Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
Blood Vessel Segmentation in Retinal Images Using Lattice Neural Networks
verfasst von : Roberto Vega, Elizabeth Guevara, Luis Eduardo Falcon, Gildardo Sanchez-Ante, Humberto Sossa
Erschienen in: Advances in Artificial Intelligence and Its Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.