فصل نامه علمی پژوهشی مهندسی پزشکی زیستی

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ارائه‌ی روشی جدید برای تشخیص خودکار بیماری دژنراسیون وابسته ‌به ‌سن ماکولا با استفاده از تصاویر مقطع‌نگاری همدوسی اپتیکی

نوع مقاله : مقاله کامل پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی برق، گروه مخابرات، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران

2 استادیار، گروه الکترونیک، دانشکده‌ی مهندسی برق و کامپیوتر، دانشگاه سمنان، سمنان، ایران

چکیده

بیماری‌های مرتبط با شبکیه و ماکولای چشم باعث از دست رفتن دائمی بینایی یا کاهش بسیار زیاد دید در افراد شده و موجب پایین آمدن کیفیت زندگی و ایجاد مشکلات فراوان در زندگی روزمره‌ی بیماران می­شود. از این‌رو شناسایی به هنگام و درست این بیماری­ها و اختلالات از اهمیت بسیار زیادی برخوردار است. روش تصویربرداری مقطع­نگاری همدوسی اپتیکی دقت بالایی در تصویربرداری داشته و هم‌چنین اطلاعات عمقی از شبکیه ارائه می­دهد. این روش تصویربرداری کمک بسیار زیادی به شناسایی دقیق بیماری­های مرتبط با ماکولا کرده است. یکی از شایع­ترین بیماری­های شبکیه‌ی چشم، بیماری دژنراسیون وابسته به سن ماکولا است. هدف از انجام این پژوهش طراحی و پیاده‌سازی سیستمی قابل اعتماد و سریع است که بتواند بیماری دژنراسیون وابسته به سن ماکولا را با استفاده از پردازش تصاویر مقطع‌نگاری همدوسی اپتیکی به خوبی و با دقت و سرعت بالا شناسایی کند. در این مطالعه از روش­های هیستوگرام گرادیان­های جهت­دار و تحلیل مولفه‌ی اصلی برای استخراج ویژگی­ها و از روش طبقه­بندی گروهی AdaBoost جهت طبقه‌بندی داده­ها استفاده شده است. پایگاه داده‌ی مورد استفاده در این مقاله شامل 269 فرد بیمار و 115 فرد سالم است. هر سه شاخص دقت، حساسیت و خاصیت سیستم پیاده­سازی شده برابر با 100% اندازه‌گیری شده است.

کلیدواژه‌ها

عنوان مقاله [English]

A New Method for Automatic Diagnosis of Age-Related Macular Degeneration using Optical Coherence Tomography Images

نویسندگان [English]

  • Amir Babaoghli 1
  • Hadi Soltanizadeh 2

1 M.Sc. Student, Telecommunications Department, Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran

2 Assistant Professor, Electronic Department, Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran

چکیده [English]

Diseases associated with the retina and macula of the eye, causing permanent loss of vision or a great deal of loss of vision in people, leads to a decrease in the quality of life and a lot of problems in daily life. For this reason, the timely and correct identification of these diseases and disorders has become important. The optical coherence tomography imaging method provides high precision in imaging and good information about the depth of the retina. This imaging technique is a great help in the accurate identification of macular-related diseases. Age-related macular degeneration is one of the most common retinal diseases. The purpose of this study is to design and implement a system that is reliable, fast and can detect the age-related macular degeneration by using optical coherence tomography image processing accurately and quickly. In these studies, histograms of orientational gradients and principal component analysis for extraction of features and AdaBoost ensemble classification method have been used to classify the data. The database used includes 269 patients and 115 healthy people. All three indicators of accuracy, sensitivity and specificity of the implemented system were measured at 100%. 

کلیدواژه‌ها [English]

  • image processing
  • Optical coherence tomography
  • Age related macular degeneration
  • Histogram of oriented gradients
  • Principal Component Analysis
  • Ensemble Classification
  • AdaBoost
مراجع
[1]   Webb and G. C. Kagadis, "Introduction to Biomedical Imaging," Medical Physics, vol. 30, no. 8, pp. 2267-2267, 2003.
[2]   L. M. Sakata, J. DeLeon‐Ortega, V. Sakata and C. A. Girkin, "Optical coherence tomography of the retina and optic nerve – a review," The Canadian Journal of Chemical Engineering, vol. 137, no. 3, pp. 90-99, 2009.
[3]   E. Margalit and S. Sadda, "Retinal and optic nerve diseases," Artif Organs, vol. 27, pp. 74-963, 2003.
[4]   A.Puliafito, M. R.Hee, C. P.Lin, E. Reichel and J. S.Schuman, "Imaging of Macular Diseases with Optical Coherence Tomography," Ophthalmology, vol. 102, no. 2, pp. 217-229, 1995.
[5]   M. Libbrecht and W. Noble, "Machine learning applications in genetics and genomics," Nature Reviews, vol. 16, no. 6, pp. 2-231, 2015.
[6]   JL.Prince and J. Links, "Basic Imaging Principles," Medical Imaging Signals and Systems, pp. 513-518, 2015.
[7]   A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying and J. L. Prince, "Multiple-object geometric deformable model for segmentation of macular OCT," Biomed. Opt. Express , vol. 5, no. 4, pp. 1062-1074, 2014.
[8]   Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying and J. L. Prince, "Retinal layer segmentation of macular OCT images using boundary classification," Biomed. Opt. Express , vol. 4, no. 7, pp. 1133-1152, 2013.
[9]   H. R. S. K. R. Kafieh, "A Review of Algorithms for Segmentation of Optical Coherence Tomography from Retina," J Med Signals Sens., vol. 3, no. 1, pp. 45-60, 2013.
[10]P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt and e. al, "Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images," Biomedical optics express, vol. 5, pp. 3568-3577, 2014.
[11]F. G. Venhuizen, B. v. Ginneken, B. Bloemen, M. J. v. Grinsven, R. Philipsen, T. T. C. Hoyng and C. I. Sánchez, "Automated age-related macular degeneration classification in oct using unsupervised feature learning," in SPIE Medical Imaging : Computer-Aided Diagnosis, 2015.
[12]W. Sun, X. Liu and Z. Yang, "Automated detection of age-related macular degeneration in OCT images using multiple instance learning," in Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017.
[13]Y. Wang, Y. Zhang, Z. Yao, R. Zhao and F. Zhou, "Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images," Biomedical Optics Express, vol. 7, no. 12, p. 4928, 2016.
[14]Albarrak, F. Coenen and Y. Zheng, "Age-related Macular Degeneration Identification In Volumetric Optical Coherence Tomography Using Decomposition and Local Feature Extraction," in The 17th Annual Conference in Medical Image Understanding and Analysis, 2013.
[15]M. C. Y.Y. Liu, H. Ishikawa, G. Wollstein, J. S. Schuman and J. M. Rehg, "Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding," Medical Image Analysis, vol. 15, no. 5, pp. 748-759, 2011.
[16]S. Farsiu, S. J. Chiu, R. V. Oconnell, F. A. Folgar, E. Yuan, J. A. Izatt and C. A. Toth, "Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography," Ophthalmology, vol. 121, no. 1, pp. 162-172, 2014.
[17]Ravenscroft, J. Deng, X. Xie, L. Terry, T. H. Margrain, R. V. North and A. Wood, "AMD Classification in Choroidal OCT Using Hierarchical Texton Mining," Advanced Concepts for Intelligent Vision Systems Lecture Notes in Computer Science, pp. 237-248, 2017.
[18]R. Rasti, H. Rabbani, A. Mehridehnavi and F. Hajizadeh, "Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble," IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 1024-1034, 2018.
[19]S. Lee, D. M. Baughman and A. Y. Lee, "Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration," Ophthalmology Retina, vol. 1, no. 4, pp. 322-327, 2017.
[20]C. C. S. I. D. Z. S. W. a. R. S. S. Apostolopoulos, "“Retinet: Automatic AMD identification in OCT volumetric data," ArXiv preprint arXiv:1610.03628, 2016.
[21]S. L. Q. N. J. I. C. T. a. S. F. L. Fang, "Sparsity based denoising of spectral domain optical coherence tomography images," Biomedical Optics Express, vol. 3, no. 5, pp. 927-942, 2012.
[22]J. O. C. D. T. J.E. Goodman, in Handbook of Discrete and Computational Geometry, boca raton,FL, CRC press, 2017, p. 383.
[23]S. Lazebnik, C. Schmid and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in IEEE Computer Vision, 2006.
[24]J. Wu and J. M. Rehg, "Where am I: Place instance and category recognition using spatial PACT," in IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[25]J. F. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, 1986.
[26]N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in International Conference on Computer Vision & Pattern Recognition (CVPR), San Diego, 2005.
[27]H. Abdi and L. Williams, "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol. 2, no. 4, pp. 433-459, 2010.
[28]J. Chan and D. Paelinckx, "Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery," Remote Sensing of Environment, vol. 112, no. 6, pp. 2999-3011, 2008.
[29]Y. Song and L. Ying, "Decision tree methods: applications for classification and prediction," Shanghai archives of psychiatry, vol. 27, no. 2, pp. 130-135, 2015.
[30]R. J. F. T.Hastie, "Tree-Based Methods," in The Elements of Statistical Learning, New york, Springer, 2009, p. 309.
[31]G. T. Denison, B. K. Mallick and A. F. M. Smith, "A Bayesian CART algorithm," Biometrika, vol. 85, no. 2, pp. 363-377, 1998.
فصل نامه علمی پژوهشی مهندسی پزشکی زیستی
دوره 13، شماره 3
پاییز 1398
صفحه 235-246
فایل ها
سابقه مقاله
  • تاریخ دریافت: 29 اسفند 1397
  • تاریخ بازنگری: 18 مرداد 1398
  • تاریخ پذیرش: 17 شهریور 1398
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