基于眼前节相干光断层扫描成像的核性白内障分类算法

doi:10.11896/jsjkx.201100085

计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 204-210.doi: 10.11896/jsjkx.201100085

• 计算机图形学&多媒体 • 上一篇下一篇

基于眼前节相干光断层扫描成像的核性白内障分类算法

章晓庆¹, 方建生¹, 肖尊杰¹, 陈浜², RisaHIGASHITA³, 陈婉⁴, 袁进⁴, 刘江^1,2

1 南方科技大学计算机科学与工程系广东深圳518055
2 中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所浙江宁波315201
3 Tomey公司日本名古屋4510051
4 中山大学中山眼科中心广州510060

收稿日期:2020-11-12 修回日期:2021-03-22 出版日期:2022-03-15 发布日期:2022-03-15
通讯作者: 刘江(liuj@sustech.edu.cn)
作者简介:(11930927@mail.sustech.edu.cn)
基金资助:
广东省重点实验室项目(2020B121201001); 广东省普通高校重点领域专项基金(2020ZDZX3043)

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 Jiang^1,2

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).

摘要/Abstract

摘要： 白内障是导致视觉损害和致盲的主要眼病,眼前节光学相干断层成像技术(Anterior Segment Optical Coherence Tomography,AS-OCT)具有非接触、高分辨率、检查快速、客观定量化测量等特点,在临床上已经被广泛应用于眼病的诊断。目前缺乏基于眼前节OCT图像的核性白内障分类研究工作,为此提出了一种基于眼前节OCT图像的核性白内障分类算法。首先,利用自适应阈值方法、边缘检测 Canny 算法和手工校正相结合的方式从眼前节OCT图像中提取晶状体的核性区域;然后,基于图像强度和直方图的特征统计方法提取18个像素特征,并应用皮尔逊相关系数方法分析提取像素特征与核性白内障严重程度之间的相关性;最后,利用随机森林算法构建分类模型,从而得到核性白内障分类结果。在一个眼前节OCT图像数据集上的实验结果表明,所提算法对核性白内障严重程度的分类准确率和召回率分别达到了75.53%和74.04%,具有作为核性白内障临床诊断的定量分析参考工具的潜力。

关键词: 白内障, 核性区域, 机器学习, 晶状体, 随机森林, 眼前节光学相干断层成像

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

中图分类号:

TP391

章晓庆, 方建生, 肖尊杰, 陈浜, RisaHIGASHITA, 陈婉, 袁进, 刘江. 基于眼前节相干光断层扫描成像的核性白内障分类算法[J]. 计算机科学, 2022, 49(3): 204-210. https://doi.org/10.11896/jsjkx.201100085

ZHANG Xiao-qing, FANG Jian-sheng, XIAO Zun-jie, CHEN Bang, Risa HIGASHITA, CHEN Wan, YUAN Jin, LIU Jiang. Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image[J]. Computer Science, 2022, 49(3): 204-210. https://doi.org/10.11896/jsjkx.201100085

参考文献

[1]GALI H E,SELLA R,AFSHARI N A.Cataract grading systems:a review of past and present[J].Current Opinion in Ophthalmology,2019,30(1):13-18.
[2]NIE M,QU C.Factors affecting cortical and nuclear cataract[J].Practical Journal of Clinical Medicine,2019,16 (4):1-5.
[3]CHYLACKL T,WOLFEJ K,SINGERD M,et al.The LensOpacities Classification System III[J].Archives of Ophthalmo-logy,1993,111(6):831-836.
[4]LI H,LIM J H,LIU J,et al.An automatic diagnosis system of nuclear cataract using slit-lamp images[C]//Conference of the IEEE Engineering in Medicine and Biology Society.2009:3693-3696.
[5]XU Y,GAO X,LIN S,et al.Automatic Grading of Nuclear Ca-taracts from Slit-Lamp Lens Images Using Group Sparsity Regression[C]//International Conference on Medical Image Computing & Computer-assisted Intervention.Berlin:Springer,2013.
[6]ANG M,MEHTA J S.Artificial intelligence-assisted telemedi-cine platform for cataract screening and management:a potential model of care for global eye health[J].British Journal of Ophthalmology,2019,103(11):1537-1538.
[7]XU C,ZHU X,HE W,et al.Fully Deep Learning for Slit-Lamp Photo Based Nuclear Cataract Grading[C]//International Conference on Medical Image Computing & Computer-assisted Intervention (MICCAI).2019.
[8]CAIXINHA M,AMARO J,SANTOS M,et al.In-Vivo Automatic Nuclear Cataract Detection and Classification in an Animal Model by Ultrasounds[J].IEEE Transactions on Biomedi-cal Engineering,2016,63(11):2326-2335.
[9]OZGOKCE M,BATUR M,ALPASLAN M,et al.A compara-tive evaluation of cataract classifications based on shear-wave elastography and B-mode ultrasound findings[J].Journal of Ultrasound,2019,22(4):447-452.
[10]WAN Y,WANG Y,ZHAO L,et al.Correlation among LensOpacities Classification System III grading,the 25-item National Eye Institute Visual Functioning Questionnaire,and Visual Function Index-14 for age-related cataract assessment[J].International Ophthalmology,2020,40(7):1831-1839.
[11]FU H,XU Y,LIN S,et al.Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT[J].IEEE Transactions on Medical Imaging,2017,36(9):1930-1938.
[12]WONG A L,LEUNG K S,WEINREB R N,et al.Quantitative assessment of lens opacities with anterior segment optical cohe-rence tomography[J].British Journal of Ophthalmology,2009,93(1):61-65.
[13]CHEN D,LI Z,HUANG J,et al.Lens nuclear opacity quantitation with long-range swept-source optical coherence tomography:correlation to LOCS III and a Scheimpflug imaging-based grading system[J/OL].British Journal of Ophthalmology,2018.https://www.researchgate.net/publication/328258927_Lens_nuclear_opacity_quantitation_with_long-range_swept-source_optical_coherence_tomography_Correlation_to_LOCS_III_and_a_Scheimpflug_imaging-based_grading_system.
[14]ZWANENBURG A,ABDALAH M A,APTE A,et al.Imagebiomarker standardisation initiative[J].Radiotherapy & Onco-logy,2018,127:S543-S544.
[15]DEHGHANI A,GLATARD T,SHIHAB E.Subject cross validation in human activity recognition[C]//ACM Knowledge Discovery and Data Mining (KDD).2019.
[16]LI H Q,LIM H Y,LIU J,et al.Feature analysis in slit-lampimage for nuclear cataract diagnosis[C/OL]//International Conference on Biomedical Engineering & Informatics.IEEE,2010.https://www.researchgate.net/publication/241175959_Feature_Analysis_in_Slit-lamp_Image_for_Nuclear_Cataract_Diagnosis.
[17]LI H,LIM J H,LIU J,et al.Towards automatic grading of nuclear cataract[J/OL].Conf. Proc. IEEE Eng. Med. Biol. Soc.,2006.http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4353454.
[18]HUANG W,CHAN K L,LI H Q,et al.A Computer Assisted Method for Nuclear Cataract Grading From Slit-Lamp Images Using Ranking[J].IEEE Transactions on Medical Imaging,2010,30(1):94-107.
[19]LI J,ZHANG L L,ZHANG L,et al.Cataract recognition and classification based on deep learning[J].Journal of the Second Military Medical University,2018,39(8):878-885.
[20]XU X,ZHANG L,LI J,et al.A Hybrid Global-Local Represen-tation CNN Model for Automatic Cataract Grading[J].IEEE Journal of Biomedical and Health Informatics,2020,24(2):556-567.
[21]FU H,BASKARAN M,XU Y,et al.A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images[J].American Journal of Ophthalmology,2019,203:37-45.
[22]CAO G,ZHAO W,HIGASHITA R,et al.An Efficient LensStructures Segmentation Method on AS-OCT Images[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE,2020.
[23]ZHANG X,XIAO Z,RISA H,et al.A Novel Deep LearningMethod for Nuclear Cataract Classification Based on Anterior Segment Optical Coherence Tomography Images[C]//2020 IEEE International Conference on Systems,Man,and Cyberne-tics(IEEE SMC).IEEE,2020.

相关文章 15

[1]	冷典典, 杜鹏, 陈建廷, 向阳. 面向自动化集装箱码头的AGV行驶时间估计 Automated Container Terminal Oriented Travel Time Estimation of AGV 计算机科学, 2022, 49(9): 208-214. https://doi.org/10.11896/jsjkx.210700028
[2]	宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[3]	何强, 尹震宇, 黄敏, 王兴伟, 王源田, 崔硕, 赵勇. 基于大数据的进化网络影响力分析研究综述 Survey of Influence Analysis of Evolutionary Network Based on Big Data 计算机科学, 2022, 49(8): 1-11. https://doi.org/10.11896/jsjkx.210700240
[4]	李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094
[5]	张光华, 高天娇, 陈振国, 于乃文. 基于N-Gram静态分析技术的恶意软件分类研究 Study on Malware Classification Based on N-Gram Static Analysis Technology 计算机科学, 2022, 49(8): 336-343. https://doi.org/10.11896/jsjkx.210900203
[6]	陈明鑫, 张钧波, 李天瑞. 联邦学习攻防研究综述 Survey on Attacks and Defenses in Federated Learning 计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079
[7]	高振卓, 王志海, 刘海洋. 嵌入典型时间序列特征的随机Shapelet森林算法 Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features 计算机科学, 2022, 49(7): 40-49. https://doi.org/10.11896/jsjkx.210700226
[8]	胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092
[9]	李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述 Survey on Bayesian Optimization Methods for Hyper-parameter Tuning 计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208
[10]	赵璐, 袁立明, 郝琨. 多示例学习算法综述 Review of Multi-instance Learning Algorithms 计算机科学, 2022, 49(6A): 93-99. https://doi.org/10.11896/jsjkx.210500047
[11]	王飞, 黄涛, 杨晔. 基于Stacking多模型融合的IGBT器件寿命的机器学习预测算法研究 Study on Machine Learning Algorithms for Life Prediction of IGBT Devices Based on Stacking Multi-model Fusion 计算机科学, 2022, 49(6A): 784-789. https://doi.org/10.11896/jsjkx.210400030
[12]	阙华坤, 冯小峰, 刘盼龙, 郭文翀, 李健, 曾伟良, 范竞敏. Grassberger熵随机森林在窃电行为检测的应用 Application of Grassberger Entropy Random Forest to Power-stealing Behavior Detection 计算机科学, 2022, 49(6A): 790-794. https://doi.org/10.11896/jsjkx.210800032
[13]	肖治鸿, 韩晔彤, 邹永攀. 基于多源数据和逻辑推理的行为识别技术研究 Study on Activity Recognition Based on Multi-source Data and Logical Reasoning 计算机科学, 2022, 49(6A): 397-406. https://doi.org/10.11896/jsjkx.210300270
[14]	姚烨, 朱怡安, 钱亮, 贾耀, 张黎翔, 刘瑞亮. 一种基于异质模型融合的 Android 终端恶意软件检测方法 Android Malware Detection Method Based on Heterogeneous Model Fusion 计算机科学, 2022, 49(6A): 508-515. https://doi.org/10.11896/jsjkx.210700103
[15]	王文强, 贾星星, 李朋. 自适应的集成定序算法 Adaptive Ensemble Ordering Algorithm 计算机科学, 2022, 49(6A): 242-246. https://doi.org/10.11896/jsjkx.210200108

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于眼前节相干光断层扫描成像的核性白内障分类算法

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

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0