Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition

Esther Alberts; Giles Tetteh; Stefano Trebeschi; Marie Bieth; Alexander Valentinitsch; Benedikt Wiestler; Claus Zimmer; Bjoern H. Menze

doi:10.1007/978-3-319-67675-3_18

Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition

Esther Alberts, Giles Tetteh, Stefano Trebeschi, Marie Bieth, Alexander Valentinitsch, Benedikt Wiestler, Claus Zimmer, Bjoern H. Menze

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Technical University of Munich

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

10 Scopus citations

Abstract

In this paper, we present a multi-modal medical image classification framework classifying brain tumor glioblastomas in genetic classes based on DNA methylation status. The framework makes use of computationally efficient 3D implementations of short local image descriptors, such as LBP, BRIEF and HOG, which are processed by a Bag-of-Patterns model to represent image regions, as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks. The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts.

Original language	English
Title of host publication	Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings
Editors	M. Jorge Cardoso, Tal Arbel
Publisher	Springer Verlag
Pages	201-209
Number of pages	9
ISBN (Print)	9783319676746
DOIs	https://doi.org/10.1007/978-3-319-67675-3_18
State	Published - 2017
Event	1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 10 Sep 2017 → 14 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10551 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	10/09/17 → 14/09/17

Access to Document

10.1007/978-3-319-67675-3_18

Cite this

Alberts, E., Tetteh, G., Trebeschi, S., Bieth, M., Valentinitsch, A., Wiestler, B., Zimmer, C., & Menze, B. H. (2017). Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition. In M. J. Cardoso, & T. Arbel (Eds.), Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 201-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10551 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67675-3_18

Alberts, Esther ; Tetteh, Giles ; Trebeschi, Stefano et al. / Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition. Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings. editor / M. Jorge Cardoso ; Tal Arbel. Springer Verlag, 2017. pp. 201-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition",

abstract = "In this paper, we present a multi-modal medical image classification framework classifying brain tumor glioblastomas in genetic classes based on DNA methylation status. The framework makes use of computationally efficient 3D implementations of short local image descriptors, such as LBP, BRIEF and HOG, which are processed by a Bag-of-Patterns model to represent image regions, as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks. The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts.",

author = "Esther Alberts and Giles Tetteh and Stefano Trebeschi and Marie Bieth and Alexander Valentinitsch and Benedikt Wiestler and Claus Zimmer and Menze, {Bjoern H.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 ; Conference date: 10-09-2017 Through 14-09-2017",

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doi = "10.1007/978-3-319-67675-3_18",

language = "English",

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Alberts, E, Tetteh, G, Trebeschi, S, Bieth, M, Valentinitsch, A, Wiestler, B, Zimmer, C & Menze, BH 2017, Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition. in MJ Cardoso & T Arbel (eds), Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10551 LNCS, Springer Verlag, pp. 201-209, 1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017, Quebec City, Canada, 10/09/17. https://doi.org/10.1007/978-3-319-67675-3_18

Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition. / Alberts, Esther; Tetteh, Giles; Trebeschi, Stefano et al.
Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings. ed. / M. Jorge Cardoso; Tal Arbel. Springer Verlag, 2017. p. 201-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10551 LNCS).

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

TY - GEN

T1 - Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition

AU - Alberts, Esther

AU - Tetteh, Giles

AU - Trebeschi, Stefano

AU - Bieth, Marie

AU - Valentinitsch, Alexander

AU - Wiestler, Benedikt

AU - Zimmer, Claus

AU - Menze, Bjoern H.

PY - 2017

Y1 - 2017

N2 - In this paper, we present a multi-modal medical image classification framework classifying brain tumor glioblastomas in genetic classes based on DNA methylation status. The framework makes use of computationally efficient 3D implementations of short local image descriptors, such as LBP, BRIEF and HOG, which are processed by a Bag-of-Patterns model to represent image regions, as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks. The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts.

AB - In this paper, we present a multi-modal medical image classification framework classifying brain tumor glioblastomas in genetic classes based on DNA methylation status. The framework makes use of computationally efficient 3D implementations of short local image descriptors, such as LBP, BRIEF and HOG, which are processed by a Bag-of-Patterns model to represent image regions, as well as deep-learned features acquired by denoising auto-encoders and hand-crafted shape features calculated on segmentation masks. The framework is validated against a cohort of 116 brain tumor patients from the TCIA database and is shown to obtain high accuracies even though the same image-based classification task is hardly possible for medical experts.

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U2 - 10.1007/978-3-319-67675-3_18

DO - 10.1007/978-3-319-67675-3_18

M3 - Conference contribution

AN - SCOPUS:85029749107

SN - 9783319676746

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

SP - 201

EP - 209

BT - Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings

A2 - Cardoso, M. Jorge

A2 - Arbel, Tal

PB - Springer Verlag

T2 - 1st International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd International Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017

Y2 - 10 September 2017 through 14 September 2017

ER -

Alberts E, Tetteh G, Trebeschi S, Bieth M, Valentinitsch A, Wiestler B et al. Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition. In Cardoso MJ, Arbel T, editors, Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics - 1st International Workshop, GRAIL 2017 6th International Workshop, MFCA 2017 and 3rd International Workshop, MICGen 2017 Held in Conjunction with MICCAI 2017, Proceedings. Springer Verlag. 2017. p. 201-209. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67675-3_18

Multi-modal Image classification using low-dimensional texture features for genomic brain tumor recognition

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