| Fan Shengnan,Deng Jun,Zhang Ziyang,et al.Preliminary study on dicentric chromosome identification algorithm based on artificial intelligence technology[J].Chinese Journal of Radiological Medicine and Protection,2022,42(5):343-347 |
| Preliminary study on dicentric chromosome identification algorithm based on artificial intelligence technology |
| Received:December 13, 2021 |
| DOI：10.3760/cma.j.cn112271-20211213-00480 |
| KeyWords:Dicentric chromosome Biodosimetry Artificial intelligence Fuzzy logic Automatic Identification |
| FundProject: |
| Author Name | Affiliation | E-mail | | Fan Shengnan | National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Deng Jun | National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | dengjun@nirp.chinacdc.cn | | Zhang Ziyang | National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Ruan Jianlei | National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Pan Yan | National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | | | Sun Quanfu | National Institute for Radiological Protection, Chinese Center for Disease Control and Prevention, Beijing 100088, China | |
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| Abstract:: |
| Objective To explore artificial intelligence technology and propose an algorithm for automatic identification of dicentric chromosomes to realize fast and high-throughput biodosimetry. In order to solve the time-consuming and laborious problem of manual analysis of dicentric chromosomes.Methods Combining artificial intelligence technology and image processing technology, based on MATLAB software, algorithms like image preprocessing, threshold segmentation algorithm, binarization processing, area identification algorithm, convolutional neural network algorithm and double centripetal recognition algorithm were applied. A fuzzy membership function was defined to describe the degree of each chromosome belonging to a dicentric chromosome, and the discrimination threshold was set to realize the automatic identification of dicentric chromosomes.Results Through the test on 1 471 chromosome images, compared with manual recognition, the detection rate of dicentric chromosomes cells of this algorithm reached 70.7%.Conclusions This algorithm method carries out a preliminary study on the automatic identification of dicentric chromosomes with good result. |
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