Details
| Original language | English |
|---|---|
| Title of host publication | Image and Video Technology |
| Subtitle of host publication | PSIVT 2013 Workshops - GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Revised Selected Papers |
| Publisher | Springer Verlag |
| Pages | 157-168 |
| Number of pages | 12 |
| ISBN (electronic) | 9783642539251 |
| Publication status | Published - 2014 |
| Event | 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2013 with GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013 - Guanajuato, Mexico Duration: 28 Oct 2013 → 29 Oct 2013 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8334 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
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.
Keywords
- Level set, Medical image, Multi-region, Segmentation, Topological graph
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Image and Video Technology: PSIVT 2013 Workshops - GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Revised Selected Papers. Springer Verlag, 2014. p. 157-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8334).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Medical image segmentation using multi-level set partitioning with topological graph prior
AU - Al-Shaikhli, Saif Dawood Salman
AU - Yang, Michael Ying
AU - Rosenhahn, Bodo
PY - 2014
Y1 - 2014
N2 - 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.
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.
KW - Level set
KW - Medical image
KW - Multi-region
KW - Segmentation
KW - Topological graph
UR - http://www.scopus.com/inward/record.url?scp=84927608184&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-53926-8_15
DO - 10.1007/978-3-642-53926-8_15
M3 - Conference contribution
AN - SCOPUS:84927608184
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 157
EP - 168
BT - Image and Video Technology
PB - Springer Verlag
T2 - 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2013 with GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013
Y2 - 28 October 2013 through 29 October 2013
ER -