DREAM: Diabetic Retinopathy Analysis Using Machine Learning

被引:214
作者
Roychowdhury, Sohini [1 ]
Koozekanani, Dara D. [2 ]
Parhi, Keshab K. [1 ]
机构
[1] Dept Elect & Comp Engn, Minneapolis, MN 55455 USA
[2] Univ Minnesota, Dept Ophthalmol & Visual Neurosci, Minneapolis, MN 55455 USA
关键词
Bright lesions; classification; diabetic retinopathy (DR); fundus image processing; red lesions; segmentation; severity grade; AUTOMATIC DETECTION; RETINAL IMAGES; SYSTEM; MICROANEURYSMS; CLASSIFICATION; DIAGNOSIS;
D O I
10.1109/JBHI.2013.2294635
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. Classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM), and AdaBoost are analyzed for classifying retinopathy lesions from nonlesions. GMM and kNN classifiers are found to be the best classifiers for bright and red lesion classification, respectively. A main contribution of this paper is the reduction in the number of features used for lesion classification by feature ranking using Adaboost where 30 top features are selected out of 78. A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. This lesion classification problem deals with unbalanced datasets and SVM or combination classifiers derived from SVM using the Dempster-Shafer theory are found to incur more classification error than the GMM and kNN classifiers due to the data imbalance. The DR severity grading system is tested on 1200 images from the publicly available MESSIDOR dataset. The DREAM system achieves 100% sensitivity, 53.16% specificity, and 0.904 AUC, compared to the best reported 96% sensitivity, 51% specificity, and 0.875 AUC, for classifying images as with or without DR. The feature reduction further reduces the average computation time for DR severity per image from 59.54 to 3.46 s.
引用
收藏
页码:1717 / 1728
页数:12
相关论文
共 39 条
[1]  
A. D. S. for the Department of Health and Ageing, 2008, GUID MAN DIAB RET
[2]   Automated detection of diabetic retinopathy: barriers to translation into clinical practice [J].
Abramoff, Michael D. ;
Niemeijer, Meindert ;
Russell, Stephen R. .
EXPERT REVIEW OF MEDICAL DEVICES, 2010, 7 (02) :287-296
[3]   Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes [J].
Abramoff, Michael D. ;
Niemeijer, Meindert ;
Suttorp-Schulten, Maria S. A. ;
Viergever, Max A. ;
Russell, Stephen R. ;
van Ginneken, Bram .
DIABETES CARE, 2008, 31 (02) :193-198
[4]   Computer-based detection of diabetes retinopathy stages using digital fundus images [J].
Acharya, U. R. ;
Lim, C. M. ;
Ng, E. Y. K. ;
Chee, C. ;
Tamura, T. .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE, 2009, 223 (H5) :545-553
[5]   Automatic Detection of Diabetic Retinopathy and Age-Related Macular Degeneration in Digital Fundus Images [J].
Agurto, Carla ;
Barriga, E. Simon ;
Murray, Victor ;
Nemeth, Sheila ;
Crammer, Robert ;
Bauman, Wendall ;
Zamora, Gilberto ;
Pattichis, Marios S. ;
Soliz, Peter .
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2011, 52 (08) :5862-5871
[6]   Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection [J].
Agurto, Carla ;
Murray, Victor ;
Barriga, Eduardo ;
Murillo, Sergio ;
Pattichis, Marios ;
Davis, Herbert ;
Russell, Stephen ;
Abramoff, Michael ;
Soliz, Peter .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2010, 29 (02) :502-512
[7]  
[Anonymous], 2011, METHODS EVALUATE SEG
[8]  
[Anonymous], 2011, DAT 2011 NAT DIAB FA
[9]   An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading [J].
Antal, Balint ;
Hajdu, Andras .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2012, 59 (06) :1720-1726
[10]   AUTOMATIC SYSTEM FOR DIABETIC RETINOPATHY SCREENING BASED ON AM-FM, PARTIAL LEAST SQUARES, AND SUPPORT VECTOR MACHINES [J].
Barriga, E. Simon ;
Murray, Victor ;
Agurto, Carla ;
Pattichis, Marios ;
Bauman, Wendall ;
Zamora, Gilberto ;
Soliz, Peter .
2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, 2010, :1349-1352