Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm

Akemi Gálvez; Iztok Fister; Iztok Fister; Eneko Osaba; Javier Del Ser; Andrés Iglesias

doi:10.1007/978-3-319-99626-4_31

Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm

Akemi Gálvez
, Iztok Fister
, Iztok Fister
, Eneko Osaba
, Javier Del Ser
, Andrés Iglesias^*

^*Corresponding author for this work

Toho University
Universidad de Cantabria
University of Maribor
Basque Center for Applied Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

9 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Verlag
Pages	357-368
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-319-99626-4_31
Publication status	Published - 2018

Publication series

Name	Studies in Computational Intelligence
Volume	798
ISSN (Print)	1860-949X

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bat algorithm
Border detection
Computational intelligence
Medical images
Nature-inspired metaheuristic techniques
Skin lesion

Access to Document

10.1007/978-3-319-99626-4_31

Cite this

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title = "Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm",

abstract = "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 B{\'e}zier 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.",

keywords = "Bat algorithm, Border detection, Computational intelligence, Medical images, Nature-inspired metaheuristic techniques, Skin lesion",

author = "Akemi G{\'a}lvez and Iztok Fister and Iztok Fister and Eneko Osaba and \{Del Ser\}, Javier and Andr{\'e}s Iglesias",

note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.",

year = "2018",

doi = "10.1007/978-3-319-99626-4\_31",

language = "English",

series = "Studies in Computational Intelligence",

publisher = "Springer Verlag",

pages = "357--368",

booktitle = "Studies in Computational Intelligence",

address = "Germany",

}

Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm. / Gálvez, Akemi; Fister, Iztok; Fister, Iztok et al.
Studies in Computational Intelligence. Springer Verlag, 2018. p. 357-368 (Studies in Computational Intelligence; Vol. 798).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm

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AU - Fister, Iztok

AU - Osaba, Eneko

AU - Del Ser, Javier

AU - Iglesias, Andrés

PY - 2018

Y1 - 2018

N2 - 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.

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.

KW - Bat algorithm

KW - Border detection

KW - Computational intelligence

KW - Medical images

KW - Nature-inspired metaheuristic techniques

KW - Skin lesion

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ER -

Automatic fitting of feature points for border detection of skin lesions in medical images with bat algorithm

Abstract

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UN SDGs

Keywords

Access to Document

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