Abstract

A system for detecting Diabetic Macular Edema (DME) using Optical Coherence Tomography (OCT) volumes is presented. In preprocessing stage noise removal and flattening of scans is done which is followed by Local binary pattern feature extraction. The extracted features are then classified using linear support vector machine classifier. The proposed system achieved an specificity and sensitivity of 100% and 86.67% respectively.

References

• National Diabetes Data Group (US), National Institute of Diabetes, Digestive, Kidney Diseases (US), and National Institutes of Health (US). Diabetes in America. Number 95. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 1995.
• Early Treatment Diabetic Retinopathy Study Group, “Photocoagulation for diabetic macular edema: early treatment diabetic retinopathy study report no 1,” JAMA Ophthalmology, vol. 103, no. 12, pp. 1796–1806, 1985.
• T. C. Chen, B. Cense, M. C. Pierce, N. Nassif, B. H. Park, S. H. Yun, B. R. White, B. E. Bouma, G. J. Tearney, and J. F. de Boer, “Spectral domain optical coherence tomography: ultra-high speed, ultra-high resolution ophthalmic imaging,” JAMA Ophthalmology, vol. 123, no. 12, pp. 1715–1720, 2005.
• M. D. Abr`amoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” Biomedical Engineering, IEEE Reviews in, vol. 3, pp. 169–208, 2010.
• E. Trucco, A. Ruggeri, T. Karnowski, L. Giancardo, E. Chaum, J. P. Hubschman, B. al Diri, C. Y. Cheung, D. Wong, M. Abramoff et al., “Validating retinal fundus image analysis algorithms: Issues and a proposalvalidating retinal fundus image analysi algorithms,” Investigative ophthalmology & visual science, vol. 54, no. 5, pp. 3546– 3559, 2013.
• L. Giancardo, F. Meriaudeau, T. P. Karnowski, K. W. Tobin Jr, E. Grisan, P. Favaro, A. Ruggeri, and E. Chaum, “Textureless macula swelling detection with multiple retinal fundus images,” Biomedical Engineering, IEEE Transactions on, vol. 58, no. 3, pp. 795–799, 2011.
• S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation,” Optics express, vol. 18, no. 18, pp. 19 413–19 428, 2010.
• M. K. Garvin, M. D. Abr`amoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-d intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” Medical Imaging, IEEE Transactions on, vol. 28, no. 9, pp. 1436–1447, 2009.
• M. K. Garvin, M. D. Abr`amoff, R. Kardon, S. R. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-d graph search,” IEEE Transactions on Medical Imaging, vol. 27, no. 10, pp. 1495–1505, 2008.
• I. Oguz, L. Zhang, M. D. Abr`amoff, and M. Sonka, “Optimal retinal cyst segmentation from oct images,” in SPIE Medical Imaging. International Society for Optics and Photonics, 2016, pp. 97 841E– 97 841E.
• P. P. Srinivasan, L. A. Kim, P. S. Mettu, S. W. Cousins, G. M. Comer, J. A. Izatt, and S. Farsiu, “Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images,” Biomedical optics express, vol. 5, no. 10, pp. 3568–3577, 2014.
• J. Sivic and A. Zisserman, “Video google: a text retrieva approach to object matching in videos,” in IEEE ICCV, 2003, pp. 1470–1477.
• F. G. Venhuizen, B. van Ginneken, B. Bloemen, M. J. van Grinsven, R. Philipsen, C. Hoyng, T. Theelen, and C. I. S´anchez, “Automated age-related macular degeneration classification in oct using unsupervised feature learning,” in SPIE Medical Imaging. International Society for Optics and Photonics, 2015, pp. 94 141I–94 141I.
• Y.-Y. Liu, M. Chen, H. Ishikawa, G. Wollstein, J. S. Schuman, and J. M. Rehg, “Automated macular pathology diagnosis in retinal oct images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding,” Medical image analysis, vol. 15, no. 5, pp. 748–759, 2011.
• Lemaıtre, Guillaume, Mojdeh Rastgoo, Joan Massich, Carol Y. Cheungc, Tien Y. Wongc, Ecosse Lamoureuxc, Dan Mileac, Fabrice Mériaudeau, and Désiré Sidibé. "Classification of SD-OCT Volumes using Local Binary Patterns: Experimental Validation for DME Detection." (2016)..
• K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
• A. N. Kuo, R. P. McNabb, S. J. Chiu, M. A. El-Dairi, S. Farsiu, C. A. Toth, and J. A. Izatt, “Correction of Ocular Shape in Retinal Optical Coherence Tomography and Effect on Current Clinical Measures,” Am. J. Ophthalmol. 156(2), 304–311 (2013).
• Ojala, T., M. Pietikainen, and T. Maenpaa. "Multiresolution Gray Scale and Rotation Invariant Texture Classification With Local Binary Patterns." IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24, Issue 7, July 2002, pp. 971-987.
• P. Doll´ar, V. Rabaud, G. Cottrell, and S. Belongie, ―Be-havior recognition via sparse spatio-temporal features‖, In VS-PETS, 2005.
• J. C. Niebles, H. Wang, and L. Fei-Fei, ―Unsupervised learning of human action categories using spatial temporal words‖, In BMVC, 2006