Medical image segmentation using multi-level set partitioning with topological graph prior

Saif Dawood Salman Al-Shaikhli; Michael Ying Yang; Bodo Rosenhahn

doi:10.1007/978-3-642-53926-8_15

Medical image segmentation using multi-level set partitioning with topological graph prior

Saif Dawood Salman Al-Shaikhli, Michael Ying Yang, Bodo Rosenhahn

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

7 Citations (Scopus)

Abstract

In this paper, we propose an approach for multi-region segmentation based on a topological graph prior within a multi-level set (MLS) formulation. We consider topological graph prior information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level that would be difficult to segment correctly using standard methods. We describe our algorithm and show the graph prior technique to explain how it gives precise multi-region segmentation. We validate our algorithm with numerous abdominal and brain image databases and compare it to other multi-region segmentation methods to demonstrate its accuracy and computational efficiency.

Original language	English
Title of host publication	Image and Video Technology – PSIVT 2013 Workshops
Subtitle of host publication	GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers
Editors	Fay Huang, Akihiro Sugimoto
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	157-168
Number of pages	12
ISBN (Electronic)	978-3-642-53926-8
ISBN (Print)	978-3-642-53925-1
DOIs	https://doi.org/10.1007/978-3-642-53926-8_15
Publication status	Published - 1 Jan 2014
Externally published	Yes
Event	6th Pacific-Rim Symposium on Image and Video Technology 2013 - Guanajuato, Mexico Duration: 28 Oct 2013 → 1 Nov 2013 Conference number: 6 http://www.psivt.org/psivt2013/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8334
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th Pacific-Rim Symposium on Image and Video Technology 2013
Abbreviated title	PSIVT 2013
Country	Mexico
City	Guanajuato
Period	28/10/13 → 1/11/13
Internet address	http://www.psivt.org/psivt2013/

Keywords

Level set
Medical image
Multi-region
Segmentation
Topological graph

Access to Document

10.1007/978-3-642-53926-8_15

Cite this

Al-Shaikhli, S. D. S., Yang, M. Y., & Rosenhahn, B. (2014). Medical image segmentation using multi-level set partitioning with topological graph prior. In F. Huang, & A. Sugimoto (Eds.), Image and Video Technology – PSIVT 2013 Workshops: GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers (pp. 157-168). (Lecture Notes in Computer Science; Vol. 8334). Springer. https://doi.org/10.1007/978-3-642-53926-8_15

Al-Shaikhli, Saif Dawood Salman ; Yang, Michael Ying ; Rosenhahn, Bodo. / Medical image segmentation using multi-level set partitioning with topological graph prior. Image and Video Technology – PSIVT 2013 Workshops: GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers. editor / Fay Huang ; Akihiro Sugimoto. Berlin, Heidelberg : Springer, 2014. pp. 157-168 (Lecture Notes in Computer Science).

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abstract = "In this paper, we propose an approach for multi-region segmentation based on a topological graph prior within a multi-level set (MLS) formulation. We consider topological graph prior information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level that would be difficult to segment correctly using standard methods. We describe our algorithm and show the graph prior technique to explain how it gives precise multi-region segmentation. We validate our algorithm with numerous abdominal and brain image databases and compare it to other multi-region segmentation methods to demonstrate its accuracy and computational efficiency.",

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Al-Shaikhli, SDS, Yang, MY & Rosenhahn, B 2014, Medical image segmentation using multi-level set partitioning with topological graph prior. in F Huang & A Sugimoto (eds), Image and Video Technology – PSIVT 2013 Workshops: GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers. Lecture Notes in Computer Science, vol. 8334, Springer, Berlin, Heidelberg, pp. 157-168, 6th Pacific-Rim Symposium on Image and Video Technology 2013, Guanajuato, Mexico, 28/10/13. https://doi.org/10.1007/978-3-642-53926-8_15

Medical image segmentation using multi-level set partitioning with topological graph prior. / Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo.

Image and Video Technology – PSIVT 2013 Workshops: GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers. ed. / Fay Huang; Akihiro Sugimoto. Berlin, Heidelberg : Springer, 2014. p. 157-168 (Lecture Notes in Computer Science; Vol. 8334).

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

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AB - In this paper, we propose an approach for multi-region segmentation based on a topological graph prior within a multi-level set (MLS) formulation. We consider topological graph prior information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level that would be difficult to segment correctly using standard methods. We describe our algorithm and show the graph prior technique to explain how it gives precise multi-region segmentation. We validate our algorithm with numerous abdominal and brain image databases and compare it to other multi-region segmentation methods to demonstrate its accuracy and computational efficiency.

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Al-Shaikhli SDS, Yang MY, Rosenhahn B. Medical image segmentation using multi-level set partitioning with topological graph prior. In Huang F, Sugimoto A, editors, Image and Video Technology – PSIVT 2013 Workshops: GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers. Berlin, Heidelberg: Springer. 2014. p. 157-168. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-53926-8_15

Medical image segmentation using multi-level set partitioning with topological graph prior

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