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Skin lesion image segmentation using a color genetic algorithm

Authors:

Alessia Amelio,

Clara PizzutiAuthors Info & Claims

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Pages 1471 - 1478

https://doi.org/10.1145/2464576.2466810

Published: 06 July 2013 Publication History

Abstract

The development of computer-aided diagnosis systems for skin cancer detection has attracted a lot of interest in the research community. In particular, the availability of an accurate automatic segmentation tool for detecting skin lesions from background skin is of primary importance for the overall diagnosis system. In this paper we investigate the capability of a color image segmentation method based on Genetic Algorithms in discriminating skin lesions. Experimental results show that the segmentation approach is able to detect lesion borders quite accurately, thus coupled with a merging technique of the surrounding region could reveal a promising method for isolating skin tumor.

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Cited By

Hasan MAhamad MYap CYang G(2023)A survey, review, and future trends of skin lesion segmentation and classificationComputers in Biology and Medicine10.1016/j.compbiomed.2023.106624155(106624)Online publication date: Mar-2023
https://doi.org/10.1016/j.compbiomed.2023.106624
Roy NKundu ASikder PBhowmik S(2023)Lesion Image Segmentation for Skin Cancer Detection Using Pix2Pix: A Deep Learning ApproachProceedings of International Conference on Data, Electronics and Computing10.1007/978-981-99-1509-5_28(303-311)Online publication date: 22-Nov-2023
https://doi.org/10.1007/978-981-99-1509-5_28
Mookiah SParasuraman KKumar Chandar S(2022)Color image segmentation based on improved sine cosine optimization algorithmSoft Computing10.1007/s00500-022-07133-526:23(13193-13203)Online publication date: 28-Jun-2022
https://doi.org/10.1007/s00500-022-07133-5
Show More Cited By

Index Terms

Skin lesion image segmentation using a color genetic algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

July 2013

1798 pages

ISBN:9781450319645

DOI:10.1145/2464576

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Hasan MAhamad MYap CYang G(2023)A survey, review, and future trends of skin lesion segmentation and classificationComputers in Biology and Medicine10.1016/j.compbiomed.2023.106624155(106624)Online publication date: Mar-2023
https://doi.org/10.1016/j.compbiomed.2023.106624
Roy NKundu ASikder PBhowmik S(2023)Lesion Image Segmentation for Skin Cancer Detection Using Pix2Pix: A Deep Learning ApproachProceedings of International Conference on Data, Electronics and Computing10.1007/978-981-99-1509-5_28(303-311)Online publication date: 22-Nov-2023
https://doi.org/10.1007/978-981-99-1509-5_28
Mookiah SParasuraman KKumar Chandar S(2022)Color image segmentation based on improved sine cosine optimization algorithmSoft Computing10.1007/s00500-022-07133-526:23(13193-13203)Online publication date: 28-Jun-2022
https://doi.org/10.1007/s00500-022-07133-5
Adegun AViriri S(2020)Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-artArtificial Intelligence Review10.1007/s10462-020-09865-yOnline publication date: 27-Jun-2020
https://doi.org/10.1007/s10462-020-09865-y
Gupta CGondhi NLehana P(2019)Analysis and Identification of Dermatological Diseases Using Gaussian Mixture ModelingIEEE Access10.1109/ACCESS.2019.29298577(99407-99427)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2929857
Amelio LAmelio A(2018)Classification Methods in Image Analysis with a Special Focus on Medical AnalyticsMachine Learning Paradigms10.1007/978-3-319-94030-4_3(31-69)Online publication date: 4-Jul-2018
https://doi.org/10.1007/978-3-319-94030-4_3
Azehoun-Pazou GAssogba KAdegbidi H(2017)A multispectral analysis of black skin color images for linea nigra segmentation2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART)10.1109/BIOSMART.2017.8095321(1-4)Online publication date: Aug-2017
https://doi.org/10.1109/BIOSMART.2017.8095321

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