Abstract
In this paper, a vessel segmentation method from hyperspectral retinal images based on the Multi-Scale Line Detection algorithm is proposed. The method consists in combining segmentation information from several consecutive images obtained at specific wavelengths around the green channel to produce an accurate segmentation of the retinal vessel network. Images obtained from six subjects were used to evaluate the performance of the proposed method. Preliminary results suggest a potential advantage of combining multispectral information instead of using only the green channel in segmenting retinal blood vessels.
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Wong, T.Y., Klein, R., Klein, B.E., Tielsch, J.M., Hubbard, L., Nieto, F.J.: Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. Surv. Ophthalmol. 46(1), 59–80 (2001)
Klein, R., Myers, C.E., Lee, K.E., Gangnon, R., Klein, B.E.: Changes in retinal vessel diameter and incidence and progression of diabeticretinopathy. Arch. Ophthalmol. 130, 749–755 (2012)
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. 25(9), 1214–1222 (2006)
Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23(4), 501–509 (2004)
Annunziata, R., Trucco, E.: Accelerating convolutional sparse coding for curvilinear structures segmentation by refining SCIRD-TS filter banks. IEEE Trans. Med. Imaging 35(11), 2381–2392 (2016)
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)
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 (2003)
Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10(7), 1010–1019 (2001)
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. Imaging 8(3), 263–269 (1989)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)
Zhang, B., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 40(4), 438–445 (2010)
Liu, I., Sun, Y.: Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans. Med. Imaging 12(2), 334–341 (1993)
Can, A., Shen, H., Turner, J.N., Tanenbaum, H.L., Roysam, B.: Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inf. Tech. Biomed. 3(2), 125–138 (1999)
Christodoulidis, A., Hurtut, T., Ben Tahar, H., Cheriet, F.: A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images. Comput. Med. Imaging Graph. 52, 28–43 (2016)
Nguyena, U.T.V., Bhuiyana, A., Park, L.A.F., Ramamohanaraoa, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 46(3), 703–715 (2013)
Narasimha-Iyer, H., Beach, J.M., Khoobehi, B., Roysam, B.: Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features. IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007)
Desjardins, M., Sylvestre, J.P., Jafari, R., Kulasekara, S., Rose, K., Trussart, R., Arbour, J.D., Hudson, C., Lesage, F.: Preliminary investigation of multispectral retinal tissue oximetry mapping using a hyperspectral retinal camera. Exp. Eye Res. 146, 330–340 (2016)
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Farah, R. et al. (2017). Retinal Vessel Segmentation from a Hyperspectral Camera Images. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_62
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DOI: https://doi.org/10.1007/978-3-319-59876-5_62
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