Abstract
Breast cancer is one of the most common diseases among women. Content-based mammogram retrieval has been proposed to aid various medical procedures. To develop a content-based mammogram retrieval system, textural feature extraction is one of the crucial requirements. This study proposes a Gabor filtering method for the extraction of textural features, which firstly performs Gabor filtering on the underlying image, applies the physical properties of a probability wave to probability transformation and then computes features to describe the textural pattern of the mammogram. This study also proposes an adaptive strategy for feature selection, filter selection and feature weighting, which utilizes a user's relevance feedback to reduce the redundancy in the representation and incorporates the user's information needs in image retrieval. Experimental results show that hypothesis tests can effectively find discriminated features and this retrieval system can improve its performance through just a few rounds of relevance feedback.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007 |
| Pages | 1503-1506 |
| Number of pages | 4 |
| Publication status | Published - 2007 |
| Event | IEEE International Conference onMultimedia and Expo, ICME 2007 - Beijing, China Duration: 02 Jul 2007 → 05 Jul 2007 |
Conference
| Conference | IEEE International Conference onMultimedia and Expo, ICME 2007 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 02/07/07 → 05/07/07 |
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Effective extraction of Gabor features for adaptive mammogram retrieval. / Wei, Chia Hung; Li, Yue; Li, Chang Tsun.
Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007. 2007. p. 1503-1506 4284947.Research output: Book chapter/Published conference paper › Conference paper
TY - GEN
T1 - Effective extraction of Gabor features for adaptive mammogram retrieval
AU - Wei, Chia Hung
AU - Li, Yue
AU - Li, Chang Tsun
PY - 2007
Y1 - 2007
N2 - Breast cancer is one of the most common diseases among women. Content-based mammogram retrieval has been proposed to aid various medical procedures. To develop a content-based mammogram retrieval system, textural feature extraction is one of the crucial requirements. This study proposes a Gabor filtering method for the extraction of textural features, which firstly performs Gabor filtering on the underlying image, applies the physical properties of a probability wave to probability transformation and then computes features to describe the textural pattern of the mammogram. This study also proposes an adaptive strategy for feature selection, filter selection and feature weighting, which utilizes a user's relevance feedback to reduce the redundancy in the representation and incorporates the user's information needs in image retrieval. Experimental results show that hypothesis tests can effectively find discriminated features and this retrieval system can improve its performance through just a few rounds of relevance feedback.
AB - Breast cancer is one of the most common diseases among women. Content-based mammogram retrieval has been proposed to aid various medical procedures. To develop a content-based mammogram retrieval system, textural feature extraction is one of the crucial requirements. This study proposes a Gabor filtering method for the extraction of textural features, which firstly performs Gabor filtering on the underlying image, applies the physical properties of a probability wave to probability transformation and then computes features to describe the textural pattern of the mammogram. This study also proposes an adaptive strategy for feature selection, filter selection and feature weighting, which utilizes a user's relevance feedback to reduce the redundancy in the representation and incorporates the user's information needs in image retrieval. Experimental results show that hypothesis tests can effectively find discriminated features and this retrieval system can improve its performance through just a few rounds of relevance feedback.
UR - http://www.scopus.com/inward/record.url?scp=46449092520&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=46449092520&partnerID=8YFLogxK
M3 - Conference paper
AN - SCOPUS:46449092520
SN - 1424410177
SN - 9781424410170
SP - 1503
EP - 1506
BT - Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007
ER -