Details
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2774-2778 |
| Number of pages | 5 |
| ISBN (electronic) | 9781479957514 |
| Publication status | Published - 28 Jan 2014 |
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.
Keywords
- Brain tumor classification, dictionary learning, gray level co-occurance matrix, sparse coding, topological matrix
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
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2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2774-2778 7025561.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Brain tumor classification using sparse coding and dictionary learning
AU - Al-Shaikhli, Saif Dawood Salman
AU - Yang, Michael Ying
AU - Rosenhahn, Bodo
PY - 2014/1/28
Y1 - 2014/1/28
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.
KW - Brain tumor classification
KW - dictionary learning
KW - gray level co-occurance matrix
KW - sparse coding
KW - topological matrix
UR - http://www.scopus.com/inward/record.url?scp=84949929366&partnerID=8YFLogxK
U2 - 10.1109/icip.2014.7025561
DO - 10.1109/icip.2014.7025561
M3 - Conference contribution
AN - SCOPUS:84949929366
SP - 2774
EP - 2778
BT - 2014 IEEE International Conference on Image Processing, ICIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -