An algorithm for segmenting gaseous objects on images

Sung Min Kim; Wonha Kim

doi:10.1007/978-3-540-24653-4_33

An algorithm for segmenting gaseous objects on images

Sung Min Kim
, Wonha Kim

Department of Biomedical Engineering

Kyung Hee University

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

3 Scopus citations

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.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Gunther R. Raidl, Stefano Cagnoni, Jurgen Branke, David W. Corne, Rolf Drechsler, Yaochu Jin, Colin G. Johnson, Penousal Machado, Elena Marchiori, Franz Rothlauf, George D. Smith, Giovanni Squillero
Publisher	Springer Verlag
Pages	322-328
Number of pages	7
ISBN (Electronic)	9783540213789
DOIs	https://doi.org/10.1007/978-3-540-24653-4_33
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3005
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

UN SDGs

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

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-540-24653-4_33

Cite this

Kim, S. M., & Kim, W. (2004). An algorithm for segmenting gaseous objects on images. In G. R. Raidl, S. Cagnoni, J. Branke, D. W. Corne, R. Drechsler, Y. Jin, C. G. Johnson, P. Machado, E. Marchiori, F. Rothlauf, G. D. Smith, & G. Squillero (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 322-328). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3005). Springer Verlag. https://doi.org/10.1007/978-3-540-24653-4_33

Kim, Sung Min ; Kim, Wonha. / An algorithm for segmenting gaseous objects on images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Gunther R. Raidl ; Stefano Cagnoni ; Jurgen Branke ; David W. Corne ; Rolf Drechsler ; Yaochu Jin ; Colin G. Johnson ; Penousal Machado ; Elena Marchiori ; Franz Rothlauf ; George D. Smith ; Giovanni Squillero. Springer Verlag, 2004. pp. 322-328 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inbook{40520078c4e0450ab9dd16b4f848828b,

title = "An algorithm for segmenting gaseous objects on images",

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.",

author = "Kim, \{Sung Min\} and Wonha Kim",

year = "2004",

doi = "10.1007/978-3-540-24653-4\_33",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "322--328",

editor = "Raidl, \{Gunther R.\} and Stefano Cagnoni and Jurgen Branke and Corne, \{David W.\} and Rolf Drechsler and Yaochu Jin and Johnson, \{Colin G.\} and Penousal Machado and Elena Marchiori and Franz Rothlauf and Smith, \{George D.\} and Giovanni Squillero",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

address = "Germany",

}

Kim, SM & Kim, W 2004, An algorithm for segmenting gaseous objects on images. in GR Raidl, S Cagnoni, J Branke, DW Corne, R Drechsler, Y Jin, CG Johnson, P Machado, E Marchiori, F Rothlauf, GD Smith & G Squillero (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3005, Springer Verlag, pp. 322-328. https://doi.org/10.1007/978-3-540-24653-4_33

An algorithm for segmenting gaseous objects on images. / Kim, Sung Min; Kim, Wonha.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Gunther R. Raidl; Stefano Cagnoni; Jurgen Branke; David W. Corne; Rolf Drechsler; Yaochu Jin; Colin G. Johnson; Penousal Machado; Elena Marchiori; Franz Rothlauf; George D. Smith; Giovanni Squillero. Springer Verlag, 2004. p. 322-328 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3005).

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

TY - CHAP

T1 - An algorithm for segmenting gaseous objects on images

AU - Kim, Sung Min

AU - Kim, Wonha

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/35048903192

U2 - 10.1007/978-3-540-24653-4_33

DO - 10.1007/978-3-540-24653-4_33

M3 - Chapter

AN - SCOPUS:35048903192

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 322

EP - 328

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Raidl, Gunther R.

A2 - Cagnoni, Stefano

A2 - Branke, Jurgen

A2 - Corne, David W.

A2 - Drechsler, Rolf

A2 - Jin, Yaochu

A2 - Johnson, Colin G.

A2 - Machado, Penousal

A2 - Marchiori, Elena

A2 - Rothlauf, Franz

A2 - Smith, George D.

A2 - Squillero, Giovanni

PB - Springer Verlag

ER -

Kim SM, Kim W. An algorithm for segmenting gaseous objects on images. In Raidl GR, Cagnoni S, Branke J, Corne DW, Drechsler R, Jin Y, Johnson CG, Machado P, Marchiori E, Rothlauf F, Smith GD, Squillero G, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 322-328. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24653-4_33

An algorithm for segmenting gaseous objects on images

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