RT Book, Section
T1 Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm
A1 Gálvez, Akemi
A1 Fister, Iztok
A1 Fister, Iztok
A1 Osaba, Eneko
A1 Del Ser, Javier
A1 Iglesias, Andrés
AB This paper addresses the problem of automatic fitting of feature points for border detection of skin lesions. This problem is an important task in segmentation of dermoscopy images for semi-automatic early diagnosis of melanoma and other skin lesions. Given a set of feature points selected by a dermatologist, we apply a powerful nature-inspired metaheuristic optimization method called bat algorithm to obtain the free-form parametric Bézier curve that fits the points better in the least-squares sense. Our experimental results on two examples of skin lesions show that the method performs quite well and might be applied to automatic fitting of feature points for border detection in medical images.
PB Springer Verlag
SN 1860-949X
YR 2018
FD 2018
LK https://hdl.handle.net/11556/2176
UL https://hdl.handle.net/11556/2176
LA eng
NO Gálvez , A , Fister , I , Fister , I , Osaba , E , Del Ser , J & Iglesias , A 2018 , Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm . in Studies in Computational Intelligence . Studies in Computational Intelligence , vol. 798 , Springer Verlag , pp. 357-368 . https://doi.org/10.1007/978-3-319-99626-4_31
NO Publisher Copyright: © 2018, Springer Nature Switzerland AG.
NO Acknowledgments. This research work has been kindly supported by the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program), grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Regional Development Funds (AEI/FEDER-UE), the project #JU12 of SODERCAN and European Regional Development Funds (SODERCAN/FEDER-UE), the Slovenian Research Agency (Research Core Funding No. P2-0057), and the project EMAITEK of the Basque Government. This research work has been kindly supported by the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie grant agreement No 778035, the Spanish Ministry of Economy and Competitiveness (Computer Science National Program), grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Regional Development Funds (AEI/FEDER-UE), the project #JU12 of SODERCAN and European Regional Development Funds (SODERCAN/FEDER-UE), the Slovenian Research Agency (Research Core Funding No. P2-0057), and the project EMAITEK of the Basque Government.
DS TECNALIA Publications
RD 28 jul 2024