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An Algorithm for Segmenting Gaseous Objects on Images

  • Conference paper
Applications of Evolutionary Computing (EvoWorkshops 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3005))

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Abstract

A new methodology for segmenting gaseous object images is introduced. Unlike in case of a rigid object, the edge intensity of a gaseous object varies along the object boundary (edge intensities of some pixels on a gaseous object boundary are weaker than those of small rigid objects or noise itself). Therefore, the conventional edge detectors may not adequately detect boundary-like edge pixels of gaseous objects. We develop a novel object segmenting method using fuzzy algorithm trained by the genetic algorithm. The proposed method consists of a fuzzy-based boundary detector applicable to gaseous as well as rigid objects, and concave region filling to recover object regions. This algorithm is well applicable to medical image such as breast cancer or tumor segmentation.

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References

  1. Parker, J.R.: Algorithms for Image Processing and Computer Vision. John Wiley & Sons, Inc., Chichester (1997)

    Google Scholar 

  2. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  3. Davis, L.S.: A survey of edge detection techniques. Computer Graphics and Image Processing 4, 248–270 (1975)

    Article  Google Scholar 

  4. Wang, L.X.: A course in fuzzy systems and control. Prentice-Hall, Inc., MA (1997)

    MATH  Google Scholar 

  5. Goldberg, D.E.: Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Reading (1989)

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Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Konkuk University,  

    Sung-Min Kim

  2. School of Electronic and Information Engineering, Kyung Hee University,  

    Wonha Kim

Authors
  1. Sung-Min Kim
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  2. Wonha Kim
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Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  3. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  10. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2004 Springer-Verlag Berlin Heidelberg

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Kim, SM., Kim, W. (2004). An Algorithm for Segmenting Gaseous Objects on Images. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_33

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive

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