FIRE: Fundus Image Registration dataset
Vol. 1 No. 4 (2017), Original Articles
Vol. 1 No. 4 (2017)

FIRE: Fundus Image Registration dataset

Original Articles
https://doi.org/10.35119/maio.v1i4.42
Published 2017-07-07
Carlos Hernandez-Matas
Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece Computer Science Department, University of Crete, Heraklion, Greece
Xenophon Zabulis
Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece
Areti Triantafyllou
3rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
Panagiota Anyfanti
3rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
Stella Douma
3rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
Antonis A Argyros
Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece Computer Science Department, University of Crete, Heraklion, Greece
PDF

How to Cite

1.
Hernandez-Matas C, Zabulis X, Triantafyllou A, Anyfanti P, Douma S, Argyros AA. FIRE: Fundus Image Registration dataset. MAIO [Internet]. 2017 Jul. 7 [cited 2024 Nov. 21];1(4):16-28. Available from: https://www.maio-journal.com/index.php/MAIO/article/view/42
Vancouver

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Copyright notice

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication, with the work twelve (12) months after publication simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.

  2. After 12 months from the date of publication, authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

Keywords

benchmark; dataset; evaluation; retinal fundus images; retinal image registration

Abstract

Purpose: Retinal image registration is a useful tool for medical professionals. However, performance evaluation of registration methods has not been consistently assessed in the literature. To address that, a dataset comprised of retinal image pairs annotated with ground truth and an evaluation protocol for registration methods is proposed.

Methods: The dataset is comprised by 134 retinal fundus image pairs. These pairs are classified into three categories, according to characteristics that are relevant to indicative registration applications. Such characteristics are the degree of overlap between images and the presence/absence of anatomical differences. Ground truth in the form of corresponding image points and a protocol to evaluate registration performance are provided.

Results: The proposed protocol is shown to enable quantitative and comparative evaluation of retinal registration methods under a variety of conditions.

Conclusion: This work enables the fair comparison of retinal registration methods. It also helps researchers to select the registration method that is most appropriate given a specific target use.

https://doi.org/10.35119/maio.v1i4.42
PDF

References

Abramoff MD, Garvin MK, Sonka M. Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 2010;3, 169–208. issn: 1937-3333. doi: 10.1109/RBME.2010.2084567.

Grosso A, Veglio F, Porta M, Grignolo FM, Wong TY. Hypertensive retinopathy revisited: some answers, more questions. British Journal of Ophthalmology, 2005;89(12): 1646–1654. doi:10.1136/bjo.2005.072546.

Meitav N, Ribak EN. Improving retinal image resolution with iterative weighted shift-and-add. Journal of the Optical Society of America A, 2011;28(7): 1395–1402. doi: 10.1364/JOSAA.28.001395.

Hernandez-Matas C, Zabulis X. Super resolution for fundoscopy based on 3D image registration. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014; 6332–6338. doi: 10.1109/EMBC.2014.6945077.

Molodij G, Ribak E, Glanc M, Chenegros G. Enhancing retinal images by extracting structural information. Optics Communications, 2014;313, 321 –328. issn: 0030-4018. doi:10.1016/j.optcom.2013.10.011.

Can A, Stewart CV, Roysam B, Tanenbaum HL. A feature-based technique for joint, linear estimation of high-order image-to-mosaic transformations: mosaicing the curved human retina. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002;24(3): 412–419. issn: 0162-8828. doi: 10.1109/34.990145.

Ryan N, Heneghan C, Chazal P de. Registration of digital retinal images using landmark correspondence by expectation maximization. Image and Vision Computing, 2004;22(11): 883 –898. issn: 0262-8856. doi: 10.1016/j.imavis.2004.04.004.

Cattin PC, Bay H, Van Gool L, Székely G. Retina Mosaicing Using Local Features. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II. Ed. by R Larsen, M Nielsen, J Sporring. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006; 185–192. isbn: 978-3-540-44728-3. doi: 10.1007/11866763_23.

Narasimha-Iyer H, Can A, Roysam B, Tanenbaum HL, Majerovics A. Integrated Analysis of Vascular and Nonvascular Changes From Color Retinal Fundus Image Sequences. IEEE Transactions on Biomedical Engineering, 2007;54(8): 1436–1445. issn: 0018-9294. doi:10.1109/TBME.2007.900807.

Troglio G, Alberti M, Benediksson JA, Moser G, Serpico SB, Stefánsson E. Unsupervised Change-Detection in Retinal Images by a Multiple-Classifier Approach. Multiple Classifier Systems: 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings. Ed. by N El Gayar, J Kittler, F Roli. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010; 94–103. isbn: 978-3-642-12127-2. doi:10.1007/978-3-642-12127-2_10.

Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, et al. An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation. IEEE Transactions on Biomedical Engineering, 2012;59(9): 2538–2548. issn: 0018-9294. doi: 10.1109/TBME.2012.2205687.

CHASEDB1 Retinal Image Database. [Online; accessed 12 July 2016]. Available from: https : / /blogs.kingston.ac.uk/retinal/chasedb1/

Carmona EJ, Rincón M, García-Feijoó J, Casa JM Martínez-de-la. Identification of the Optic Nerve Head with Genetic Algorithms. Artificial Intelligence in Medicine, July 2008;43(3): 243–259. issn: 0933-3657. doi: 10.1016/j.artmed.2008.04.005.

DRIONS-DB: Digital Retinal Images for Optic Nerve Segmentation Database. [Online; accessed 12 July 2016]. Available from: http://www.ia.uned.es/~ejcarmona/DRIONS-DB.html.

Sivaswamy J, Krishnadas SR, Joshi GD, Jain M, Tabish AUS. Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). 2014; 53–56. doi: 10.1109/ISBI.2014.6867807.

Drishti-GS Dataset. [Online; accessed 12 July 2016]. Available from: http://cvit.iiit.ac.in/projects/mip/drishti-gs.

Staal J, Abramoff MD, Niemeijer M, Viergever MA, Ginneken B van. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 2004;23(4): 501–509. issn: 0278-0062. doi: 10.1109/TMI.2004.825627.

DRIVE: Digital Retinal Images for Vessel Extraction. [Online; accessed 12 July 2016]. Available from: http://www.isi.uu.nl/Research/Databases/DRIVE/.

Odstrcilík J, Jan J, Gazárek J, Kolár R. Improvement of Vessel Segmentation by Matched Filtering in Colour Retinal Images. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany: Vol. 25/11 Biomedical Engineering for Audiology, Ophthalmology, Emergency&Dental Medicine. Ed. by O Dössel, WC Schlegel. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009; 327–330. isbn: 978-3-642-03891-4. doi: 10.1007/978-3-642-03891-4_87.

HRF: High-Resolution Fundus Image Database. [Online; accessed 12 July 2016]. Available from: https://www5.cs.fau.de/research/data/fundus-images/.

Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, et al. Feedback on a publicly distributed image database: the MESSIDOR database. Image Analysis & Stereology, 2014;33(3): 231–234. issn: 1854-5165. doi: 10.5566/ias.1155.

MESSIDOR: Methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology. [Online; accessed 12 July 2016]. Available from: http://www.adcis.net/en/Download-Third-Party/Messidor.html.

Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, et al. Optic nerve head segmentation. IEEE Transactions on Medical Imaging, 2004;23(2): 256–264. issn: 0278-0062. doi:10.1109/TMI.2003.823261.

ONHSD: Optic Nerve Head Segmentation Dataset. [Online; accessed 12 July 2016]. Available from: http://reviewdb.lincoln.ac.uk/Image%20Datasets/ONHSD.aspx.

Al-Diri B, Hunter A, Steel D, Habib M, Hudaib T, Berry S. REVIEW - A reference data set for retinal vessel profiles. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2008; 2262–2265. doi: 10.1109/IEMBS.2008.4649647.

REVIEW: Retinal Vessel Image set for Estimation of Widths. [Online; accessed 12 July 2016]. Available from: http://reviewdb.lincoln.ac.uk/REVIEWDB/REVIEWDB.aspx.

Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, et al. DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms. Available from: http://www.it.lut.fi/project/imageret/diaretdb0/doc/diaretdb0_techreport_v_1_1.pdf.

DIARETDB0: Standard Diabetic Retinopathy Database Calibration level 0. [Online; accessed 12 July 2016]. Available from: http://www2.it.lut.fi/project/imageret/diaretdb0/.

Niemeijer M, Ginneken B van, Cree MJ, Mizutani A, Quellec G, Sanchez CI, et al. Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 2010;29(1): 185–195. issn: 0278-0062. doi: 10.1109/TMI.2009.2033909.

ROC: Retinopathy Online Challenge Database. [Online; accessed 12 July 2016]. Available from: http://webeye.ophth.uiowa.edu/ROC/.

Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Raninen A, et al. DIARETDB1 diabetic retinopathy database and evaluation protocol. Available from: http://www.it.lut.fi/project/imageret/diaretdb1/doc/diaretdb1_techreport_v_1_1.pdf.

DIARETDB1: Standard Diabetic Retinopathy Database Calibration level 1. [Online; accessed 12 July 2016]. Available from: http://www2.it.lut.fi/project/imageret/diaretdb1/

Decencière E, Cazuguel G, Zhang X, Thibault G, Klein JC, Meyer F, et al. TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM, 2013;34(2): Special issue: ANR TECSAN: Technologies for Health and Autonomy, 196 –203. issn: 1959-0318. doi: 10.1016/j.irbm.2013.01.010.

e-ophtha: A Color Fundus Image Database. [Online; accessed 12 July 2016]. Available from: http://www.adcis.net/en/Download-Third-Party/E-Ophtha.html.

Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Transactions on Medical Imaging, 2003;22(8): 951–958. issn: 0278-0062. doi:10.1109/TMI.2003.815900.

STARE: Structured Analysis of the Retina. [Online; accessed 12 July 2016]. Available from: http://cecas.clemson.edu/~ahoover/stare/index.html.

Niemeijer M, Xu X, Dumitrescu AV, Gupta P, Ginneken B van, Folk JC, et al. Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 2011;30(11): 1941–1950. issn: 0278-0062. doi: 10.1109/TMI.2011.2159619.

INSPIRE: Iowa Normative Set for Processing Images of the Retina. [Online; accessed 12 July 2016]. Available from: http://www.medicine.uiowa.edu/eye/Datasets/.

Vázquez SG, Cancela B, Barreira N, Penedo MG, Rodríguez-Blanco M, Pena Seijo M, et al. Improving retinal artery and vein classification by means of a minimal path approach. Machine Vision and Applications, 2013;24(5): 919–930. issn: 1432-1769. doi: 10.1007/s00138-012-0442-4.

VICAVR Database. [Online; accessed 12 July 2016]. Available from: http://www.varpa.es/vicavr.html.

Ortega M, Penedo MG, Rouco J, Barreira N, Carreira MJ. Retinal Verification Using a Feature Points based Biometric Pattern. EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective, Jan. 2009;2009, 2:1–2:13. issn: 1110-8657. doi:0.1155/2009/235746.

VARIA. [Online; accessed 12 July 2016]. Available from: http://www.varpa.es/varia.html.

FIRE: Fundus Image Registration Dataset. [Available upon acceptance]. Available from: http://www.ics.forth.gr/cvrl/fire.

Adal KM, Etten PG van, Martinez JP, Vliet LJ van, Vermeer KA. Accuracy asessment of intra- and intervisit fundus image registration for diabetic retinopathy screening. Investigative Ophthalmology & Visual Science, 2015;56(3): 1805–1812. doi: 10.1167/iovs.14-15949.

RODREP: Rotterdam Ophthalmic Data Repository Longitudinal diabetic retinopathy screening data. [Online; accessed 12-July-2016]. Available from: http://www.rodrep.com/longitudinal-diabetic-retinopathy-screening---description.html.

Stewart CV, Tsai CL, Roysam B. The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Transactions on Medical Imaging, 2003;22(11): 1379–1394. issn: 0278-0062. doi: 10.1109/TMI.2003.819276.

Tsai CL, Li CY, Yang G, Lin KS. The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Registration of Multimodal Fluorescein Angiogram Sequence. IEEE Transactions on Medical Imaging, 2010;29(3): 636–649. issn: 0278-0062. doi: 10.1109/TMI.2009.2030324.

Chen J, Tian J, Lee N, Zheng J, Smith RT, Laine AF. A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration. IEEETransactionsonBiomedical Engineering, 2010;57(7): 1707–1718. issn: 0018-9294. doi: 10.1109/TBME.2010.2042169.

Zheng J, Tian J, Deng K, Dai X, Zhang X, Xu M. Salient Feature Region: A NewMethod for Retinal Image Registration. IEEE Transactions on Information Technology in Biomedicine, 2011;15(2): 221–232. issn:1089-7771. doi: 10.1109/TITB.2010.2091145.

Ghassabi Z, Shanbehzadeh J, Sedaghat A, Fatemizadeh E. An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors. EURASIP Journal on Image and Video Processing, 2013;2013(1): 1–16. issn: 1687-5281. doi: 10.1186/1687-5281-2013-25.

Hernandez M, Medioni G, Hu Z, Sadda S. Multimodal Registration of Multiple Retinal Images Based on Line Structures. 2015 IEEE Winter Conference on Applications of Computer Vision. 2015; 907–914. doi:10.1109/WACV.2015.125.

Hernandez-Matas C, Zabulis X, Argyros AA. Retinal image registration based on keypoint correspondences, spherical eye modeling and camera pose estimation. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015; 5650–5654. doi: 10.1109/EMBC.2015.7319674.

Hernandez-Matas C, Zabulis X, Triantafyllou A, Anyfanti P, ArgyrosAA. Retinal image registration under the assumption of a spherical eye. Computerized Medical Imaging and Graphics, 2016; –. issn: 0895-6111. doi: 10.1016/j.compmedimag.2016.06.006.

Hernandez-Matas C, Zabulis X, Argyros AA. Retinal image registration through simultaneous camera pose and eye shape estimation. To appear, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016; –.

Lin Y, Medioni G. Retinal image registration from 2D to 3D. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. 2008; 1–8. doi: 10.1109/CVPR.2008.4587705.

Legg P, Rosin P, Marshall D, Morgan J. Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation. Computerized Medical Imaging and Graphics, 2013;37(7–8): 597 –606. issn: 0895-6111. doi: 10.1016/j.compmedimag.

08.004.

Reel PS, Dooley LS, Wong KCP, Börner A. Robust retinal image registration using expectation maximisation with mutual information. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2013; 1118–1122. doi: 10.1109/ICASSP.2013.6637824.

Gharabaghi S, Daneshvar S, Sedaaghi MH. Retinal Image Registration Using Geometrical Features. Journal of Digital Imaging, 2013;26(2): 248–258. issn: 1618-727X. doi: 10.1007/s10278-012-9501-7.

Perez-Rovira A, Cabido R, Trucco E, McKenna SJ, Hubschman JP. RERBEE: Robust Efficient Registration via Bifurcations and Elongated Elements Applied to Retinal Fluorescein Angiogram Sequences. IEEE Transactions on Medical Imaging, 2012;31(1): 140–150. issn: 0278-0062. doi: 10.1109/TMI.2011.2167517.

Lowe DG. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 2004;60(2): 91 –110. issn: 1573-1405. doi: 10.1023/B:VISI.0000029664.99615.94.

Bay H, Ess A, Tuytelaars T, Gool LV. Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding, 2008;110(3): Similarity Matching in Computer Vision and Multimedia, 346 –359. issn:1077-3142. doi: 10.1016/j.cviu.2007.09.014.

Babenko B, Yang MH, Belongie S. Robust Object Tracking with Online Multiple Instance Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011;33(8): 1619–1632. issn: 0162-8828. doi: 10.1109/TPAMI.2010.226.

Yang G, Stewart CV, Sofka M, Tsai CL. Registration of Challenging Image Pairs: Initialization, Estimation, and Decision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007;29(11): 1973–1989. issn: 0162-8828. doi: 10.1109/TPAMI.2007.1116.

PDF
1