Abstract
Deep learning performs as a computational tool with various potential utilities in ophthalmology. Retinal infections of the eye need to analyze small retinal vessels, microaneurysms, and exudates in the diagnosis of retinal diseases. Due to the appearance of various noises in the fundus images, the retinal vasculature is too complicated to be analyzed for retinal conditions. In this work, we have focused on the field of advanced deep learning in which plethora of architecture is available with the increase in dimension and flexibility of the retinal fundus images. Removal of noise is an essential part to better visibility of noisy fundus and thus a deep learning method for degraded retinal fundus image restoration scheme has been suggested in this investigation. A deep convolutional denoising auto-encoder method based on total variational multi-norm loss function minimization with batch normalization approach has been introduced for restoration of the fundus. The proposed scheme is utilized to restore the perceptible structural details of fundus as well as to decrease the noise level. Moreover, the speed of the network for target noisy images is faster compared to that of other models after fine-tuning of the network with the dropout mechanism. The retinal image databases such as DRIVE, STARE, and DIARETDB1 have been adopted to assess image denoising effects. Our approach to increase the visibility of fundus images by noise reduction through a deep training method has significantly delivered better performance without losing image details along with having fast convergence rate.
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References
Sonka, M., Hlavac, V., Boyle, R.: Image processing analysis and machine vision. Thomson Learn (2001)
McBee, M.P., et al.: Deep Learning in Radiology. Elsevier, Academic Radiology (2018)
Moraru, L., Obreja, C.D., Dey, N., Ashour, A.S.: Dempster-shafer fusion for effective retinal vessels diameter measurement. Soft Comput. Based Med. Image Anal. 149-160 (2018)
Chakraborty, S., Mukherjee, A., Chatterjee, D., Maji, P., Acharjee, S., Dey, N.: A semi-automated system for optic nerve head segmentation in digital retinal images. In: 2014 International Conference on Information Technology (ICIT), IEEE (2014)
Zhang, Y., Tian, X., Ren, P.: An adaptive bilateral filter based framework for image denoising. Neurocomputing (2014)
Zhang, Y., Zhang, W.H., Chen, H., Yang, N.L., Li, T.Y., Zhou, J.L.: Few-view image reconstruction combining total variation and a high-order norm. Int. J. Imaging Syst. Technol. 23(3), 249–255 (2013)
Hang, Z., Gallo, O., Frosio, I., Kautz, J.: Loss Functions for Image Restoration with Neural Networks. IEEE Transactions on Computational Imaging, ACM, arxiv.org/abs/1511.08861 (2016)
Abramoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)
Ghosh, S.K., Ghosh, A., Chakrabarti, A.: VEA: Vessels extraction algorithm and a novel wavelet analyser for diabetic retinopathy detection. Int. Journal of Image and Graphics-Worldscientific 18(2) (2018)
Singer, D.E., Nathan, D.M., Fogel, H.A., Schachat, A.P.: Screening for diabetic retinopathy. Ann. Intern. Med. 116, 660–671 (1992)
Kanski JJ, Bowling B (2012) Synopsis of Clinical Ophthalmology. Elsevier Health Sci
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Goodfellow, I., Bengio, Y., et al.: Deep Learning. MIT Press (2016)
Chen, Y., Shi, L., Feng, O., Yang, J., Shu, H., et al.: Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans. Med. Imaging 33(12), 2271–2292 (2014)
Zhang, Y., Wang, Y., et al.: Statistical iterative reconstruction using adaptive fractional order regularization. Biomed. Opt. Exp. 7(3), 1015–1029 (2016)
Li, Z., et al.: Adaptive nonlocal means filtering based on local noise level for CT denoising. Med. Phys. 41(1) (2014)
Huang, Z.K., Li, Z.H.: Comparison of different image denoising algorithms for Chinese calligraphy images. Neurocomputing 188, 102–112 (2016)
Gondara, L.: Medical image denoising using convolutional denoising autoencoders. Pro. of the IEEE Int. Con. Data Mining Workshops (ICDMW 2016) pp. 241–246 (2016)
Doersch, C.: Tutorial on variational autoencoders. Mach. Learn. arxiv.org/abs/1606.05908 (2016)
Im, D.J., Ahn, S., Memisevic, R., Bengio, Y.: Denoising Criterion for Variational auto-encoding framework. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) arXiv:1511.06406 (2017)
Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A.: Variational autoencoder for deep learning of images, Labels and Captions. In: 30th Conference on Neural Information Processing Systems (NIPS 2016), arXiv:1609.08976 (2016)
Vincent, P.: A connection between score matching and denoising autoencoders. Neural Comput. 23(7), 1661–1674 (2011)
Mao, X.J., Shen, C., Yang, Y.B.: Image restoration using convolutional autoencoders with symmetric skip connections. In: Proceeding of NIPS (2016)
Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P.: Image quality assessment: from error visibility to structural similarity. IEEE Tran. of Image Processing 13(4) (2004)
Niemeijer, M., Staal, J.J., Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database In: Fitzpatrick, J.M., Sonka M. (eds.), SPIE Medical Imaging, SPIE 5370, pp. 648-656 (2004)
Available from: https://www.isi.uu.nl/Research/Databases/DRIVE/
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Available from: http://cecas.clemson.edu/~ahoover/stare/
Kalesnykiene, V., Kamarainen, J. K., Voutilainen, R., Pietil, J., Klviinen, H., Uusitalo, H.: DIARETDB1 diabetic retinopathy database and evaluation protocol (2007)
Gu, K., Zhai, G., Yang, X., Zhang, W.: Using free energy principle for blind image quality assessment. IEEE Trans. Multimedia 17(1), 50–63 (2015)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3) (2002)
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Biswas, B., Ghosh, S.K., Ghosh, A. (2020). DVAE: Deep Variational Auto-Encoders for Denoising Retinal Fundus Image. In: Bhattacharyya, S., Konar, D., Platos, J., Kar, C., Sharma, K. (eds) Hybrid Machine Intelligence for Medical Image Analysis. Studies in Computational Intelligence, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-8930-6_10
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