Abstract
The computer-aided diagnosis technology in retinal image analysis requires localization of different fundus structures. Efficient detection and localization of fovea are essential in the analysis of diabetic macular edema. This paper demonstrates a novel technique for detection of fovea from color fundus images based on image enhancement by adaptive manifold filter and further mathematical morphological operations for final foveal center localization. The major advantage of the proposed technique is that it does not need a spatial relationship of optic disc and vessels for the detection of fovea. It is robust to illumination changes and interference caused by retinal pathologies. Experiments show encouraging results that are analyzed on five publically available databases DRIVE, HEI-MED, DIARETDB1, HRF, and MESSIDOR with an accuracy of detection as 100%, 99.40%, 98.88%, 100%, and 98.66%, respectively. Comparative analysis of results indicates that the proposed method achieves better performance than other earlier methods present in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Sahasrabuddhe, V.: Automatic retinal image analysis for the detection of diabetic retinopathy. Biomed. Signal Image Process. Patient Care 146 (2017)
Tobin, K.W., Chaum, E., Govindasamy, V.P., Karnowski, T.P.: Detection of anatomic structures in human retinal imagery. IEEE Trans. Med. Imaging 26(12), 1729–1739 (2007)
Sagar, A.V., Balasubramanian, S., Chandrasekaran, V.: Automatic detection of anatomical structures in digital fundus retinal images. In: MVA, pp. 483–486 (2007)
Niemeijer, M., Abramoff, M.D., Van Ginneken, B.: Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Trans. Med. Imaging 26(1), 116–127 (2007)
Welfer, D., Scharcanski, J., Marinho, D.R.: Fovea center detection based on the retina anatomy and mathematical morphology. Comput. Methods Program. Biomed. 104(3), 397–409 (2011)
Chin, K.S., Trucco, E., Tan, L., Wilson, P.J.: Automatic fovea location in retinal images using anatomical priors and vessel density. Pattern Recogn. Lett. 34(10), 1152–1158 (2013)
Akram, M.U., Tariq, A., Khan, S.A., Javed, M.Y.: Automated detection of exudates and macula for grading of diabetic macular edema. Comput. Methods Program. Biomed. 114(2), 141–152 (2014)
Aquino, A.: Establishing the macular grading grid by means of fovea centre detection using anatomical-based and visual-based features. Comput. Biol. Med. 55, 61–73 (2014)
Kao, E.F., Lin, P.C., Chou, M.C., Jaw, T.S., Liu, G.C.: Automated detection of fovea in fundus images based on vessel-free zone and adaptive gaussian template. Comput. Methods Program. Biomed. 117(2), 92–103 (2014)
Medhi, J.P., Dandapat, S.: An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Comput. Biol. Med. 74, 30–44 (2016)
Molina-Casado, J.M., Carmona, E.J., GarcÃa-Feijoó, J.: Fast detection of the main anatomical structures in digital retinal images based on intra-and inter-structure relational knowledge. Comput. Methods Program. Biomed. (2017)
Tan, J.H., Acharya, U.R., Bhandary, S.V., Chua, K.C., Sivaprasad, S.: Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci. 20, 70–79 (2017)
Gastal, E.S., Oliveira, M.M.: Adaptive manifolds for real-time high-dimensional filtering. ACM Trans. Graph. (TOG) 31(4), 33 (2012)
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Giancardo, L., Meriaudeau, F., Karnowski, T.P., Li, Y., Garg, S., Tobin, K.W., Chaum, E.: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med. Image Anal. 16(1), 216–226 (2012)
Kauppi, T., Kamarainen, J.K., Lensu, L., Kalesnykiene, V., Sorri, I., Uusitalo, H., Kälviäinen, H.: A framework for constructing benchmark databases and protocols for retinopathy in medical image analysis. In: International Conference on Intelligent Science and Intelligent Data Engineering, pp. 832–843. Springer (2012)
Khler, 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)
Decencière, E., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., Gain, P., Ordóñez-Varela, J.R., Massin, P., Erginay, A., et al.: Feedback on a publicly distributed image database: the messidor database. Image Anal. Stereol. 33(3), 231–234 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pachade, S., Porwal, P., Kokare, M. (2019). A Novel Method to Detect Fovea from Color Fundus Images. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_97
Download citation
DOI: https://doi.org/10.1007/978-981-13-1513-8_97
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1512-1
Online ISBN: 978-981-13-1513-8
eBook Packages: EngineeringEngineering (R0)