Abstract
In this paper, an age-related macular degeneration (AMD) classification algorithm based on local texture features is proposed to support the automated analysis of optical coherence tomography angiography (OCTA) images in wet AMD. The algorithm is based on rotation invariant uniform Local Binary Patterns (\( LBPs^{riu2} \)) as a texture measurement technique. It was chosen due to its computational simplicity and its invariance against any transformation of the grey level as well as against texture orientation change. The texture features are extracted from the whole image without targeting a particular area. The algorithm was tested on two-dimensional angiogram greyscale images of four different retinal layers acquired via OCTA scan. The evaluation was performed using a ten-fold cross-validation strategy applied to a set of 184 OCTA images consisting of 92 normal control and 92 wet AMD images. The classification was performed on each separate retinal layer, and on all layers together. According to the results, the algorithm was able to achieve a promising performance with mean accuracy of 89% for all layers together and 89%, 94%, 98% and 100% for the superficial, deep, outer and choriocapillaris layers respectively.
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De Carlo, T.E., et al.: Spectral-domain optical coherence tomography angiography of choroidal neovascularization. Ophthalmology 122(6), 1228–1238 (2015)
Jia, Y., et al.: Quantitative optical coherence tomography angiography of vascular abnormalities in the living human eye. Proc. Natl. Acad. Sci. U.S.A. 112(18), E2395–E2402 (2015)
de Carlo, T.E., Romano, A., Waheed, N.K., Duker, J.S.: A review of optical coherence tomography angiography (OCTA). Int. J. Retin. Vitr. 1(1), 5 (2015)
Liu, L., Gao, S.S., Bailey, S.T., Huang, D., Li, D., Jia, Y.: Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography. Biomed. Opt. Express 6(9), 3564 (2015)
Tourassi, G.D.: Journey toward computer-aided diagnosis: role of image texture analysis. Radiology 213, 317–320 (1999)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Jia, Y., et al.: Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration. Ophthalmology 121(7), 1435–1444 (2014)
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994, Vol. 1-Conference A: Computer Vision & Image Processing, vol. 1, pp. 582–585 (1994)
Spaide, R.F., Fujimoto, J.G., Waheed, N.K.: Image artifacts in optical coherence angiography. Retina (Phila. Pa.) 35(11), 2163 (2015)
Strand, J., Taxt, T.: Local frequency features for texture classification. Pattern Recognit. 27(10), 1397–1406 (1994)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)
Ohanian, P.P., Dubes, R.C.: Performance evaluation for four classes of textural features. Pattern Recognit. 25(8), 819–833 (1992)
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Alfahaid, A., Morris, T. (2018). An Automated Age-Related Macular Degeneration Classification Based on Local Texture Features in Optical Coherence Tomography Angiography. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_19
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DOI: https://doi.org/10.1007/978-3-319-95921-4_19
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