Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images

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

doi:10.1007/978-3-319-14249-4_46

Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images

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

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Brain tumor segmentation and labeling is a challenging task in medical imaging. In this paper, a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed. Based on image reconstruction, we present coupled dictionaries, one dictionary of grayscale brain tumor image patches and one dictionary of tumor labels, which can then be used for automatic multi-label brain tumor segmentation of a test image data. The dictionaries are learned from training images of BraTS-MICCAI and the SPL/NSG brain tumor databases. The label dictionary is proposed to select foreground and background labels for automatic graph-cut segmentation. For quantitative evaluation, five different metric scores are computed using the online evaluation tool provided by the BraTS organizers. Experimental results demonstrate that the proposed approach achieves accurate results and outperforms most of the state-of-the-art methods cited in BraTS-MICCAI challenge.

Original language	English
Title of host publication	Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings
Editors	Jason Jerald, George Bebis, Bahram Parvin, Zhigang Deng, Richard Boyle, El Choubassi Maha, Hui Zhang, Darko Koracin, Ryan McMahan, Steven M. Drucker, Mark Carlson, Kambhamettu Chandra
Place of Publication	Las Vegas
Publisher	Springer
Pages	489-500
Number of pages	12
ISBN (Electronic)	9783319142487
DOIs	https://doi.org/10.1007/978-3-319-14249-4_46
Publication status	Published - 1 Jan 2014
Externally published	Yes
Event	10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States Duration: 8 Dec 2014 → 10 Dec 2014 Conference number: 10

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8887
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Symposium on Visual Computing, ISVC 2014
Abbreviated title	ISVC
Country/Territory	United States
City	Las Vegas
Period	8/12/14 → 10/12/14

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10.1007/978-3-319-14249-4_46

Cite this

Al-Shaikhli, S. D. S., Yang, M. Y., & Rosenhahn, B. (2014). Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images. In J. Jerald, G. Bebis, B. Parvin, Z. Deng, R. Boyle, E. C. Maha, H. Zhang, D. Koracin, R. McMahan, S. M. Drucker, M. Carlson, & K. Chandra (Eds.), Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings (pp. 489-500). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8887). Springer. https://doi.org/10.1007/978-3-319-14249-4_46

Al-Shaikhli, Saif Dawood Salman ; Yang, Michael Ying ; Rosenhahn, Bodo. / Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images. Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. editor / Jason Jerald ; George Bebis ; Bahram Parvin ; Zhigang Deng ; Richard Boyle ; El Choubassi Maha ; Hui Zhang ; Darko Koracin ; Ryan McMahan ; Steven M. Drucker ; Mark Carlson ; Kambhamettu Chandra. Las Vegas : Springer, 2014. pp. 489-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images",

abstract = "Brain tumor segmentation and labeling is a challenging task in medical imaging. In this paper, a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed. Based on image reconstruction, we present coupled dictionaries, one dictionary of grayscale brain tumor image patches and one dictionary of tumor labels, which can then be used for automatic multi-label brain tumor segmentation of a test image data. The dictionaries are learned from training images of BraTS-MICCAI and the SPL/NSG brain tumor databases. The label dictionary is proposed to select foreground and background labels for automatic graph-cut segmentation. For quantitative evaluation, five different metric scores are computed using the online evaluation tool provided by the BraTS organizers. Experimental results demonstrate that the proposed approach achieves accurate results and outperforms most of the state-of-the-art methods cited in BraTS-MICCAI challenge.",

author = "Al-Shaikhli, {Saif Dawood Salman} and Yang, {Michael Ying} and Bodo Rosenhahn",

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doi = "10.1007/978-3-319-14249-4_46",

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Al-Shaikhli, SDS, Yang, MY & Rosenhahn, B 2014, Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images. in J Jerald, G Bebis, B Parvin, Z Deng, R Boyle, EC Maha, H Zhang, D Koracin, R McMahan, SM Drucker, M Carlson & K Chandra (eds), Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8887, Springer, Las Vegas, pp. 489-500, 10th International Symposium on Visual Computing, ISVC 2014, Las Vegas, Nevada, United States, 8/12/14. https://doi.org/10.1007/978-3-319-14249-4_46

Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images. / Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo.

Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. ed. / Jason Jerald; George Bebis; Bahram Parvin; Zhigang Deng; Richard Boyle; El Choubassi Maha; Hui Zhang; Darko Koracin; Ryan McMahan; Steven M. Drucker; Mark Carlson; Kambhamettu Chandra. Las Vegas : Springer, 2014. p. 489-500 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8887).

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

TY - GEN

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AU - Al-Shaikhli, Saif Dawood Salman

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AU - Rosenhahn, Bodo

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N2 - Brain tumor segmentation and labeling is a challenging task in medical imaging. In this paper, a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed. Based on image reconstruction, we present coupled dictionaries, one dictionary of grayscale brain tumor image patches and one dictionary of tumor labels, which can then be used for automatic multi-label brain tumor segmentation of a test image data. The dictionaries are learned from training images of BraTS-MICCAI and the SPL/NSG brain tumor databases. The label dictionary is proposed to select foreground and background labels for automatic graph-cut segmentation. For quantitative evaluation, five different metric scores are computed using the online evaluation tool provided by the BraTS organizers. Experimental results demonstrate that the proposed approach achieves accurate results and outperforms most of the state-of-the-art methods cited in BraTS-MICCAI challenge.

AB - Brain tumor segmentation and labeling is a challenging task in medical imaging. In this paper, a novel patch based dictionary learning algorithm for automatic multi-label brain tumor segmentation is proposed. Based on image reconstruction, we present coupled dictionaries, one dictionary of grayscale brain tumor image patches and one dictionary of tumor labels, which can then be used for automatic multi-label brain tumor segmentation of a test image data. The dictionaries are learned from training images of BraTS-MICCAI and the SPL/NSG brain tumor databases. The label dictionary is proposed to select foreground and background labels for automatic graph-cut segmentation. For quantitative evaluation, five different metric scores are computed using the online evaluation tool provided by the BraTS organizers. Experimental results demonstrate that the proposed approach achieves accurate results and outperforms most of the state-of-the-art methods cited in BraTS-MICCAI challenge.

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Al-Shaikhli SDS, Yang MY, Rosenhahn B. Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images. In Jerald J, Bebis G, Parvin B, Deng Z, Boyle R, Maha EC, Zhang H, Koracin D, McMahan R, Drucker SM, Carlson M, Chandra K, editors, Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings. Las Vegas: Springer. 2014. p. 489-500. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-14249-4_46

Coupled dictionary learning for automatic multi-label brain tumor segmentation in flair MRI images

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