Automated microaneurysm detection method based on eigenvalue analysis using hessian matrix in retinal fundus images

Diabetic retinopathy (DR) is the most frequent cause of blindness. Microaneurysm (MA) is an early symptom of DR. Therefore, the detection of MA is important for the early detection of DR. We have proposed an automated MA detection method based on double-ring filter, but it has given many false positives. In this paper, we propose an MA detection method based on eigenvalue analysis using a Hessian matrix, with an aim to improve MA detection. After image preprocessing, the MA candidate regions were detected by eigenvalue analysis using the Hessian matrix in green-channeled retinal fundus images. Then, 126 features were calculated for each candidate region. By a threshold operation based on feature analysis, false positive candidates were removed. The candidate regions were then classified either as MA or false positive using artificial neural networks (ANN) based on principal component analysis (PCA). The 126 features were reduced to 25 components by PCA, and were then inputted to ANN. When the method was evaluated on visible MAs using 25 retinal images from the retinopathy online challenge (ROC) database, the true positive rate was 73%, with eight false positives per image.

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

[2]  Lei Zhang,et al.  Sparse Representation Classifier for microaneurysm detection and retinal blood vessel extraction , 2012, Inf. Sci..

[3]  Bunyarit Uyyanonvara,et al.  Automatic Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Mathematical Morphology Methods , 2011 .

[4]  Bálint Antal,et al.  An adaptive weighting approach for ensemble-based detection of microaneurysms in color fundus images , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Roberto Hornero,et al.  Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images , 2009, Medical Imaging.

[6]  Ardimas Andi Purwita,et al.  Automated microaneurysm detection using mathematical morphology , 2011, 2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering.

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

[8]  Qin Li,et al.  Hierarchical detection of red lesions in retinal images by multiscale correlation filtering , 2009, Medical Imaging.

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

[10]  Bálint Antal,et al.  An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading , 2012, IEEE Transactions on Biomedical Engineering.

[11]  Atsushi Mizutani,et al.  Automated microaneurysm detection method based on double ring filter in retinal fundus images , 2009, Medical Imaging.

[12]  Kenneth W. Tobin,et al.  Microaneurysm detection with radon transform-based classification on retina images , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  András Hajdu,et al.  Microaneurysm detection in retinal images using a rotating cross-section based model , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[14]  Bunyarit Uyyanonvara,et al.  Fine Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using a Hybrid Approach , .

[15]  Hiroshi Fujita,et al.  Automated microaneurysm detection method based on double-ring filter and feature analysis in retinal fundus images , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).