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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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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