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The coordinate system of the eye in cataract surgery: Performance comparison of the circle Hough transform and Daugman's algorithm

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dc.title The coordinate system of the eye in cataract surgery: Performance comparison of the circle Hough transform and Daugman's algorithm en
dc.contributor.author Vlachynská, Alžběta
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Sramka, Martin
dc.relation.ispartof AIP Conference Proceedings
dc.identifier.issn 0094-243X Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9780735415386
dc.date.issued 2017
utb.relation.volume 1863
dc.event.title International Conference of Numerical Analysis and Applied Mathematics 2016, ICNAAM 2016
dc.event.location Rhodes
utb.event.state-en Greece
utb.event.state-cs Řecko
dc.event.sdate 2016-09-19
dc.event.edate 2016-09-25
dc.type conferenceObject
dc.language.iso en
dc.publisher American Institute of Physics (AIP)
dc.identifier.doi 10.1063/1.4992259
dc.relation.uri http://aip.scitation.org/doi/abs/10.1063/1.4992259
dc.subject cataract surgery en
dc.subject Daugmans algorithm en
dc.subject Pupil detection en
dc.subject the circle Hough transform en
dc.description.abstract The aim of the work is to determine the coordinate system of an eye and insert a polar-axis system into images captured by a slip lamp. The image of the eye with the polar axis helps a surgeon accurately implant toric intraocular lens in the required position/rotation during the cataract surgery. In this paper, two common algorithms for pupil detection are compared: the circle Hough transform and Daugman's algorithm. The procedures were tested and analysed on the anonymous data set of 128 eyes captured at Gemini eye clinic in 2015. © 2017 Author(s). en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007301
utb.identifier.obdid 43876780
utb.identifier.scopus 2-s2.0-85026652042
utb.identifier.wok 000410159800109
utb.source d-scopus
dc.date.accessioned 2017-09-03T21:40:09Z
dc.date.available 2017-09-03T21:40:09Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Grant Agency of the Czech Republic-GACR [588 P103/15/06700S]; Internal Grant Agency of Tomas Bata University in Zlin [IGA/CebiaTech/2016/007]
utb.contributor.internalauthor Vlachynská, Alžběta
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.fulltext.affiliation Alzbeta Vlachynska 1, a) , Zuzana Kominkova Oplatkova 1, b) , Martin Sramka 2, c) 1 Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, nam. T.G. Masaryka 5555, 760 01 Zlin, Czech Republic 2 Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic a) Corresponding author: vlachynska@fai.utb.cz b) kominkovaoplatkova@fai.utb.cz c) sramkma2@fel.cvut.cz
utb.fulltext.dates -
utb.fulltext.references 1. Daugman, J.G. 1993. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol:15, is: 11 (Nov). 1148-1363 2. Wildes, R.P. 1997. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, vol. 85, is. 9 (Sep) 1348-1368. 3. Burge, M.J., Bowyer, K.W. 2013. Handbook of Iris recognition. Advances in Computer Vision and Pattern recognition, Springer-Verlag London 4. Bowyer, K.W., Hollingsworth, K., Flynn, P.J. 2008. Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding 110, 281-307 5. Lang, G.K. 2007. Ophtalmology: A short textbook. Thieme, New York, 2nd ed., rev. and enl. 604 p. 6. Basit, A. Javed, M.Y. 2007, Localization of iris in gray scale images using intensity gradient. Optics and Lasers in Engineering 45, 1107-1114 7. Jan, F., Usman, I., Agha, S. 2013. Reliable iris localization using Hough transform, histogram-bisection, and eccentricity. Signal processing 93, 230-241 8. Jeong, D.S., Hwang, J.W., Kang, B.J., Park, K.R., Won, C.S., Park, D.K., Kim, J. 2010. A new iris segmentation method for non-ideal iris images. Image and Vision Computing 28, 254-260 9. Koh, J., Govindaraju, V. Chaudhary, 2010. A robust iris localization method using an active contour model and Hough transform. 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turky, 2852-2856. 10. Li, P. Liu, L., Xiao, L. 2010. Robust and accurate iris segmentation in very noisy iris images. Image and Vision Computing 28, 246-253 11. Ren, X., Peng, Z., Zeng, Q., Peng, Ch., Zhang, J., Wu, S., Zeng, Y. 2008 An improved method for Daugman’s iris localization algorithm. Computers in Biology and Medicine vol. 38 (Sep), 111 – 115 12. Daugman, J.G. 2004. How iris recognition works. IEEE transactions on circuits and systems for video technology, vol 14, no. 1 (Jan). 21-3
utb.fulltext.sponsorship This work was done in cooperation with Gemini eye clinic in Zlin, Czech Republic. This work was also supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014), by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, further by Grant Agency of the Czech Republic—GACR 588 P103/15/06700S and by Internal Grant Agency of Tomas Bata University in Zlin under the project No. IGA/CebiaTech/2016/007.
utb.scopus.affiliation Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, nam. T.G. Masaryka 5555, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, Prague 6, Czech Republic
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