A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy

Early detection of microaneurysms (MAs), the first sign of Diabetic Retinopathy (DR), is an essential first step in automated detection of DR to prevent vision loss and blindness. This study presents a novel and different algorithm for automatic detection of MAs in fluorescein angiography (FA) fundus images, based on Radon transform (RT) and multi-overlapping windows. This project addresses a novel method, in detection of retinal land marks and lesions to diagnose the DR. At the first step, optic nerve head (ONH) was detected and masked. In preprocessing stage, top-hat transformation and averaging filter were applied to remove the background. In main processing section, firstly, we divided the whole preprocessed image into sub-images and then segmented and masked the vascular tree by applying RT in each sub-image. After detecting and masking retinal vessels and ONH, MAs were detected and numbered by using RT and appropriated thresholding. The results of the proposed method were evaluated on three different retinal images databases, the Mashhad Database with 120 FA fundus images, Second Local Database from Tehran with 50 FA retinal images and a part of Retinopathy Online Challenge (ROC) database with 22 images. Automated DR detection demonstrated a sensitivity and specificity of 94% and 75% for Mashhad database and 100% and 70% for the Second Local Database respectively.

[1]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[2]  T. Banaee,et al.  Radon transform technique for linear structures detection: Application to vessel detection in fluorescein angiography fundus images , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[3]  P F Sharp,et al.  An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. , 1996, Computers and biomedical research, an international journal.

[4]  P. Sharp,et al.  Automated detection and quantification of microaneurysms in fluorescein angiograms , 2004, Graefe's Archive for Clinical and Experimental Ophthalmology.

[5]  Jean-Claude Klein,et al.  Automatic Detection Of Microaneurysms In Retinopathy Fluoro-Angiogram , 1984, Optics + Photonics.

[6]  J. Olson,et al.  Automated assessment of diabetic retinal image quality based on clarity and field definition. , 2006, Investigative ophthalmology & visual science.

[7]  Keith A Goatman,et al.  Automated measurement of microaneurysm turnover. , 2003, Investigative ophthalmology & visual science.

[8]  M. Kamel,et al.  A neural network approach for the automatic detection of microaneurysms in retinal angiograms , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[9]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[10]  Kenneth W. Tobin,et al.  Microaneurysms detection with the radon cliff operator in retinal fundus images , 2010, Medical Imaging.

[11]  P F Sharp,et al.  Quantifying changes in retinal circulation: the generation of parametric images from fluorescein angiograms. , 1998, Physiological measurement.

[12]  Reza Pourreza,et al.  Segmentation of blood vessels in fundus color images by Radon transform and morphological reconstruction , 2010, Third International Workshop on Advanced Computational Intelligence.

[13]  E. Kohner,et al.  Quantitative Evaluation of Fluorescein Angiograms: Microaneurysm Counts , 1983, Diabetes.

[14]  Ahmed Ali Mohammed,et al.  Integral transforms and their applications , 2009 .

[15]  V. Mohan,et al.  Prevalence of diabetic retinopathy in urban India: the Chennai Urban Rural Epidemiology Study (CURES) eye study, I. , 2005, Investigative ophthalmology & visual science.

[16]  D. Klonoff,et al.  An economic analysis of interventions for diabetes. , 2000, Diabetes care.

[17]  J. Olson,et al.  Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool , 2000, Diabetic medicine : a journal of the British Diabetic Association.

[18]  B. van Ginneken,et al.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. , 2007, Investigative ophthalmology & visual science.

[19]  T. Banaee,et al.  Automated optic nerve head detection in fluorescein angiography fundus images , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[20]  Gwénolé Quellec,et al.  Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.

[21]  Meindert Niemeijer,et al.  Automated detection of diabetic retinopathy: barriers to translation into clinical practice , 2010, Expert review of medical devices.

[22]  Bálint Antal,et al.  Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods , 2012, Pattern Recognit..

[23]  Peter F. Sharp,et al.  Automated microaneurysm detection using local contrast normalization and local vessel detection , 2006, IEEE Transactions on Medical Imaging.

[24]  Ana Maria Mendonça,et al.  Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[25]  Peter F Sharp,et al.  A fully automated comparative microaneurysm digital detection system , 1997, Eye.

[26]  Reza Pourreza-Shahri,et al.  A Radon Transform Based Approach for Extraction of Blood Vessels in Conjunctival Images , 2008, MICAI.

[27]  Stephen J. Aldington,et al.  Methodology for retinal photography and assessment of diabetic retinopathy: the EURODIAB IDDM Complications Study , 1995, Diabetologia.

[28]  Shehzad Khalid,et al.  Identification and classification of microaneurysms for early detection of diabetic retinopathy , 2013, Pattern Recognit..

[29]  Qin Li,et al.  Detection of microaneurysms using multi-scale correlation coefficients , 2010, Pattern Recognit..

[30]  J. Klein,et al.  Automatic detection of microaneurysms in diabetic fluorescein angiography. , 1984, Revue d'epidemiologie et de sante publique.

[31]  M. Hafez,et al.  Using adaptive edge technique for detecting microaneurysms in fluorescein angiograms of the ocular fundus , 2002, 11th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.02CH37379).

[32]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[33]  Jayanthi Sivaswamy,et al.  A Successive Clutter-Rejection-Based Approach for Early Detection of Diabetic Retinopathy , 2011, IEEE Transactions on Biomedical Engineering.

[34]  M. Cree,et al.  A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms , 1998, Comput. Biol. Medicine.

[35]  Mustafa Ertas,et al.  Prevalence of Peripheral Neuropathy and Painful Peripheral Neuropathy in Turkish Diabetic Patients , 2011, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.