Blood Vessel Segmentation in Retinal Images Using Lattice Neural Networks

  • Roberto Vega
  • Elizabeth Guevara
  • Luis Eduardo Falcon
  • Gildardo Sanchez-Ante
  • Humberto Sossa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8265)


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.


Feature Vector Diabetic Retinopathy Control Chart Retinal Image Moment Invariant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roberto Vega
    • 1
  • Elizabeth Guevara
    • 2
  • Luis Eduardo Falcon
    • 1
  • Gildardo Sanchez-Ante
    • 1
  • Humberto Sossa
    • 2
  1. 1.Computer Science DepartmentTecnológico de Monterrey, Campus GuadalajaraZapopanMéxico
  2. 2.Instituto Politécnico Nacional-CICMéxico, Distrito FederalMéxico

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