A robust energy minimization algorithm for MS-lesion segmentation

Zhaoxuan Gong; Dazhe Zhao; Chunming Li; Wenjun Tan; Christos Davatzikos

doi:10.1007/978-3-319-27857-5_47

A robust energy minimization algorithm for MS-lesion segmentation

Zhaoxuan Gong, Dazhe Zhao, Chunming Li, Wenjun Tan, Christos Davatzikos

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation.

Original language	English (US)
Title of host publication	Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings
Editors	Mark Elendt, Richard Boyle, Eric Ragan, Bahram Parvin, Rogerio Feris, Tim McGraw, Ioannis Pavlidis, Regis Kopper, George Bebis, Darko Koracin, Zhao Ye, Gunther Weber
Publisher	Springer Verlag
Pages	521-530
Number of pages	10
ISBN (Print)	9783319278568
DOIs	https://doi.org/10.1007/978-3-319-27857-5_47
State	Published - 2015
Event	11th International Symposium on Advances in Visual Computing, ISVC 2015 - Las Vegas, United States Duration: Dec 14 2015 → Dec 16 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9474
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Symposium on Advances in Visual Computing, ISVC 2015
Country/Territory	United States
City	Las Vegas
Period	12/14/15 → 12/16/15

Keywords

Bias field
Energy minimization
MR
Multiple sclerosis lesion
True image

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-27857-5_47

Cite this

Gong, Z., Zhao, D., Li, C., Tan, W., & Davatzikos, C. (2015). A robust energy minimization algorithm for MS-lesion segmentation. In M. Elendt, R. Boyle, E. Ragan, B. Parvin, R. Feris, T. McGraw, I. Pavlidis, R. Kopper, G. Bebis, D. Koracin, Z. Ye, & G. Weber (Eds.), Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings (pp. 521-530). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9474). Springer Verlag. https://doi.org/10.1007/978-3-319-27857-5_47

A robust energy minimization algorithm for MS-lesion segmentation. / Gong, Zhaoxuan; Zhao, Dazhe; Li, Chunming et al.
Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings. ed. / Mark Elendt; Richard Boyle; Eric Ragan; Bahram Parvin; Rogerio Feris; Tim McGraw; Ioannis Pavlidis; Regis Kopper; George Bebis; Darko Koracin; Zhao Ye; Gunther Weber. Springer Verlag, 2015. p. 521-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9474).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gong, Z, Zhao, D, Li, C, Tan, W & Davatzikos, C 2015, A robust energy minimization algorithm for MS-lesion segmentation. in M Elendt, R Boyle, E Ragan, B Parvin, R Feris, T McGraw, I Pavlidis, R Kopper, G Bebis, D Koracin, Z Ye & G Weber (eds), Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9474, Springer Verlag, pp. 521-530, 11th International Symposium on Advances in Visual Computing, ISVC 2015, Las Vegas, United States, 12/14/15. https://doi.org/10.1007/978-3-319-27857-5_47

Gong Z, Zhao D, Li C, Tan W, Davatzikos C. A robust energy minimization algorithm for MS-lesion segmentation. In Elendt M, Boyle R, Ragan E, Parvin B, Feris R, McGraw T, Pavlidis I, Kopper R, Bebis G, Koracin D, Ye Z, Weber G, editors, Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings. Springer Verlag. 2015. p. 521-530. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27857-5_47

Gong, Zhaoxuan ; Zhao, Dazhe ; Li, Chunming et al. / A robust energy minimization algorithm for MS-lesion segmentation. Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings. editor / Mark Elendt ; Richard Boyle ; Eric Ragan ; Bahram Parvin ; Rogerio Feris ; Tim McGraw ; Ioannis Pavlidis ; Regis Kopper ; George Bebis ; Darko Koracin ; Zhao Ye ; Gunther Weber. Springer Verlag, 2015. pp. 521-530 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation.",

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author = "Zhaoxuan Gong and Dazhe Zhao and Chunming Li and Wenjun Tan and Christos Davatzikos",

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doi = "10.1007/978-3-319-27857-5_47",

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N1 - Funding Information: This research was partly supported by National Natural Science Foundation of China(NSFC) under Grant No. 61302012 and No. 61172002, the Fundamental Research Funds for the Central Universities under Grant N130418002 and N120518001, and Liaoning Natural Science Foundation under Grant No. 2013020021. Publisher Copyright: © Springer International Publishing Switzerland 2015

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N2 - The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation.

AB - The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic robust algorithm for lesion segmentation based on MR images is proposed. This method takes full advantage of the decomposition of MR images into the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity. An energy function is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions and white matter. Then postprocessing operations is used to select the most plausible lesions in the obtained hyperintense signals. The experimental results show that our approach is effective and robust for the lesion segmentation.

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A robust energy minimization algorithm for MS-lesion segmentation

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Keywords

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