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2019 | OriginalPaper | Chapter

Retinal Blood Vessel Segmentation by Multi-channel Deep Convolutional Autoencoder

Authors : Andrés Ortiz, Javier Ramírez, Ricardo Cruz-Arándiga, María J. García-Tarifa, Francisco J. Martínez-Murcia, Juan M. Górriz

Published in: International Joint Conference SOCO’18-CISIS’18-ICEUTE’18

Publisher: Springer International Publishing

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Abstract

The evaluation and diagnosis of retina pathologies are usually made by the analysis of different image modalities that allows to explore its structure. The most popular retina image method is the retinography, a technique to show the retina and other structures in the fundus of the eye. This paper deals with an important stage of the retina image processing for a diagnosis tool which aims to show the blood vessel structure. Our proposal is based on a deep convolutional neural network, that avoids any preprocessing stage such as gray scale conversion, histogram equalization, and other image transformations that determine the final result. Thus, we obtain the blood vessel segmentation directly from the original RGB color retinography image. The results obtained with our method are comparable to the state-of-the art methods but using a smaller network with less memory and computation requirements. Our approach has been assessed using the DRIVE database.

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Literature
1.
2.
Fraz, M.M., et al.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538–2548 (2012)CrossRef
3.
4.
Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)CrossRef
5.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
6.
Liskowski, P., et al.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35, 2369–2380 (2016)CrossRef
7.
Melinscak, M., et al.: Retinal vessel segmentation using deep neural networks. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015), pp. 577–582 (2015)
8.
Niemeijer, M., Staal, J.J., Van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Michael Fitzpatrick, J., Sonka, M. (eds.) SPIE Medical Imaging SPIE, vol. 5370, pp. 648–656 (2004)
9.
Ortiz, A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D., Llamas-Elvira, J.M.: Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Appl. Soft Comput. 13(5), 2668–2682 (2013)CrossRef
10.
Ortiz, A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D.: Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering. Inf. Sci. 262, 117–136 (2014)CrossRef
11.
Ortiz, A., Munilla, J., Górriz, J.M., Ramírez, J.: Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s Disease. Int. J. Neural Syst. 26, 07 (2016)CrossRef
12.
Ortiz, A., Górriz, J.M., Ramírez, J., Martínez-Murcia, F.J.: Automatic ROI selection in structural brain MRI using SOM 3D projection. PLoS ONE 9(4), e93851 (2014)CrossRef
13.
Osareh, A., et al.: Automatic blood vessel segmentation in color images of retina. Iran. J. Sci. Technol. Trans. B Eng. 33(B2), 191–206 (2009)MATH
14.
15.
Roychowdhury, S., et al.: Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J. Biomed. Health Inform. 19(3), 1118–1128 (2015)
16.
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep Inside convolutional networks: visualising image classification models and saliency maps. CoRR, abs/1312.6034 (2013)
17.
Soares, J.V., et al.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)CrossRef
Metadata
Title
Retinal Blood Vessel Segmentation by Multi-channel Deep Convolutional Autoencoder
Authors
Andrés Ortiz
Javier Ramírez
Ricardo Cruz-Arándiga
María J. García-Tarifa
Francisco J. Martínez-Murcia
Juan M. Górriz
Copyright Year
2019
Book
International Joint Conference SOCO’18-CISIS’18-ICEUTE’18

Print ISBN: 978-3-319-94119-6

Electronic ISBN: 978-3-319-94120-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-94120-2_4

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