Abstract
Automated classification of retinal vessels in fundus images is the first step towards measurement of retinal characteristics that can be used to screen and diagnose vessel abnormalities for cardiovascular and retinal disorders. This paper presents a novel approach to vessel classification to compute the artery/vein ratio (AVR) for all blood vessel segments in the fundus image. The features extracted are then subjected to a selection procedure using Random Forests (RF) where the features that contribute most to classification accuracy are chosen as input to a polynomial kernel Support Vector Machine (SVM) classifier. The most dominant feature was found to be the vessel information obtained from the Light plane of the LAB color space. The SVM is then subjected to one time training using 10-fold cross validation on images randomly selected from the VICAVR dataset before testing on an independent test dataset, derived from the same database. An Area Under the ROC Curve (AUC) of 97.2% was obtained on an average of 100 runs of the algorithm. The proposed algorithm is robust due to the feature selection procedure, and it is possible to get similar accuracies across many datasets.
Original language | English (US) |
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Title of host publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1320-1323 |
Number of pages | 4 |
Volume | 2016-October |
ISBN (Electronic) | 9781457702204 |
DOIs | |
State | Published - Oct 13 2016 |
Event | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States Duration: Aug 16 2016 → Aug 20 2016 |
Other
Other | 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 |
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Country/Territory | United States |
City | Orlando |
Period | 8/16/16 → 8/20/16 |