Chinese Journal of Radiological Health, 2022, 31(3): 290-295.

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