Retinal vasculature segmentation and measurement framework for color fundus and SLO images

• Autorzy: Pachade Samiksha , Porwal Prasanna , Kokare Manesh , Giancardo Luca , Meriaudeau Fabrice

Identyfikatory

DOI

10.1016/j.bbe.2020.03.001

• Języki publikacji: EN

Abstrakty

EN

The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width measurement under these challenging circumstances and also on two different imaging modalities: color fundus imaging and Scanning Laser Ophthalmoscopy (SLO). In this framework, initially, vessel enhancement is done using linear recursive filtering. Then, a unique combination of morphological operations, background estimation, and iterative thresholding are applied to segment the blood vessels. Further, vessel diameter is estimated in two steps: firstly, vessel centerlines are extracted using the graph-based algorithm. Then, vessel edges are localized from the image profiles, by utilizing spline fitting to obtain vascular orientations and then finding the zero-crossings. Extensive experiments have been carried out on several publicly accessible datasets for vessel segmentation and diameter measurement, i.e., DRIVE, STARE, IOSTAR, RC-SLO and REVIEW dataset. Results demonstrate the competitive and comparable performance than earlier methods. The encouraging quantitative and visual performance of the proposed framework makes it an important component of a decision support system for retinal images.

Słowa kluczowe

EN

blood vessel; retinal image analysis; vessel segmentation; vessel diameter measurement; SLO images; diabetic retinopathy

PL

naczynia krwionośne; obrazowanie siatkówki; segmentacja naczyń krwionośnych; retinopatia cukrzycowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 865--900
• Opis fizyczny: Bibliogr. 67 poz., rys., tab., wykr.

Twórcy

autor

Pachade Samiksha

• Research Scholar, Center of Excellence in Signal and Image Processing, Dept. of Electronics & Telecomm., SGGS Institute of Engg. and Tech., Nanded, India

autor

Porwal Prasanna

• Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India

autor

Kokare Manesh

• Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India

autor

Giancardo Luca

• Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA

autor

Meriaudeau Fabrice

• ImViA/IFTIM, Université de Bourgogne, Dijon, France

Bibliografia

• [1] Kanski JJ, Bowling B. Synopsis of clinical ophthalmology e-book: expert consult-online and print. Elsevier Health Sciences; 2012.
• [2] Owen CG, Rudnicka AR, Nightingale CM, Mullen R, Barman SA, Sattar N, et al. Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; the child heart and health study in England (chase). Arterioscler Thromb Vasc Biol 2011;31(8):1933–8.
• [3] Porwal P, Pachade S, Kokare M, Deshmukh G, Sahasrabuddhe V. Automatic retinal image analysis for the detection of diabetic retinopathy. Biomedical signal and image processing in patient care. IGI Global; 2018. p. 146–61.
• [4] Jain AB, Prakash VJ, Bhende M. Techniques of fundus imaging. Med Vis Res Found 2015;33 (2):100.
• [5] Manivannan A, Van der Hoek J, Vieira P, Farrow A, Olson J, Sharp PF, et al. Clinical investigation of a true color scanning laser ophthalmoscope. Arch Ophthalmol 2001;119(6):819–24.
• [6] Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, et al. Blood vessel segmentation methodologies in retinal images-a survey. Comput Methods Programs Biomed 2012;108(1):407–33.
• [7] Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics. Comput Methods Programs Biomed 2018;158:71–91.
• [8] Vostatek P, Claridge E, Uusitalo H, Hauta-Kasari M, Fält P, Lensu L. Performance comparison of publicly available retinal blood vessel segmentation methods. Comput Med Imaging Graphics 2017;55:2–12.
• [9] Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004;23(4):501–9.
• [10] Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff MD. Comparative study of retinal vessel segmentation methods on a new publicly available database. Medical imaging 2004: image processing, vol. 5370. International Society for Optics and Photonics; 2004. p. 648–57.
• [11] Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ. Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans Med Imaging 2006;25(9):1214–22.
• [12] Dai P, Luo H, Sheng H, Zhao Y, Li L, Wu J, et al. A new approach to segment both main and peripheral retinal vessels based on gray-voting and gaussian mixture model. PLOS ONE 2015;10 (6):e0127748.
• [13] Salem SA, Salem NM, Nandi AK. Segmentation of retinal blood vessels using a novel clustering algorithm (racal) with a partial supervision strategy. Med Biol Eng Comput 2007;45(3):261–73.
• [14] 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 Trans Biomed Eng 2012;59(9):2538–48.
• [15] Ganjee R, Azmi R, Gholizadeh B. An improved retinal vessel segmentation method based on high level features for pathological images. J Med Syst 2014;38(9):108.
• [16] Kaur J, Mittal D. A generalized method for the detection of vascular structure in pathological retinal images. Biocybern Biomed Eng 2017;37 (1):184–200.
• [17] Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T. A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 2016;35(1):109–18.
• [18] Rahebi J, Hardalaç F. Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J Med Syst 2014;38(8):85.
• [19] Vega R, Sanchez-Ante G, Falcon-Morales LE, Sossa H, Guevara E. Retinal vessel extraction using lattice neural networks with dendritic processing. Comput Biol Med 2015;58:20–30.
• [20] Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation; 2018, arXiv preprint arXiv:1802.06955.
• [21] 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.
• [22] Zhang B, Huang S, Hu S. Multi-scale neural networks for retinal blood vessels segmentation; 2018, arXiv preprint arXiv:1804.04206.
• [23] Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 2016;35(11):2369–80.
• [24] Girard F, Kavalec C, Cheriet F. Joint segmentation and classification of retinal arteries/veins from fundus images. Artif Intell Med 2019;94:96– 109.
• [25] Fu H, Xu Y, Lin S, Wong DWK, Liu J. Deepvessel: retinal vessel segmentation via deep learning and conditional random field. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2016. p. 132–9.
• [26] Chakraborti T, Jha DK, Chowdhury AS, Jiang X. A self-adaptive matched filter for retinal blood vessel detection. Mach Vis Appl 2015;26(1):55–68.
• [27] Krause M, Alles RM, Burgeth B, Weickert J. Fast retinal vessel analysis. J Real-Time Image Process 2016;11(2):413–22.
• [28] Zhang J, Dashtbozorg B, Bekkers E, Pluim JP, Duits R, ter Haar Romeny BM. Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans Med Imaging 2016;35(12):2631–44.
• [29] Strisciuglio N, Petkov N. Delineation of line patterns in images using b-cosfire filters. 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI). IEEE; 2017. p. 1–6.
• [30] Farokhian F, Yang C, Demirel H, Wu S, Beheshti I. Automatic parameters selection of gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation. Biocybern Biomed Eng 2017;37(1):246–54.
• [31] Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, et al. An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 2012;108(2):600–16.
• [32] Akram MU, Khan SA. Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy. Eng Comput 2013;29(2):165–73.
• [33] Jiang Z, Yepez J, An S, Ko S. Fast, accurate and robust retinal vessel segmentation system. Biocybern Biomed Eng 2017;37(3):412–21.
• [34] Nguyen UT, Bhuiyan A, Park LA, Ramamohanarao K. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit 2013;46(3):703–15.
• [35] Yu H, Barriga S, Agurto C, Zamora G, Bauman W, Soliz P. Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy. Medical imaging 2012: computer-aided diagnosis, vol. 8315. International Society for Optics and Photonics; 2012. p. 83151B.
• [36] Wang Y, Ji G, Lin P, Trucco E. Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recognit 2013;46(8):2117–33.
• [37] Shah SAA, Tang TB, Faye I, Laude A. Blood vessel segmentation in color fundus images based on regional and hessian features. Graefe's Arch Clin Exp Ophthalmol 2017;255(8):1525–33.
• [38] Rodrigues LC, Marengoni M. Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and hessian-based multi-scale filtering. Biomedical signal processing and control, vol. 36. 2017; p. 39–49.
• [39] Panda R, Puhan N, Panda G. New binary hausdorff symmetry measure based seeded region growing for retinal vessel segmentation. Biocybern Biomed Eng 2016;36(1):119–29.
• [40] Christodoulidis A, Hurtut T, Tahar HB, Cheriet F. A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images. Comput Med Imaging Graphics 2016;52:28–43.
• [41] Argüello F, Vilariño DL, Heras DB, Nieto A. Gpu-based segmentation of retinal blood vessels. J Real-Time Image Process 2018;14(4):773–82.
• [42] GeethaRamani R, Balasubramanian L. Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern Biomed Eng 2016;36(1):102–18.
• [43] M. H.-G. Model, Analysis of retinal vasculature using a multiresolution hermite-gaussian model.
• [44] 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(4):407–29.
• [45] Martinez-Perez ME, Highes A, Stanton AV, Thorn SA, Chapman N, Bharath AA, et al. Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans Biomed Eng 2002;49 (8):912–7.
• [46] Pakter HM, Ferlin E, Fuchs SC, Maestri MK, Moraes RS, Nunes G, et al. Measuring arteriolar-to-venous ratio in retinal photography of patients with hypertension: development and application of a new semi-automated method. Am J Hypertens 2005;18(3):417–21.
• [47] Perez-Rovira A, MacGillivray T, Trucco E, Chin K, Zutis K, Lupascu C, et al. Vampire: vessel assessment and measurement platform for images of the retina. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2011. p. 3391–4.
• [48] Bankhead P, Scholfield CN, McGeown JG, Curtis TM. Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 2012;7(3):e32435.
• [49] Xu X, Niemeijer M, Song Q, Sonka M, Garvin MK, Reinhardt JM, et al. Vessel boundary delineation on fundus images using graph-based approach. IEEE Trans Med Imaging 2011;30(6):1184–91.
• [50] Zhou L, Rzeszotarski MS, Singerman LJ, Chokreff JM. The detection and quantification of retinopathy using digital angiograms. IEEE Trans Med Imaging 1994;13(4):619–26.
• [51] Gao X, Bharath A, Stanton A, Hughes A, Chapman N, Thom S, et al. Measurement of vessel diameters on retinal images for cardiovascular studies. Proceedings of Medical Image Understanding and Analysis; 2001. p. 1–4.
• [52] Lowell J, Hunter A, Steel D, Basu A, Ryder R, Kennedy RL. Measurement of retinal vessel widths from fundus images based on 2-d modeling. IEEE Trans Med Imaging 2004;23 (10):1196–204.
• [53] Li H, Hsu W, Lee M-L, Wang H. A piecewise gaussian model for profiling and differentiating retinal vessels. Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429), vol. 1. IEEE; 2003. p. I–1069.
• [54] Gang L, Chutatape O, Krishnan SM. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order gaussian filter. IEEE Trans Biomed Eng 2002;49 (2):168–72.
• [55] Kumar DK, Aliahmad B, Hao H. Retinal vessel diameter measurement using unsupervised linear discriminant analysis. ISRN Ophthalmol 2012.
• [56] Gregson P, Shen Z, Scott R, Kozousek V. Automated grading of venous beading. Comput Biomed Res 1995;28(4):291–304.
• [57] Brinchmann-Hansen O, Heier H. Theoretical relations between light streak characteristics and optical properties of retinal vessels. Acta Ophthalmol 1986;64(S179):33–7.
• [58] Al-Diri B, Hunter A, Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 2009;28(9):1488– 97.
• [59] Pellegrini E, Robertson G, Trucco E, MacGillivray TJ, Lupascu C, van Hemert J, et al. Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy. Biomed Optics Express 2014;5(12):4329–37.
• [60] Lupascu CA, Tegolo D, Trucco E. Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution hermite model. Med Image Anal 2013;17(8):1164–80.
• [61] Pachade S, Porwal P, Kokare M. A novel unsupervised framework for retinal vasculature segmentation. Advanced computational and communication paradigms. Springer; 2018. p. 490–7.
• [62] Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. Proceedings of the AMIA Symposium. American Medical Informatics Association; 1998. p. 931.
• [63] Al-Diri B, Hunter A, Steel D, Habib M, Hudaib T, Berry S. A reference data set for retinal vessel profiles. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2008. p. 2262–5.
• [64] Gastal ES, Oliveira MM. Adaptive manifolds for real-time high-dimensional filtering. ACM Trans Graphics (TOG) 2012;31(4):33.
• [65] Pachade S, Porwal P, Kokare M. A novel method to detect fovea from color fundus images. Computing, communication and signal processing. Springer; 2019. p. 957–65.
• [66] Kamble R, Kokare M. Automatic blood vessel extraction technique using phase stretch transform in retinal images. 2016 International Conference on Signal and Information Processing (IConSIP). IEEE; 2016. p. 1–5.
• [67] Sopharak A, Uyyanonvara B, Barman S, Williamson TH. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graphics 2008;32(8):720–7.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).