Mass classification in mammogram with semi-supervised relief based feature selection

Xiaoming Liu; Jun Liu; Zhilin Feng; Xin Xu; J. Tang

doi:10.1117/12.2051006

10 January 2014 Mass classification in mammogram with semi-supervised relief based feature selection

Xiaoming Liu, Jun Liu, Zhilin Feng, Xin Xu, J. Tang

Proceedings Volume 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013); 90691B (2014) https://doi.org/10.1117/12.2051006
Event: Fifth International Conference on Graphic and Image Processing, 2013, Hong Kong, China

Abstract

Mammogram is currently the best way for early detection of breast cancer. Mass is a typical sign of breast cancer, and the classification of masses as malignant or benign may assist radiologists in reducing the biopsy rate without increasing false negatives. Typically, different geometry and texture features are extracted and utilized to train a classifier to classify a mass. However, not each feature is equally important for a classifier, and some features may indeed decrease the performance of a classifier. In this paper, we investigated the usage of semi-supervised feature selection method for classification. After a mass is extracted from a ROI (region of interest) with level set method. Morphological and texture features are extracted from the segmented regions and surrounding regions. SSLFE (Semi- Supervised Local Feature Extraction, proposed in our previous work) is utilized to select important features for KNN classifier. Mammography images from DDSM were used for experiment. The experimental result shows that by incorporating information embedded in unlabeled data, SSLFE can improve the performance compared to the method without feature selection and traditional Relief method.

Citation Download Citation

Xiaoming Liu, Jun Liu, Zhilin Feng, Xin Xu, and J. Tang "Mass classification in mammogram with semi-supervised relief based feature selection", Proc. SPIE 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013), 90691B (10 January 2014); https://doi.org/10.1117/12.2051006

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available