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Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature Read chapter Read first chapter

2016 | OriginalPaper | Chapter

SAHF: Unsupervised Texture-Based Multiscale with Multicolor Method for Retinal Vessel Delineation

Authors : Temitope Mapayi, Jules-Raymond Tapamo

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

Automatic vessel delineation has been challenging due to complexities during the acquisition of retinal images. Although, great progress have been made in this field, it remains the subject of on-going research as there is need to further improve on the delineation of more large and thinner retinal vessels as well as the computational speed. Texture and color are promising, as they are very good features applied for object detection in computer vision. This paper presents an investigatory study on sum average Haralick feature (SAHF) using multi-scale approach over two different color spaces, CIElab and RGB, for the delineation of retinal vessels. Experimental results show that the method presented in this paper is robust for the delineation of retinal vessels having achieved fast computational speed with the maximum average accuracy of 95.67% and maximum average sensitivity of 81.12% on DRIVE database. When compared with the previous methods, the method investigated in this paper achieves higher average accuracy and sensitivity rates on DRIVE.

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Literature
1.
2.
Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging, image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)CrossRef
3.
Davitt, B.V., Wallace, D.K.: Plus disease. Surv. Ophthalmol. 54(6), 663–670 (2009)CrossRef
4.
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Systems Man Cybern. 3(6), 610–621 (1973)CrossRef
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. Imaging 19(3), 203–210 (2000)CrossRef
6.
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)CrossRef
7.
Li, B., Li, H.K.: Automated analysis of diabetic retinopathy images: principles, recent developments, and emerging trends. Curr. Diab. Rep. 13(4), 453–459 (2013)CrossRef
8.
Li, Q., You, J., Zhang, D.: Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Syst. Appl. 39(9), 7600–7610 (2012)CrossRef
9.
Mäenpää, T., Pietikäinen, M.: Classification with color and texture: jointly or separately? Pattern Recogn. 37(8), 1629–1640 (2004)CrossRef
10.
Mapayi, T., Tapamo, J.-R., Viriri, S., Adio, A.: Automatic retinal vessel detection and tortuosity measurement. Image Anal. Stereology 35(2), 117–135 (2016)MathSciNetCrossRef
11.
Mapayi, T., Viriri, S., Tapamo, J.-R.: Comparative study of retinal vessel segmentation based on global thresholding techniques. Comput. Math. Methods Med. 2015 (2015)
12.
Marín, D., Aquino, A., Gegúndez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level, moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)CrossRef
13.
Martínez-Pérez, M.E., Hughes, A.D., Stanton, A.V., Thom, S.A., Bharath, A.A., Parker, K.H.: Retinal blood vessel segmentation by means of scale-space analysis and region growing. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 90–97. Springer, Heidelberg (1999). doi:10.​1007/​10704282_​10 CrossRef
14.
Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)CrossRef
15.
Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical Imaging 2004, pp. 648–656. International Society for Optics and Photonics (2004)
16.
Ohta, Y.-I., Kanade, T., Sakai, T.: Color information for region segmentation. Comput. Graph. Image Process. 13(3), 222–241 (1980)CrossRef
17.
Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recogn. 37(5), 965–976 (2004)CrossRef
18.
Patasius, M., Marozas, V., Jegelevicius, D., Lukoševičius, A.: Ranking of color space components for detection of blood vessels in eye fundus images. In: Sloten, J.V., Verdonck, P., Nyssen, M., Haueisen, J. (eds.) 4th European Conference of the International Federation for Medical and Biological Engineering, pp. 464–467. Springer, Heidelberg (2009)CrossRef
19.
Pratt, W.: Spatial transform coding of color images. IEEE Trans. Commun. Technol. 19(6), 980–992 (1971)CrossRef
20.
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007)CrossRef
21.
Saffarzadeh, V.M., Osareh, A., Shadgar, B.: Vessel segmentation in retinal images using multi-scale line operator, K-means clustering. J. Med. Sig. Sens. 4(2), 122 (2014)
22.
Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D gabor wavelet, supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)CrossRef
23.
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRef
24.
Tobin, K.W., Edward Chaum, V., Govindasamy, P., Karnowski, T.P.: Detection of anatomic structures in human retinal imagery. IEEE Trans. Med. Imaging 26(12), 1729–1739 (2007)CrossRef
25.
Van de Wouwer, G., Scheunders, P., Livens, S., Van Dyck, D.: Wavelet correlation signatures for color texture characterization. Pattern Recogn. 32(3), 443–451 (1999)CrossRef
26.
Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Comput. Med. Imaging Graph. 34(3), 213–227 (2010)CrossRef
27.
Wang, Y., Ji, G., Lin, P., Trucco, E., et al.: Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recogn. 46(8), 2117–2133 (2013)CrossRef
28.
Yang, Y., Huang, S.: Image segmentation by fuzzy C-means clustering algorithm with a novel penalty term. Comput. Inf. 26(1), 17–31 (2012)MATH
29.
Yin, Y., Adel, M., Bourennane, S.: Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation. Comput. Math. Methods Med. 2013 (2013)
Metadata
Title
SAHF: Unsupervised Texture-Based Multiscale with Multicolor Method for Retinal Vessel Delineation
Authors
Temitope Mapayi
Jules-Raymond Tapamo
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_57

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