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.
The work is partially funded by an European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 690974 and Dottorato innovativo a caratterizzazione industriale PON R&I FSE-FESR 2014–2020.
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Here standard deviation is depicted as a band around the line; observe that an overlapping region is evidenced between the two bands.
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Calimeri, F., Marzullo, A., Stamile, C., Terracina, G. (2019). Blood Vessel Segmentation in Retinal Fundus Images Using Hypercube NeuroEvolution of Augmenting Topologies (HyperNEAT). In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_17
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