TY - GEN
T1 - Blood vessel segmentation in retinal images using lattice neural networks
AU - Vega, Roberto
AU - Guevara, Elizabeth
AU - Falcon, Luis Eduardo
AU - Sanchez-Ante, Gildardo
AU - Sossa, Humberto
PY - 2013
Y1 - 2013
N2 - 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 T2 control chart to reduce the dimensionality of the feature vector from 7 to 5 dimensions, and measured the effectiveness of the methodology with the F1 Score metric, obtaining a maximum of 0.81; compared to 0.79 of a traditional neural network.
AB - 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 T2 control chart to reduce the dimensionality of the feature vector from 7 to 5 dimensions, and measured the effectiveness of the methodology with the F1 Score metric, obtaining a maximum of 0.81; compared to 0.79 of a traditional neural network.
UR - http://www.scopus.com/inward/record.url?scp=84894128965&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-45114-0_42
DO - 10.1007/978-3-642-45114-0_42
M3 - Contribución a la conferencia
SN - 9783642451133
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 532
EP - 544
BT - Advances in Artificial Intelligence and Its Applications - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Proceedings
T2 - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013
Y2 - 24 November 2013 through 30 November 2013
ER -