Publication: Automated Cytomegalovirus Retinitis Screening in Fundus Images
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Issued Date
2020-07-01
Resource Type
ISSN
1557170X
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2-s2.0-85091004326
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol.2020-July, (2020), 1996-2002
Suggested Citation
P. Kingkosol, P. Pooprasert, P. Choopong, B. Hunchangsith, V. Laksanaphuk, C. Tantibundhit Automated Cytomegalovirus Retinitis Screening in Fundus Images. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol.2020-July, (2020), 1996-2002. doi:10.1109/EMBC44109.2020.9175461 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/59043
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Title
Automated Cytomegalovirus Retinitis Screening in Fundus Images
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Abstract
© 2020 IEEE. This work proposes an automated algorithms for classifying retinal fundus images as cytomegalovirus retinitis (CMVR), normal, and other diseases. Adaptive wavelet packet transform (AWPT) was used to extract features. The retinal fundus images were transformed using a 4-level Haar wavelet packet (WP) transform. The first two best trees were obtained using Shannon and log energy entropy, while the third best tree was obtained using the Daubechies-4 mother wavelet with Shannon entropy. The coefficients of each node were extracted, where the feature value of each leaf node of the best tree was the average of the WP coefficients in that node, while those of other non-leaf nodes were set to zero. The feature vector was classified using an artificial neural network (ANN). The effectiveness of the algorithm was evaluated using ten-fold cross-validation over a dataset consisting of 1,011 images (310 CMVR, 240 normal, and 461 other diseases). In testing, a dataset consisting of 101 images (31 CMVR, 24 normal, and 46 other diseases), the AWPT-based ANN had sensitivities of 90.32%, 83.33%, and 91.30% and specificities of 95.71%, 94.81%, and 92.73%. In conclusion, the proposed algorithm has promising potential in CMVR screening, for which the AWPT-based ANN is applicable with scarce data and limited resources.