Abstract
Blood vessel segmentation is the first step in the process of automated diagnosis of cardiovascular diseases using retinal images. Unlike previous work described in literature, which uses rule-based methods or classical supervised learning algorithms, we applied Lattice Neural Networks with Dendritic Processing (LNNDP) to solve this problem. LNNDP differ from traditional neural networks in the computation performed by the individual neuron, showing more resemblance with biological neural networks, and offering high performance on the training phase (99.8% precision in our case). Our methodology requires four steps: 1)Preprocessing, 2)Feature computation, 3)Classification, 4)Postprocessing. We used the Hotelling T 2 control chart to reduce the dimensionality of the feature vector from 7 to 5 dimensions, and measured the effectiveness of the methodology with the F 1 Score metric, obtaining a maximum of 0.81; compared to 0.79 of a traditional neural network.
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Agurto, C., Barriga, E., Murray, V., Nemeth, S., Crammer, R., Bauman, W., Zamora, G., Pattichis, M., Soliz, P.: Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Investigative Ophthalmology & Visual Science, 5862–5871 (2011)
Núñez Cortés, J., Alegria, E., Walther, L., Gimilio, J., Rallo, C., Morat, T., Prieto, J., Montoya, J.B., Sala, X.: Documento abordaje de la dislipidemia. sociedad española de arteriosclerosis (parte ii). Clínica e Investigación en Arteriosclerosis, 40–52 (2012)
Sy, R., Morales, D., Dans, A., Paz-Pacheco, E., Punzalan, F., Abelardo, N., Duante, C.: Prevalence of atherosclerosis-related risk factors and diseases in the philippines. Journal of Epidemiology, 440–447 (2012)
National Center for Health Statistics: Hypertension Among Adults in the United States 2009-2010 (2012)
Fraz, M., Remagnino, P., Hoppe, A., Barman, S.: Retinal image analysis aimed at extraction of vascular structure using linear discriminant classifier. In: International Conference on Computer Medical Applications (ICCMA), pp. 1–6 (2013)
Patton, N., Aslam, T., MacGillivray, T., Deary, I., Dhillon, B., Eikelboom, R., Yogesan, K., Constable, I.: Retinal image analysis: Concepts, applications and potential. Progress in Retinal and Eye Research 25, 99–127 (2006)
Abramoff, M., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering 3, 169–208 (2010)
Bernardes, R., Serranho, P., Lobo, C.: Digital ocular fundus imaging: A review. Ophthalmologica 226, 161–181 (2011)
Karthikeyan, R., Alli, P.: Retinal image analysis for abnormality detection-an overview. Journal of Computer Science 8, 436 (2012)
Goldbaum, M., Moezzi, S., Taylor, A., Chatterjee, S., Boyd, J., Hunter, E., Jain, R.: Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images. In: International Conference on Image Processing, pp. 695–698 (1996)
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 19, 203–210 (2000)
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 501–509 (2004)
Kondermann, C., Kondermann, D., Yan, M.: Blood vessel classification into arteries and veins in retinal images. In: SPIE Medical Imaging, pp. 651247–651247 9 (2007)
Hijazi, M., Coenen, F., Zheng, Y.: Retinal image classification for the screening of age-related macular degeneration. In: Research and Development in Intelligent Systems XXVII, pp. 325–338 (2011)
Tariq, A., Akram, M.: An automated system for colored retinal image background and noise segmentation. In: IEEE Symposium on Industrial Electronics and Applications (ISIEA), Penang, Malaysia, pp. 423–427 (2010)
Maruthusivarani, M., Ramakrishnan, T., Santhi, D., Muthukkutti, K.: Comparison of automatic blood vessel segmentation methods in retinal images. In: International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System (ICEVENT), pp. 1–4 (2013)
Preethi, M., Vanithamani, R.: Review of retinal blood vessel detection methods for automated diagnosis of diabetic retinopathy. In: International Conference on Advances in Engineering, Science and Management (ICAESM), pp. 262–265 (2012)
Marin, D., Aquino, A., Gegundez-Arias, M., Bravo, J.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Transactions on Medical Imaging 30, 146–158 (2011)
Lau, Q., Lee, M., Hsu, W., Wong, T.: Simultaneously identifying all true vessels from segmented retinal images. IEEE Transactions on Biomedical Engineering 60, 1851–1858 (2013)
Ritter, G., Iancu, L., Urcid, G.: Morphological perceptrons with dendritic structure. In: The 12th IEEE International Conference on Fuzzy Systems, FUZZ 2003, vol. 2, pp. 1296–1301 (2003)
Ritter, G., Schmalz, M.: Learning in lattice neural networks that employ dendritic computing. In: IEEE International Conference on Fuzzy Systems 2006, pp. 7–13 (2006)
Sussner, P.: Morphological perceptron learning. In: International Symposium on Intelligent Control (ISIC), held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS), pp. 477–482 (1998)
Ritter, G., Sussner, P.: An introduction to morphological neural networks. In: International Conference on Pattern Recognition, vol. 4, pp. 709–717 (1996)
Ritter, G., Beaver, T.: Morphological perceptrons. In: International Joint Conference on Neural Networks, IJCNN 1999, vol. 1, pp. 605–610 (1999)
Sossa, H., Guevara, E.: Efficient training for dendrite morphological neural networks (Submitted to Neurocomputing, 2013)
Nixon, M., Aguado, A.: Feature extraction & image processing. Newnes, Great Britain (2008)
Montgomery, D.: Introduction to Statistical Quality Control, 5th edn. Wiley, USA (2005)
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Vega, R., Guevara, E., Falcon, L.E., Sanchez-Ante, G., Sossa, H. (2013). Blood Vessel Segmentation in Retinal Images Using Lattice Neural Networks. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45114-0_42
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DOI: https://doi.org/10.1007/978-3-642-45114-0_42
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