Blood vessel segmentation in retinal images using lattice neural networks

Roberto Vega, Elizabeth Guevara, Luis Eduardo Falcon, Gildardo Sanchez-Ante, Humberto Sossa

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

14 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaAdvances in Artificial Intelligence and Its Applications - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Proceedings
Páginas532-544
Número de páginas13
EdiciónPART 1
DOI
EstadoPublicada - 2013
Evento12th Mexican International Conference on Artificial Intelligence, MICAI 2013 - Mexico City, México
Duración: 24 nov. 201330 nov. 2013

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NúmeroPART 1
Volumen8265 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia12th Mexican International Conference on Artificial Intelligence, MICAI 2013
País/TerritorioMéxico
CiudadMexico City
Período24/11/1330/11/13

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