Brain tumor classification using sparse coding and dictionary learning

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

doi:10.1109/ICIP.2014.7025561

Brain tumor classification using sparse coding and dictionary learning

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

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

20 Citations (Scopus)

Abstract

Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.

Original language	English
Title of host publication	2014 IEEE International Conference on Image Processing (ICIP)
Place of Publication	Paris
Pages	2774-2778
Number of pages	5
ISBN (Electronic)	978-1-4799-5751-4
DOIs	https://doi.org/10.1109/ICIP.2014.7025561
Publication status	Published - Oct 2014
Externally published	Yes
Event	IEEE International Conference on Image Processing, ICIP 2014 - Paris, France Duration: 27 Oct 2014 → 30 Oct 2014 https://icip2014.wp.imt.fr/

Conference

Conference	IEEE International Conference on Image Processing, ICIP 2014
Abbreviated title	ICIP
Country/Territory	France
City	Paris
Period	27/10/14 → 30/10/14
Internet address	https://icip2014.wp.imt.fr/

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10.1109/ICIP.2014.7025561

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title = "Brain tumor classification using sparse coding and dictionary learning",

abstract = "Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.",

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

year = "2014",

month = oct,

doi = "10.1109/ICIP.2014.7025561",

language = "English",

pages = "2774--2778",

booktitle = "2014 IEEE International Conference on Image Processing (ICIP)",

note = "IEEE International Conference on Image Processing, ICIP 2014, ICIP ; Conference date: 27-10-2014 Through 30-10-2014",

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T1 - Brain tumor classification using sparse coding and dictionary learning

AU - Al-shaikhli, Saif Dawood Salman

AU - Yang, Michael Ying

AU - Rosenhahn, Bodo

PY - 2014/10

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N2 - Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.

AB - Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.

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M3 - Conference contribution

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CY - Paris

T2 - IEEE International Conference on Image Processing, ICIP 2014

Y2 - 27 October 2014 through 30 October 2014

ER -

Brain tumor classification using sparse coding and dictionary learning

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