nach oben

Journal of Imaging Informatics in Medicine

Erschienen in:

01.08.2010

Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis

verfasst von: Rangaraj M. Rangayyan, Xiaolu Zhu, Fábio J. Ayres, Anna L. Ells

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 4/2010

Einloggen, um Zugang zu erhalten

Abstract

We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.

Youssif AAHAR, Ghalwash AZ, Ghoneim AASAR: Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imag 27(1):11–18, 2008CrossRef

Walter T, Klein JC: Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques. Medical Data Analysis, Lecture Notes in Computer Science, Volume 2199, Springer: Berlin, Pages 282-287, 2001

Zhu X, Rangayyan RM, Ells AL: Detection of the optic disc in fundus images of the retina using the Hough transform for circles. J Digit Imaging, in press, 2009

Acharya R, Tan W, Yun WL, Ng EYK, Min LC, Chee C, Gupta M, Nayak J, Suri JS: The human eye. In: Acharya R, Ng EYK, Suri JS Eds. Image Modeling of the Human Eye. Norwood: Artech House, 2008, pp. 1–35

Michaelson IC, Benezra D: Textbook of the Fundus of the Eye, 3rd edition. Edinburgh: Churchill Livingstone, 1980

Park M, Jin JS, Luo S: Locating the optic disc in retinal images. In: Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation, page 5, Sydney, QLD, Australia, July, 2006. IEEE

Tobin KW, Chaum E, Govindasamy VP, Karnowski TP: Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imag 26(12):1729–1739, 2007CrossRef

Li H, Chutatape O: Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 51(2):246–254, 2004CrossRefPubMed

Fleming AD, Goatman KA, Philip S, Olson JA, Sharp PF: Automatic detection of retinal anatomy to assist diabetic retinopathy screening. Phys Med Biol 52:331–345, 2007CrossRefPubMed

10.

Early Treatment Diabetic Retinopathy Study Research Group: Grading diabetic retinopathy from stereoscopic color fundus photographs—An extension of the modified Airlie House classification (ETDRS report number 10). Ophthalmology 98:786–806, 1991

11.

Sun H, Nathans J: The challenge of macular degeneration. Sci Am 285(4):68–75, 2001CrossRefPubMed

12.

Ells A, Holmes JM, Astle WF, Williams G, Leske DA, Fielden M, Uphill B, Jennett P, Hebert M: Telemedicine approach to screening for severe retinopathy of prematurity: A pilot study. Ophthalmology 110(11):2113–2117, 2003CrossRefPubMed

13.

Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ: Retinal image analysis: Concepts, applications and potential. Prog Retin Eye Res 25(1):99–127, 2006CrossRefPubMed

14.

Osareh A, Mirmehd M, Thomas B, Markham R: Comparison of colour spaces for optic disc localisation in retinal images. In: Proceedings 16th International Conference on Pattern Recognition. Quebec City, Quebec, Canada, 2002, pp 743–746

15.

Hoover A, Kouznetsova V, Goldbaum M: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imag 19(3):203–210, 2000CrossRef

16.

Hoover A, Goldbaum M: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imag 22(8):951–958, 2003CrossRef

17.

Structured Analysis of the Retina, http://www.ces.clemson.edu/˜ahoover/stare/, accessed on March 24, 2008

18.

Staal J, Abràmoff MD, Niemeijer M, Viergever MA, van Ginneken B: Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imag 23(4):501–509, 2004CrossRef

19.

DRIVE: Digital Retinal Images for Vessel Extraction, http://www.isi.uu.nl/Research/Databases/DRIVE/, accessed on March 24, 2008

20.

ter Haar F: Automatic localization of the optic disc in digital colour images of the human retina. Master’s thesis, Utrecht University, Utrecht, the Netherlands, 2005

21.

Lalonde M, Beaulieu M, Gagnon L: Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imag 20(11):1193–1200, 2001CrossRef

22.

Barrett SF, Naess E, Molvik T: Employing the Hough transform to locate the optic disk. Biomed Sci Instrum 37:81–86, 2001PubMed

23.

Chrástek R, Skokan M, Kubecka L, Wolf M, Donath K, Jan J, Michelson G, Niemann H: Multimodal retinal image registration for optic disk segmentation. In: Methods of Information in Medicine, German BVM-Workshop on Medical Image Processing, volume 43. Germany: Schattauer GmbH, 2004, pp 336–342

24.

Chrástek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, Lausen B, Lämmer R, Mardin CY, Michelson G: Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Image Anal 9(4):297–314, 2005CrossRefPubMed

25.

Zhu X, Rangayyan RM: Detection of the optic disc in images of the retina using the Hough transform. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver, BC, Canada, August 20–24, 2008. IEEE, pages 3546–3549

26.

Sekhar A, Al-Nuaimy W, Nandi AK: Automated localisation of retinal optic disk using Hough transform. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2008, 2008, pp 1577–1580

27.

Sinthanayothin C, Boyce JF, Cook HL, Williamson TH: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83(4):902–910, 1999CrossRefPubMed

28.

Carmona EJ, Rincon M, García-Feijoó J, Martínez de-la Casa JM: Identification of the optic nerve head with genetic algorithms. Artif Intell Med 43(3):243–259, 2008CrossRefPubMed

29.

Hussain AR: Optic nerve head segmentation using genetic active contours. In: Proceeding International Conference on Computer and Communication Engineering. Kuala Lumpur, Malaysia, 2008, IEEE, pp 783–787

30.

Foracchia M, Grisan E, Ruggeri A: Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imag 23(10):1189–1195, 2004CrossRef

31.

Kim SK, Kong HJ, Seo JM, Cho BJ, Park KH, Hwang JM, Kim DM, Chung H, Kim HC: Segmentation of optic nerve head using warping and RANSAC. In: Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, France, 2007, IEEE, pp 900–903

32.

Ying H, Zhang M, Liu JC: Fractal-based automatic localization and segmentation of optic disc in retinal images. In: Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, France, 2007, IEEE, pp 4139–4141

33.

Park J, Kien NT, Lee G: Optic disc detection in retinal images using tensor voting and adaptive mean-shift. In: IEEE 3rd International Conference on Intelligent Computer Communication and Processing, ICCP. Romania: Cluj-Napoca, 2007, pp 237–241

34.

Rangayyan RM, Ayres FJ, Oloumi F, Oloumi F, Eshghzadeh-Zanjani P: Detection of blood vessels in the retina with multiscale Gabor filters. J Electron Imaging 17(2):023018:1–7, 2008

35.

Rao AR, Jain RC: Computerized flow field analysis: Oriented texture fields. IEEE Trans Pattern Anal Mach Intell 14(7):693–709, 1992CrossRef

36.

Rangayyan RM, Zhu X, Ayres FJ: Detection of the optic disc in images of the retina using Gabor filters and phase portrait analysis. In: Vander Sloten J, Verdonck P, Nyssen M, Haueisen J Eds. IFMBE Proceedings 22: Proceedings of 4th European Congress for Medical and Biomedical Engineering. Belgium: Antwerp, 2008, pp 468–471

37.

Image Processing and Analysis in Java, http://rsbweb.nih.gov/ij/, accessed on September 3, 2008

38.

Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. Upper Saddle River: Prentice Hall, 2002

39.

Soares JVB, Leandro JJG, Cesar Jr, RM, Jelinek HF, Cree MJ: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imag 25(9):1214–1222, 2006CrossRef

40.

Rangayyan RM, Ayres FJ: Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput 44(10):883–894, 2006CrossRefPubMed

41.

Rangayyan RM: Biomedical Image Analysis, Boca Raton: CRC, 2005

42.

Ayres FJ, Rangayyan RM: Design and performance analysis of oriented feature detectors. J Electron Imaging 16(2):023007:1–12, 2007

43.

Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842, 1996CrossRef

44.

Swanson C, Cocker KD, Parker KH, Moseley MJ, Fielder AR: Semiautomated computer analysis of vessel growth in preterm infants without and with ROP. Br J Ophthalmol 87(12):1474–1477, 2003CrossRefPubMed

45.

Ayres FJ, Rangayyan RM: Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model. Int J Comput Assist Radiol Surg 1(6):361–369, 2007CrossRef

46.

Wylie CR, Barrett LC: Advanced Engineering Mathematics, 6th edition. New York: McGraw-Hill, 1995

47.

Ayres FJ, Rangayyan RM: Characterization of architectural distortion in mammograms. IEEE Eng Med Biol Mag 24(1):59–67, 2005CrossRefPubMed

48.

Egan JP, Greenberg GZ, Schulman AI: Operating characteristics, signal detectability, and the method of free response. J Acoust Soc Am 33(8):993–1007, 1961CrossRef

49.

Edwards DC, Kupinski MA, Metz CE, Nishikawa RM: Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med Phys 29:2861–2870, 2002CrossRefPubMed

Titel: Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis
verfasst von: Rangaraj M. Rangayyan
Xiaolu Zhu
Fábio J. Ayres
Anna L. Ells
Publikationsdatum: 01.08.2010
Verlag: Springer-Verlag
Erschienen in: Journal of Imaging Informatics in Medicine / Ausgabe 4/2010
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-009-9261-1

Neu im Fachgebiet Radiologie

23.04.2024 | Pankreaskarzinom | Nachrichten

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis

Abstract

Neu im Fachgebiet Radiologie

S3-Leitlinie zu Pankreaskrebs aktualisiert

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Update Radiologie

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 4/2010

Analysis of the Glomerular Basement Membrane in Images of Renal Biopsies Using the Split-and-Merge Method: A Pilot Study

Web-based Training Method for Interpretation of Dental Images

Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images

A Method for Assessing 3D Shape Variations of Fuzzy Regions and its Application on Human Bony Orbits

Customization of Medical Report Data

The Magical iPad

Neu im Fachgebiet Radiologie

S3-Leitlinie zu Pankreaskrebs aktualisiert

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

Wie toxische Männlichkeit der Gesundheit von Männern schadet

Update Radiologie