Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)

Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis

Authors
Bo Liu1, H. D. Cheng, Jianghua Huang, Jiafeng Liu, Tang XIanglong
1Harbin Institute of Technology
Corresponding Author
Bo Liu
Available Online December 2008.
DOI
https://doi.org/10.2991/jcis.2008.33How to use a DOI?
Keywords
texture classification, support vector machine (SVM), computer aided diagnosis, breast ultrasound (BUS) imaging
Abstract

In this paper, a novel fully automatic classification method of breast tumors using ultrasound (US) image is proposed. The proposed method can be divided into two steps: “ROI generation step” and “ROI classification step”. In the ROI generation step, the proposed method fo-cuses on finding a credible ROI instead of finding the precise location of the breast tumor. In the ROI classification step, lo-cal textures in the ROI are considered with a novel strategy. Both steps were implemented by utilizing supervised tex-ture classification approach. The experi-ments demonstrate that the proposed method is effective and useful for classi-fying breast tumors.

Copyright
© 2008, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
Series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.33How to use a DOI?
Copyright
© 2008, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Bo Liu
AU  - H. D. Cheng
AU  - Jianghua Huang
AU  - Jiafeng Liu
AU  - Tang XIanglong
PY  - 2008/12
DA  - 2008/12
TI  - Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis
BT  - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008)
PB  - Atlantis Press
SP  - 188
EP  - 194
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.33
DO  - https://doi.org/10.2991/jcis.2008.33
ID  - Liu2008/12
ER  -