Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 204-210.doi: 10.11896/jsjkx.201100085

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image

ZHANG Xiao-qing1, FANG Jian-sheng1, XIAO Zun-jie1, CHEN Bang2, Risa HIGASHITA3, CHEN Wan4, YUAN Jin4, LIU Jiang1,2   

  1. 1 Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong 518055,China
    2 Cixi Institute of Biomedical Engineering,Ningbo Institute of Materials Technology & Engineering,Chinese Academy of Sciences,Ningbo, Zhejiang 315201,China
    3 Tomey Corporation,Nagoya 4510051,Japan
    4 Zhongshan Ophthalmic Center,Sun Yat-sen University,Guangzhou 510060,China
  • Received:2020-11-12 Revised:2021-03-22 Online:2022-03-15 Published:2022-03-15
  • About author:ZHANG Xiao-qing,born in 1993,Ph.D candidate,is a student member of China Computer Federation.His main research interests include machine lear-ning and medical image processing.
    LIU Jiang,born in 1968,Ph.D,professor,Ph.D supervisor.His main research interests include medical image proces-sing and artificial intelligence.
  • Supported by:
    Guangdong Provincial Key Laboratory(2020B121201001) and Key areas of Guangdong Province Colleges and Universities Special Funding(2020ZDZX3043).

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Abstract: Cataract is a main ocular disease for visual impairment and blindness.Anterior segment optical coherence tomography (AS-OCT) technique has the characteristics of non-invasiveness,high resolution,rapid inspection,and objective quantitative measurement.AS-OCT images have been widely used for the diagnosis of ocular diseases in clinical ophthalmology.Inthecurrent,it is lack of the research on classification methods of nuclear cataract based on AS-OCT images.To this end,this paper proposes a nuclear cataract classification method based on AS-OCT images.First,the nucleus region of the lens is extracted from AS-OCT images using a combination of adaptive threshold method,edge detection Canny algorithm and manual correction pattern.Then,eighteen pixel features are extracted based on image intensity and histogram feature statistical methods,and the Pearson correlation coefficient method is used to analyze the correlation between the extracted pixel features and the severity of nuclear cataract.Finally,the random forest algorithm is used to build a classification model for getting nuclear cataract classification results.Experimental results on an AS-OCT image dataset show that the proposed method achieves the accuracy and recall with 75.53% and 74.04% respectively.Therefore,the proposed method has the potential as a quantitative analysis reference tool for the clinical diagnosis of nuclear cataract.

Key words: Anterior segment optical coherence tomography, Cataract, Lens, Machine learning, Nuclear region, Random forest

CLC Number: 

  • TP391
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