Enhancement of Retinal Fundus Images via Pixel Color Amplification
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We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multi-task segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together.
KeywordsImage enhancement Medical image analysis Dehazing Segmentation Multi-task learning
We thank Dr. Alexander R. Gaudio, a retinal specialist and expert in degenerative retinal diseases, for his positive feedback and education of fundus images.
Supported in part by the National Funds through the Fundação para a Ciência e a Tecnologia within under Project CMUPERI/TIC/0028/2014.
- 1.Cao, L., Li, H., Zhang, Y.: Retinal image enhancement using low-pass filtering and alpha-rooting. Signal Process. 170, 107445 (2020). https://doi.org/10.1016/j.sigpro.2019.107445. http://www.sciencedirect.com/science/article/pii/S0165168419304967CrossRefGoogle Scholar
- 3.Galdran, A., Bria, A., Alvarez-Gila, A., Vazquez-Corral, J., Bertalmio, M.: On the duality between retinex and image dehazing. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (June 2018). https://doi.org/10.1109/cvpr.2018.00857
- 4.Gaudio, A.: Open source code (2020). https://github.com/adgaudio/ietk-ret
- 10.Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
- 11.Sahasrabuddhe, V., et al.: Indian diabetic retinopathy image dataset (IDRID) (2018). https://doi.org/10.21227/H25W98
- 12.Savelli, B., et al.: Illumination correction by dehazing for retinal vessel segmentation. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp. 219–224 (June 2017). https://doi.org/10.1109/CBMS.2017.28
- 13.Smailagic, A., Sharan, A., Costa, P., Galdran, A., Gaudio, A., Campilho, A.: Learned pre-processing for automatic diabetic retinopathy detection on eye fundus images. In: Karray, F., Campilho, A., Yu, A. (eds.) ICIAR 2019. LNCS, vol. 11663, pp. 362–368. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27272-2_32CrossRefGoogle Scholar
- 14.Tan, R.T.: Visibility in bad weather from a single image. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (June 2008). https://doi.org/10.1109/CVPR.2008.4587643
- 15.Wang, Y., Zhuo, S., Tao, D., Bu, J., Li, N.: Automatic local exposure correction using bright channel prior for under-exposed images. Signal Process. 93(11), 3227–3238 (2013). https://doi.org/10.1016/j.sigpro.2013.04.025. http://www.sciencedirect.com/science/article/pii/S0165168413001680CrossRefGoogle Scholar