Abstract
An analysis of the optical disc is challenging task in the field of clinical ophthalmology. Optical disc (OD) is frequently utilized as reference parameter for time evolution of retinal changes therefore, their analysis is significantly important. In the clinical practice, there are especially problem with lower quality of retinal records acquired by retinal probe of RetCam 3, and worse observation of OD area. Therefore, many algorithms are unable to precisely approximate of OD area. We propose a method based on the active snake model carrying out automatic extraction of retinal disc area even in the spots where an OD is not clearly observable, or image edges completely missing. Furthermore, the proposed solution calculates OD centroid, and respective area for further comparison of OD with retinal lesions.
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References
Kumar, S.N., Fred, A.L., Kumar, H.A., Miriam, L.R.J., Asha, M.R.: Retinal blood vessel extraction using wavelet transform and morphological operations. Res. J. Pharm. Biol. Chem. Sci. 7(5), 1479–1487 (2016)
Ko, M.W.L., Leung, C.K.S., Yuen, T.Y.P.: Automated segmentation of optic nerve head for the topological assessment. In: Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges, GMEPE/PAHCE 2016 (2016). Article no. 7504611
Reza, M.N., Ahmad, M.: Automatic detection of optic disc in fundus images by curve operator. In: 2nd International Conference on Electrical Information and Communication Technologies, EICT 2015, pp. 143–147 (2015). Article no. 7391936
Gopinath, K., Sivaswamy, J., Mansoori, T.: Automatic glaucoma assessment from angio-OCT images. In: Proceedings - International Symposium on Biomedical Imaging, June 2016, pp. 193–196 (2016). Article no. 7493242
Karimi, S., Pourghassem, H.: Optical disc detection in retinal image based on spatial density of grayscale pixels. Int. J. Imaging Robot. 16(2), 105–117 (2016)
Claro, M., Santos, L., Silva, W., Araújo, F., Santana, A.D.A.: Automatic detection of glaucoma using disc optic segmentation and feature extraction In: Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015 (2015). Article no. 7360047
Issac, A., Sarathi, M.P., Dutta, M.K.: An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput. Methods Programs Biomed. 122(2), 229–244 (2015)
Park, H., Schoepflin, T., Kim, Y.: Active contour model with gradient directional information: directional snake. IEEE Trans. Circ. Syst. Video Technol. 11(2), 252–256 (2001)
Zhu, S., Zhou, Q., Gao, R.: A novel snake model using new multi-step decision model for complex image segmentation. Comput. Electr. Eng. 51, 58–73 (2016)
Kubicek, J., Timkovic, J., Augustynek, M., Penhaker, M., Pokrývková, M.: Optical nerve disc segmentation using circual integro differencial operator. In: Sulaiman, H.A., Othman, M.A., Othman, M.F.I., Rahim, Y.A., Pee, N.C. (eds.) Advanced Computer and Communication Engineering Technology. LNEE, vol. 362, pp. 387–396. Springer, Heidelberg (2016). doi:10.1007/978-3-319-24584-3_32
Kubicek, J., Penhaker, M., Bryjova, I., Kodaj, M.: Articular cartilage defect detection based on image segmentation with colour mapping. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 214–222. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11289-3_22
Kubicek, J., Penhaker, M., Bryjova, I., Augustynek, M.: Classification method for macular lesions using fuzzy thresholding method. In: Kyriacou, E., Christofides, S., Pattichis, Constantinos, S. (eds.) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IP, vol. 57, pp. 239–244. Springer, Heidelberg (2016). doi:10.1007/978-3-319-32703-7_48
Almazroa, A., Burman, R., Raahemifar, K., Lakshminarayanan, V.: Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey 2015. J. Ophthalmol. (2015). Article no. 180972
Augustynek, M., Penhaker, M.: Finger plethysmography classification by orthogonal transformatios. In: 2010 Second International Conference on Computer Engineering and Applications (ICCEA), vol. 2, pp. 173–177. IEEE (2010)
Penhaker, M., Stula, T., Cerny, M.: Automatic ranking of eye movement in electrooculographic records, pp. 456–460 (2010 edn.)
Partila, P., Voznak, M., Peterek, T., Penhaker, M., Novak, V., Tovarek, J., Mehic, M., Vojtech, L.: Impact of human emotions on physiological characteristics. In: SPIE (2014 edn.)
Augustynek, M., Penhaker, M.: Non invasive measurement and visualizations of blood pressure. Elektronika Ir Elektrotechnika 10, 55–58 (2011)
Krawiec, J., Penhaker, M., Krejcar, O., Novak, V., Bridzik, R.: System for storage and exchange of electrophysiological data. In: Proceedings of 5th International Conference on Systems, ICONS, pp. 11–16 (2010)
Acknowledgment
The work and the contributions were supported by the project SV4506631/2101 ‘Biomedicínské inženýrské systémy XII’. This article has been supported by financial support of TA ČR, PRE SEED Fund of VSB-Technical university of Ostrava/TG01010137.
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Kubicek, J., Timkovic, J., Penhaker, M., Augustynek, M., Bryjova, I., Kasik, V. (2017). Extraction of Optical Disc Geometrical Parameters with Using of Active Snake Model with Gradient Directional Information. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_43
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