Trainable COSFIRE filters for vessel delineation with application to retinal images

Medical Image Analysis

Volumn 19, Issue 1, 2015, Pages 46-57

  Azzopardi, George ^a   Strisciuglio, Nicola ^a,b   Vento, Mario ^b   Petkov, Nicolai ^a  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

^b UNIVERSITY OF SALERNO   (Italy)

Author keywords

COSFIRE; Delineation; Retinal image analysis; Trainable filters; Vessel segmentation

Indexed keywords

DIAGNOSIS; DIGITAL STORAGE; FORESTRY; MEDICAL IMAGING; OPHTHALMOLOGY;

AUTOMATIC CONFIGURATION; AUTOMATIC SEGMENTATIONS; COSFIRE; DELINEATION; DIFFERENCE OF GAUSSIANS FILTERS; RETINAL IMAGE ANALYSIS; STATE-OF-THE-ART METHODS; VESSEL SEGMENTATION;

IMAGE SEGMENTATION;

ARTICLE; AUTOANALYSIS; DIAGNOSTIC TEST ACCURACY STUDY; EYE FUNDUS; FILTER; HUMAN; IMAGE ANALYSIS; LUMINANCE; PERCEPTIVE DISCRIMINATION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RETINA BLOOD VESSEL; RETINA IMAGE; SENSITIVITY AND SPECIFICITY; TRAINABLE FILTER; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; FLUORESCENCE ANGIOGRAPHY; IMAGE ENHANCEMENT; OBSERVER VARIATION; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; RETINA DISEASE;

DIAGNOSIS; IMAGES; PATHOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FLUORESCEIN ANGIOGRAPHY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; OBSERVER VARIATION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RETINAL DISEASES; RETINAL VESSELS; SENSITIVITY AND SPECIFICITY;

EID: 84908032530     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.08.002     Document Type: Article

References (40)

1. Abramoff, M.D., Garvin, M.K., Sonka, M., 2010. Retinal imaging and image analysis.
2. Al-Diri, B., Hunter, A., Steel, D., Habib, M., Hudaib, T., Berry, S., 2008. Review - a reference data set for retinal vessel profiles, in: EMBS 2008. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 2262-2265.
3. Al-Diri B., Hunter A., Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imag. 2009, 28:1488-1497.
4. Al-Rawi M., Qutaishat M., Arrar M. An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 2007, 37:262-267.
5. Azzopardi G., Petkov N. A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biol. Cybern. 2012, 106:177-189.
6. Azzopardi G., Petkov N. Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recogn. Lett. 2013, 34:922-933.
7. Azzopardi G., Petkov N. Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35:490-503.
8. Chauduri S., Chatterjee S., Katz N., Nelson M., Goldbaum M. Detection of blood-vessels in retinal images using two-dimensional matched-filters. IEEE Trans. Med. Imag. 1989, 8:263-269.
9. Chutatape, O., Liu Zheng, Krishnan, S., 1998. Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, in: Chang, H., Zhang, Y. (Eds.), Proc. 20th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBS'98), pp. 3144-3149.
10. Cinsdikici M.G., Aydin D. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput. Methods Prog. Biomed. 2009, 85-95.
11. Fadzil, M., Nugroho, H., Nugroho, H., Iznita, I., 2009. Contrast enhancement of retinal vasculature in digital fundus image, in: 2009 International Conference on Digital Image Processing, pp. 137-141.
12. Fang B., Hsu W., Lee M. Reconstruction of vascular structures in retinal images. Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429) 2003, vol. 3:II-157-II-160. IEEE Signal Process. Soc.
13. Fraz M., Remagnino P., Hoppe A., Uyyanonvara B., Rudnicka A., Owen C., Barman S. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 2012, 59:2538-2548.
14. Gang L., Chutatape O., Krishnan S. Detection and measurement of retinal vessels, in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans. Biomed. Eng. 2002, 49:168-172.
15. Grisan E., Foracchia M., Ruggeri A. A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans. Med. Imag. 2008, 27:310-319.
16. Harding S., Broadbent D., Neoh C., White M., Vora J. Sensitivity and specificity of photography and direct ophthalmoscopy in screening for sight threatening eye disease - the Liverpool diabetic eye study. Brit. Med. J. 1995, 311:1131-1135.
17. Heneghan C., Flynn J., O'Keefe M., Cahill M. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med. Image Anal. 2002, 6:407-429.
18. 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. 2000, 19:203-210.
19. Irvine G., Casagrande V., Norton T. Center surround relationships of magnocellular, parvocellular, and koniocellular relay cells in primate lateral geniculate-nucleus. Visual Neurosci. 1993, 10:363-373.
20. Jiang X., Mojon D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25:131-137.
21. Kong A., Zhang D., Kamel M. A survey of palmprint recognition. Pattern Recogn. 2009, 42:1408-1418.
22. Lam B., Gao Y., Liew A.C. General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans. Med. Imag. 2010, 29:1369-1381.
23. Liu I., Sun Y. Recursive tracking of vascular networks in angiograms based on the detection deletion scheme. IEEE Trans. Med. Imag. 1993, 12:334-341.
24. Marin D., Aquino A., Emilio Gegundez-Arias M., Manuel Bravo J. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imag. 2011, 30:146-158.
25. Martinez-Pérez M.E., Hughes A.D., Thom S.A., Bharath A.A., Parker K.H. Segmentation of blood vessels from red-free and fluorescein retinal images. Med. Image Anal. 2007, 11:47-61.
26. Mendonca A.M., Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imag. 2006, 25:1200-1213.
27. Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M., 2004. Comparative study of retinal vessel segmentation methods on a new publicly available database, in: Proc. of the SPIE - The International Society for Optical Engineering, pp. 648-656 (Medical Imaging 2004. Image Processing, 16-19 February 2004, San Diego, CA, USA).
28. Owen C.G., Rudnicka A.R., Mullen R., Barman S.A., Monekosso D., Whincup P.H., Ng J., Paterson C. Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program. Invest. Ophthalmol. Vis. Sci. 2009, 50:2004-2010.
29. Petkov, N., Visser, W.T., 2005. Modifications of Center-Surround, Spot Detection and Dot-Pattern Selective operators. Technical Report CS 2005-9-01. Institute of Mathematics and Computing Science, University of Groningen, The Netherlands.
30. Pizer S., Amburn E., Austin J., Cromartie R., Geselowitz A., Greer T., Ter Haar Romeny B., Zimmerman J., Zuiderveld K. Adaptative histogram equalization and its varations. Comput. Vis. Graph. Image Process. 1987, 39:355-368.
31. Ricci E., Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imag. 2007, 26:1357-1365.
32. Rodieck R.W. Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vis. Res. 1965, 5:583-601.
33. Setiawan, A., Mengko, T., Santoso, O., Suksmono, A., 2013. Color retinal image enhancement using clahe. In: 2013 International Conference on ICT for Smart Society (ICISS), pp. 1-3.
34. Soares J.V.B., Leandro J.J.G., Cesar R.M., Jelinek H.F., Cree M.J. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imag. 2006, 25:1214-1222.
35. Staal J., Abramoff M., Niemeijer M., Viergever M., van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 2004, 23:501-509.
36. Tolias Y., Panas S. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imag. 1998, 17:263-273.
37. von Wendt G., Heikkila K., Summanen P. Assessment of diabetic retinopathy using two-field 60° fundus photography. A comparison between red-free, black-and-white prints and colour transparencies. Acta Ophthalmol. Scand. 1999, 77:638-647.
38. Xu X., Bonds A., Casagrande V. Modeling receptive-field structure of koniocellular, magnocellular, and parvocellular LGN cells in the owl monkey (Aotus trivigatus). Visual Neurosci. 2002, 19:703-711.
39. Zana F., Klein J. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Med. Imag. 2001, 10:1010-1019.
40. Zhou L., Rzeszotarski M., Singerman L., Chokreff J. The detection and quantification of retinopathy using digital angiograms. IEEE Trans. Med. Imag. 1994, 13:619-626.