A generalized method for the detection of vascular structure in pathological retinal images

• Autorzy: Kaur J. , Mittal D.

Identyfikatory

DOI

10.1016/j.bbe.2016.09.002

• Języki publikacji: EN

Abstrakty

EN

Variations in blood vasculature morphology of retinal fundus images is one of the dominant characteristic for the early detection and analysis of retinal abnormalities. Therefore the accurate interpretation of blood vasculature is useful for ophthalmologists to diagnose patients that suffer from retinal abnormalities. A generalized method to detect and segment blood vasculature using retinal fundus images has been proposed in this work using (i) preprocessing for quality improvement of retinal fundus images, (ii) initial segmentation of vasculature map to find vascular and non vascular structures, (iii) extraction of relevant set of geometrical based features from the vasculature map and intensity based features from original retinal fundus image that differentiate vascular and non vascular structures efficiently, (iv) supervised classification of vascular and non vascular structures using the extracted features, and (v) joining of candidate vascular structures to create connectivity. The proposed method is evaluated on clinically acquired dataset and different publically available standard datasets such as DRIVE, STARE, ARIA and HRF. The clinically acquired dataset consists of 468 retinal fundus images comprising of healthy images, images with mild, intermediate and severe pathologies. Test results of the proposed method shows average sensitivity/specificity/accuracy of 85.43/97.94/95.45 on the 785 retinal fundus images. The proposed method shows an improvement of 14.01% in sensitivity without degrading specificity and accuracy in comparison to the recently published methods.

Słowa kluczowe

EN

blood vasculature; retinal fundus image; segmentation; neural network; geometrical features; intensity features

PL

naczynie krwionośne; segmentacja obrazu; sieć neuronowa; cecha geometryczna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 1
• Strony: 184--200
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Kaur J.

• Electrical and Instrumentation Engineering Department, Thapar University, Patiala 147004, India

autor

Mittal D.

• Electrical and Instrumentation Engineering Department, Thapar University, Patiala 147004, India

Bibliografia

• [1] National programme for control of blindness, Directorate general of health services (DGHS), Chandigarh: Government of Punjab; 2010. [Online]. Available: http://pbhealth.gov.in/pdf/Blindness.pdf.
• [2] Bresnick GH, Mukamel DB, Dickinson JC, Cole DR. A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. Ophthalmology 2000;107(1):19–24.
• [3] Niemeijer M, van Ginneken B, Staal JJ, Suttorp-Schulten MSA, Abramoff MD. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imag 2005 May;24(5):584–92.
• [4] 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 Mar;8(3):263–9.
• [5] 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 Mar;19 (3):203–10.
• [6] Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imag 2004 Apr;23 (4):501–9.
• [7] Soares JVB, Leandro JJG, Cesar Jr RM, Jelinek HF, Cree MJ. Retinal vessel segmentation using the 2D Gabor wavelet and supervised classification. IEEE Trans Med Imag 2006 Sep;25(9):1214–22.
• [8] Mendonça AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imag 2006 Sep;25(9):1200–13.
• [9] Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imag 2007;26:1357–65.
• [10] Martinez-Perez ME, Hughes AD, Thom SA, Bharath AA, Parker KH. Segmentation of blood vessels from red-free and uorescein retinal images. Med Image Anal 2007;11:47–61.
• [11] Zhang L, Li Q, You J, Zhang D. A modified matched filter with double-sided thresholding for screening proliferative diabetic retinopathy. IEEE Trans Inf Technol Biomed 2009;13:528–34.
• [12] Zhang B, Zhang L, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 2010;40:438–45.
• [13] Lupascu CA, Tegolo D, Trucco E. Retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 2010;14:1267–74.
• [14] Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, wen CG, Barman SA. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 2012;59:2538–48.
• [15] Li Q, You J, Zhang D. Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Syst Appl 2012;39:7600–10.
• [16] Wang Y, Ji G, Lin P, Trucco E. Retinal vessel segmentation using multi wavelet kernels and multiscale hierarchical decomposition. Pattern Recogn 2013;46:2117–33.
• [17] Nguyen UTV, Bhuiyan A, Park LAF, Ramamohanarao K. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn 2013;46:703–15.
• [18] Salazar-Gonzalez A, Kaba D, Liu X. Segmentation of the blood vessels and optic disc in retinal images. IEEE J Biomed Health Infr 2014;18(6):1874–86.
• [19] Rahebi J, Hardalac F. Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J Med Syst 2014;38(8):85. 1-12.
• [20] Franklin SW, Rajan SE. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern Biomed Eng 2014;34:117–24.
• [21] Zhao YQ, Wang XH, Wang XF, Shih FY. Retinal vessels segmentation based on level set and region growing. Pattern Recogn 2014;47:2437–46.
• [22] Zhao Y, Rada L, Chen K, Harding SP, Zheng Y. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Trans Med Imag 2015 Sep;34(9):1797–807.
• [23] Roychowdhury S, Koozekanani DD, Parhi KK. Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 2015;62(7):1738–49.
• [24] Imani E, Javidi M, Pourreza HR. Improvement of retinal blood vessel detection using morphological component analysis. Comput Methods Prog Biomed 2015;118:263–79.
• [25] Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 2015;149:708–17.
• [26] 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(1):131–7.
• [27] Sofka M, Stewart CV. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans Med Imag 2006;25(12):1531–46.
• [28] Zhou L, Rzeszotarski MS, Singerman LJ, Chokreff JM. The detection and quantification of retinopathy using digital angiograms. IEEE Trans Med Imag 1994;13(4):619–26.
• [29] Chutatape O, Zheng L, Krishnan S, Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, IEEE Int. Conf. on Eng. in Med. and Bio. Society 6, pp. 3144–3149.
• [30] Tolias YA, Panas SM. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imag 1998;17(4):263–73.
• [31] Can A, Shen H, Turner JN, Tanenbaum HL, Roysam B. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans Inform Technol Biomed 1999;3 (2):125–38.
• [32] You X, Peng Q, Yaun Y, Cheng Y, Lei J. Segmentation of retinal blood vessels sing the radial projection and semi-supervised approach. Pattern Recogn 2011;44: 2314–24.
• [33] Cinsdikici MG, Aydin D. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput Methods Prog Biomed 2009;96:85–95.
• [34] Yao C, Chen HJ. Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-otsu algorithm. J Central South Univ Technol 2009;16:640–6.
• [35] Mittal D, Kumar V, Saxena SC, Khandelwal N, Kalra N. Neural network based focal liver lesion diagnosis using ultrasound images. Int J Comput Med Imaging Graph 2011;35(4):315–23.
• [36] Mittal D, Kumari K. Automated detection and segmentation of drusen in retinal fundus images. Comput Elec Eng 2015;47:82–95.
• [37] Kaur J, Mittal D. Segmentation and measurement of exudates in fundus images of the retina for detection of retinal disease. J Biomed Eng Med Imag 2015;2(1):27–38.
• [38] Chugh S, Kaur J, Mittal D. Exudates segmentation in retinal fundus images for the detection of diabetic retinopathy. Int J Eng Res Technol 2014;3(10):673–7.
• [39] Goldbaum MDM, STARE Dataset, Clemson University, Clemson, SC, USA, 1975, [Online]. Available: http://www. ces.clemson.edu.
• [40] Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B, Digital Retinal Image for Vessel Extraction (DRIVE) Database, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands, 2004, [Online]. Available: http://www.isi.uu.nl/Research/Databases/ DRIVE/.
• [41] ARIA database, [Online]. Available: http://www.eyecharity. com/aria_online.
• [42] Rogowska J, Preston K, Sashin D. Evaluation of digital unsharp masking and local contrast stretching as applied to chest radiographs. IEEE Trans Biomed Eng 1988;35(10):817–27.
• [43] Al-Rawi M, Qutaishat M, Arrar M. An improved matched filter for blood vessel detection of digital retinal images. Comput Biol Med 2007;37:262–7.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).