Retinal Image Quality Assessment by Mean-Subtracted Contrast-Normalized Coefficients
The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost.
This work is financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia within project CMUP-ERI/TIC/0028/2014. Teresa Araújo is funded by the FCT grant contract SFRH/BD/122365/2016.
- 6.Hamid, L.A., El-Rafei, A., El-Ramly, S., Michelson, G., Hornegger, J.: No-reference wavelet based retinal image quality assessment. In: Computational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE), Spain, 2015, p. 123. CRC Press (2015)Google Scholar
- 7.Kohler, T., Budai, A., Kraus, M.F., Odstrilik, J., Michelson, G., Hornegger, J.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 95–100 (2013)Google Scholar
- 8.Kolar, R., Odstrcilik, J., Jan, J., Harabis, V.: Illumination correction and contrast equalization in colour fundus images. In: European Signal Processing Conference, pp. 298–302 (2011)Google Scholar
- 13.Savelli, B., Bria, A., Marrocco, C., Molinara, M., Tortorella, F., Galdran, A., Campilho, A.: Illumination correction by Dehazing for retinal vessel segmentation. In: The 30th IEEE International Symposium on Computer-Based Medical Systems (CBMS) (2017)Google Scholar