Abstract
Age-related macular degeneration (ARMD) is one of the most widespread diseases of the eye fundus and is the most common cause of vision loss for those over the age of 60. There are several ways to diagnose ARMD. One of them is the Fundus Autofluorescence (FAF) method, and is one of the modalities of Heidelberg Engineering diagnostic devices. The BluePeak™ modality utilizes the fluorescence of lipofuscin (a pigment in the affected cells) to display the extent of the disease’s progression. The native image is further evaluated to more precisely specify the diagnosis of the disease—it is necessary to determine the size of the macular lesion area. Calculations of the geometric parameters of macular lesions were conducted in the MATLAB® software; the size of the lesion area was determined using the Image Processing Toolbox. The automated lesion detection method occurs using a parametric active contour (active contours driven by local Gaussian distribution fitting energy) that encloses the affected macular lesion, thereby allowing a precise determination of the affected area. This method is relatively quick for use in clinical practice and allows evaluation the macular lesions exactly based on the proportion with the feature extraction in advance. The proposed methodology is fully automatic. In the algorithm input we define area of interest and initial circle, which is placed inside of the object. Image background is suppressed by low pass filter. Final contour is formed in consecutive steps, up to shape of macular lesion.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, Z.L., et al.: Bevacizumab cured age-related macular degeneration (AMD) via down-regulate TLR2 pathway. Central Eur. J. Biol. 9(5), 469–475 (2014). doi:10.2478/s11535-014-0290-5
Christen, W.G., Chew, E.Y.: Does long-term aspirin use increase the risk of neovascular age-related macular degeneration? Expert Opin. Drug Saf. 13(4), 421–429 (2014). doi:10.1517/14740338.2014.889680
Pustkova, R., et al.: Measurement and calculation of cerebrospinal fluid in proportion to the skull. In: 2010 9th Roedunet International Conference (RoEduNet) (2010)
Cheung, L.K., Eaton, A.: Age-related macular degeneration. Pharmacotherapy: J. Human Pharmacol. Drug Ther. 33(8), 838–855 (2013). doi:10.1002/phar.1264
Penhaker, M., Matejka, V.: Image registration in neurology applications. In: 2010 International Conference on Networking and Information Technology (ICNIT) (2010)
Tsika, Ch., Tsilimbaris, M.K., Makridaki, M., Kontadakis, G., Plainis, S., Mos-chandreas, J.: Assessment of macular pigment optical density (MPOD) in patients with unilateral wet age-related macular degeneration (AMD). Acta Ophthalmol. 89(7), e573–e578 (2011)
Stetson, P.F., et al.: OCT minimum intensity as a predictor of geographic atrophy enlargement. Invest. Ophthalmol. 55(2), 792–800 (2014). doi:10.1167/iovs.13-13199
Alam, S., et al.: Clinical Application of rapid serial fourier-domain optical coherence tomography for macular imaging. Ophthalmology 113(8), 1425–1431 (2006). doi:10.1016/j.ophtha.2006.03.020
Kubicek, J., et al.: Segmentation of MRI data to extract the blood vessels based on fuzzy thresholding. In: New Trends in Intelligent Information and Database Systems, pp. 43–52. Springer International Publishing, Berlin (2015)
Coscas, G., et al.: Optical coherence tomography in age-related macular degener-ation: OCT in AMD. Springer, Heidelberg (2009). ISBN 978-364-2014-680
Besirli, C.G., Comer, G.M.: High-resolution OCT imaging of RPE degeneration in bilateral diffuse uveal melanocytic proliferation. Ophthalmic Surg. Lasers Imaging. 41(6), S96–S100 (2010). doi:10.3928/15428877-20101031-03
Blue Laser Autofluorescence. A supplement to Ophthalmology Times Europe: Blue laser autofluorescence [online]. Advanstar Communications, Chester (2009) [cit. 2013-12-03]. ISSN 1753-3066)
Wang, L., et al.: Active contour driven by local Gaussian distribution fitting energy. Sig. Proc. 89(12), 2435–2447 (2009). doi:10.1016/j.sigpro
Kubicek, J., Penhaker, M.: Fuzzy algorithm for segmentation of images in extraction of objects from MRI. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE (2014)
Kubicek, J., et al.: Articular cartilage defect detection based on image segmentation with colour mapping, in computational collective intelligence. Technologies and Applications, pp. 214–222. Springer International Publishing, Berlin (2014)
Acknowledgements
The work and the contributions were supported by the project SP2015/179 ‘Biomedicínské inženýrské systémy XI’ and This work is partially supported by the Science and Research Fund 2014 of the Moravia-Silesian Region, Czech Republic and this paper has been elaborated in the framework of the project “Support research and development in the Moravian-Silesian Region 2014 DT 1—Research Teams” (RRC/07/2014). Financed from the budget of the Moravian-Silesian Region.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kubicek, J., Bryjova, I., Penhaker, M., Javurkova, J., Kolarcik, L. (2016). Segmentation of Macular Lesions Using Active Shape Contour Method. In: Stýskala, V., Kolosov, D., Snášel, V., Karakeyev, T., Abraham, A. (eds) Intelligent Systems for Computer Modelling . Advances in Intelligent Systems and Computing, vol 423. Springer, Cham. https://doi.org/10.1007/978-3-319-27644-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-27644-1_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27642-7
Online ISBN: 978-3-319-27644-1
eBook Packages: EngineeringEngineering (R0)