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

Vlachynská, Alžběta; Komínková Oplatková, Zuzana; Sramka, Martin

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	978-0-7354-1538-6
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