Automatic detection of microaneurysms in diabetic retinopathy fundus images using the L*a*b color space.

We develop an automated image processing system for detecting microaneurysm (MA) in diabetic patients. Diabetic retinopathy is one of the main causes of preventable blindness in working age diabetic people with the presence of an MA being one of the first signs. We transform the eye fundus images to the L*a*b* color space in order to separately process the L* and a* channels, looking for MAs in each of them. We then fuse the results, and last send the MA candidates to a k-nearest neighbors classifier for final assessment. The performance of the method, measured against 50 images with an ophthalmologist's hand-drawn ground-truth, shows high sensitivity (100%) and accuracy (84%), and running times around 10 s. This kind of automatic image processing application is important in order to reduce the burden on the public health system associated with the diagnosis of diabetic retinopathy given the high number of potential patients that need periodic screening.

[1]  Massimo Castagnola,et al.  Inactivation of Human Salivary Glutathione Transferase P1-1 by Hypothiocyanite: A Post-Translational Control System in Search of a Role , 2014, PloS one.

[2]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[3]  Joseph M. Reinhardt,et al.  Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images , 2013, IEEE Transactions on Medical Imaging.

[4]  Tien Yin Wong,et al.  Diabetic retinopathy , 2010, The Lancet.

[5]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

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

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

[8]  Chengjun Liu,et al.  Learning the Uncorrelated, Independent, and Discriminating Color Spaces for Face Recognition , 2008, IEEE Transactions on Information Forensics and Security.

[9]  Georg Lambert,et al.  Wavelet methods for texture defect detection , 1997, Proceedings of International Conference on Image Processing.

[10]  W. Ambrosius,et al.  Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses , 2014, PloS one.

[11]  H. Taylor,et al.  World blindness: a 21st century perspective , 2001, The British journal of ophthalmology.

[12]  Guy Cazuguel,et al.  FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE , 2014 .

[13]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Jamshid Dehmeshki,et al.  Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification , 2014, Comput. Methods Programs Biomed..

[15]  Herbert F Jelinek,et al.  Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[16]  Bingo Wing-Kuen Ling,et al.  The Reading of Components of Diabetic Retinopathy: An Evolutionary Approach for Filtering Normal Digital Fundus Imaging in Screening and Population Based Studies , 2013, PloS one.

[17]  Bülent Sankur,et al.  Selection of thresholding methods for nondestructive testing applications , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[18]  S. Edward Rajan,et al.  Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images , 2014, IET Image Process..

[19]  Gwénolé Quellec,et al.  Exudate detection in color retinal images for mass screening of diabetic retinopathy , 2014, Medical Image Anal..

[20]  Bunyarit Uyyanonvara,et al.  Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images , 2013, Comput. Medical Imaging Graph..

[21]  A. Hussain,et al.  Feature Extraction Technique using Discrete Wavelet Transform for Image Classification , 2007, 2007 5th Student Conference on Research and Development.

[22]  Keshab K. Parhi,et al.  DREAM: Diabetic Retinopathy Analysis Using Machine Learning , 2014, IEEE Journal of Biomedical and Health Informatics.

[23]  Karolin Baecker,et al.  Two Dimensional Signal And Image Processing , 2016 .

[24]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[25]  Philip J. Morrow,et al.  Algorithms for digital image processing in diabetic retinopathy , 2009, Comput. Medical Imaging Graph..