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Journal of Imaging Informatics in Medicine

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01-08-2011

_{An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection}

Authors: Marwan D. Saleh, C. Eswaran, Ahmed Mueen

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2011

Abstract

This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.

Kanski JJ: Clinical Ophthalmology: A Systematic Approach. Butterworth-Heinemann, London, UK, 1989

Sussman EJ, Tsiaras WG, Soper KA: Diagnosis of diabetic eye disease. J Am Med Assoc 247:3231–3234, 1982CrossRef

Lee SJ, McCarty CA, Taylor HR, Keeffe JE: Costs of mobile screening for diabetic retinopathy: A practical framework for rural populations. Aust J Rural Health 8:186–192, 2001CrossRef

Taylor HR, Keeffe JE: World blindness: A 21st century perspective. Brit J Ophthalmol 85:261–266, 2001CrossRef

Streeter L, Cree MJ: Microaneurysm detection in colour fundus images. In: Image and Vision Computing. New Zealand, Palmerston North, New Zealand, Nov. 2003, pp. 280–284

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

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

Chaudhuri 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 8:263–269, 1989CrossRef

Tolias Y, Panas S: A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imag 17:263–273, 1998CrossRef

10.

Sun Y: Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm. IEEE Trans Med Imag 8:78–88, 1989CrossRef

11.

Tamura S, Okamoto Y, Yanashima K: Zero-crossing interval correction in tracing eye-fundus blood vessels. Pattern Recognit 21(3):227–233, 1988CrossRef

12.

Tamura S, Tanaka K, Ohmori S, Okazaki K, Okada A, Hoshi M: Semiautomatic leakage analyzing system for time series fluorescein ocular fundus angiography. Pattern Recognit 16(2):149–162, 1983CrossRef

13.

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 25(1):131–137, 2003CrossRef

14.

Thackray BD, Nelson AC: Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Trans Med Imag 12:385–392, 1993CrossRef

15.

Klein AK, Lee F, Amini A: Quantitive coronary angiography with deformable spline models. IEEE Trans Med Imag 16:468–482, 1997CrossRef

16.

Nekovei R, Sun Y: Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans Neural Networks 6:64–72, 1995CrossRef

17.

18.

Ritchings RT, Colchester ACF: Detection of abnormalities on carotid angiograms. Pattern Recogn Lett 4:367–374, 1986CrossRef

19.

Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE: Contrast-limited adaptive histogram equalization: Speed and effectiveness. In: Proc. of the 1st Conf. on Visualization in Biomedical Computing, 1990, pp 337–345

20.

Ridler TW, Calvard: Picture thresholding using an iterative selection method. In: Proc. IEEE Trans. On Systems, Man, Cybernetics, vol. SMC-8, 1978, pp 630–632

21.

Niemeijer M, van Ginneken B: 2002 [Online]. Available: http://www.isi.uu.nl/Reseach/Databases/DRIVE/results.php

22.

Leandro JJG, Soares JVB, Cesar RM Jr., Jelinek HF: Blood vessels segmentation in non-mydriatic images using wavelets and statistical classifiers. In: Proc. of the 16th Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), 2003, pp 262–269

23.

Salem NM, Nandi AK: Novel and adaptive contribution of the red channel in pre-processing of colour fundus images. J Franklin Inst 344:243–256, 2007CrossRef

24.

Jain AK: Fundamental of digital image processing. Prentice Hall, 1989, ISBN: 0133325764

25.

Hossain F, Alsharif MR: Image enhancement based on logarithmic transform coefficient and adaptive histogram equalization. In: Proc. of Int. Conf. on Convergence Information Technology. 2007, pp 1439–1444

26.

The MathWorks, Inc, (1994–2010). The Matlab package, [online]. Available: http://www.mathworks.com/ [2008, October 18]

27.

Papadopoulos A, Fotiadis DI, Costaridou L: Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. J Comput Biol Med 38:1045–1055, 2008CrossRef

28.

Costa LF, Cesar RM Jr: Shape analysis and classification: Theory and practice. Boca Raton: CRC Press, 2001, ISBN 0-8493-3493-4

29.

Kwan HK: Fuzzy filters for noisy image filtering. In: Proc. Int. Sym. on Circuits and Systems (ISCAS), vol. 4, 2003, pp 161–164

30.

Gonzalez RC, Woods RE: Digital Image Processing, 2nd edition. Englewood Cliffs, NJ: Prentice hall, 2002. ISBN: 0201180758

31.

Mendonca AM: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. In: Proc. IEEE Trans. on Med. Imag., vol. 25, no. 9, pp. 1200–1213, 2006

32.

Metz CE: Basic principles of ROC analysis. Semin Nucl Med 8(4):283–298, 1978PubMedCrossRef

33.

Cohen J: A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46, 1960CrossRef

34.

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

35.

Niemeijer M, Staal J, Van Ginneken B, Loog M, Abramoff MD: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick M, Sonka M Eds. Proc. SPIE Med. Image, vol. 5370, 2004, pp. 648–656

36.

Reza AW, Eswaran C, Hati S: Diabetic retinopathy: A quadtree based blood vessel detection algorithm using RGB components in fundus images. J Med Syst 32(2):147–155, 2008PubMedCrossRef

Title: An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection
Authors: Marwan D. Saleh
C. Eswaran
Ahmed Mueen
Publication date: 01-08-2011
Publisher: Springer-Verlag
Published in: Journal of Imaging Informatics in Medicine / Issue 4/2011
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-010-9302-9

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_{An Automated Blood Vessel Segmentation Algorithm Using Histogram Equalization and Automatic Threshold Selection}

Abstract

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

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