@inproceedings{scholars4937,
             doi = {10.1109/ICIAS.2014.6869472},
            note = {cited By 1; Conference of 2014 5th International Conference on Intelligent and Advanced Systems, ICIAS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:107042},
         address = {Kuala Lumpur},
           title = {Making every microaneurysm count: A hybrid approach to monitor progression of diabetic retinopathy},
            year = {2014},
         journal = {2014 5th International Conference on Intelligent and Advanced Systems: Technological Convergence for Sustainable Future, ICIAS 2014 - Proceedings},
       publisher = {IEEE Computer Society},
        keywords = {Edge detection, Canny edge detection; Diabetic retinopathy; Hybrid approach; Local thresholding; Microaneurysms; Noise filtering; Retinal image analysis; Retinal vessels, Eye protection},
          author = {Ali, S. S. A. and Tang, T. B. and Laude, A. and Faye, I.},
            isbn = {9781479946549},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906342273&doi=10.1109\%2fICIAS.2014.6869472&partnerID=40&md5=a910f7a6732c6c9df0544cab4ac5a100},
        abstract = {Previous studies have shown that the progression of diabetic retinopathy (DR) can be monitored by counting the number of microaneurysms (MAs). In this work, we propose an approach to maximize the sensitivity of MA detection by using a combination of two different techniques, namely local thresholding and Canny edge detection. The proposed approach was evaluated with publically available dataset DIARETDB1 where ten fundus images containing a total of 315 MAs were randomly selected. Out of these, the proposed approach was able to detect 295 MAs, achieving a sensitivity of 93.65. Limiting factors were identified to be noise filtering, presence of artifacts and dark-shaded retinal vessels. {\^A}{\copyright} 2014 IEEE.}
}