Image recognition applications has been capturing interest of researchers for many years, as they found countless real-life applications. A significant role in the development of such systems has recently been played by evolutionary algorithms. Among those, HyperNEAT shows interesting results when dealing with potentially high-dimensional input space: the capability to encode and exploit spatial relationships of the problem domain makes the algorithm effective in image processing tasks. In this work, we aim at investigating the effectiveness of HyperNEAT on a particular image processing task: the automatic segmentation of blood vessels in retinal fundus digital images. Indeed, the proposed approach consists of one of the first applications of HyperNEAT to image processing tasks to date. We experimentally tested the method over the DRIVE and STARE datasets, and the proposed method showed promising results on the study case; interestingly, our approach highlights HyperNEAT capabilities of evolving towards small architectures, yet suitable for non-trivial biomedical image segmentation tasks.

Blood vessel segmentation in retinal fundus images using hypercube neuroevolution of augmenting topologies (HyperNEAT)

Calimeri, Francesco;Terracina, Giorgio
2019

Abstract

Image recognition applications has been capturing interest of researchers for many years, as they found countless real-life applications. A significant role in the development of such systems has recently been played by evolutionary algorithms. Among those, HyperNEAT shows interesting results when dealing with potentially high-dimensional input space: the capability to encode and exploit spatial relationships of the problem domain makes the algorithm effective in image processing tasks. In this work, we aim at investigating the effectiveness of HyperNEAT on a particular image processing task: the automatic segmentation of blood vessels in retinal fundus digital images. Indeed, the proposed approach consists of one of the first applications of HyperNEAT to image processing tasks to date. We experimentally tested the method over the DRIVE and STARE datasets, and the proposed method showed promising results on the study case; interestingly, our approach highlights HyperNEAT capabilities of evolving towards small architectures, yet suitable for non-trivial biomedical image segmentation tasks.
Blood vessel segmentation; Evolutionary neural networks; Genetic algorithms; Hyperneat; Retinal fundus images; Decision Sciences (all); Computer Science (all)
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/290078
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact