TY - GEN
T1 - Medical image classification via SVM using LBP features from saliency-based folded data
AU - Camlica, Zehra
AU - Tizhoosh, H. R.
AU - Khalvati, Farzad
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/3/2
Y1 - 2016/3/2
N2 - Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 xray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.
AB - Good results on image classification and retrieval using support vector machines (SVM) with local binary patterns (LBPs) as features have been extensively reported in the literature where an entire image is retrieved or classified. In contrast, in medical imaging, not all parts of the image may be equally significant or relevant to the image retrieval application at hand. For instance, in lung x-ray image, the lung region may contain a tumour, hence being highly significant whereas the surrounding area does not contain significant information from medical diagnosis perspective. In this paper, we propose to detect salient regions of images during training and fold the data to reduce the effect of irrelevant regions. As a result, smaller image areas will be used for LBP features calculation and consequently classification by SVM. We use IRMA 2009 dataset with 14,410 xray images to verify the performance of the proposed approach. The results demonstrate the benefits of saliency-based folding approach that delivers comparable classification accuracies with state-of-the-art but exhibits lower computational cost and storage requirements, factors highly important for big data analytics.
KW - Folding
KW - Image classification
KW - Local binary patterns
KW - Saliency
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84969627287&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969627287&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2015.131
DO - 10.1109/ICMLA.2015.131
M3 - Conference contribution
AN - SCOPUS:84969627287
T3 - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
SP - 128
EP - 132
BT - Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
Y2 - 9 December 2015 through 11 December 2015
ER -