Mammographic image classification using histogram intersection

Authors:
Erkang Cheng;Nianhua Xie;Haibin Lin;Predrag R. Bakic;Andrew D. A. Maidment;Vasileios Megalooikonomou
Affiliations:
Center for Infonnation Science and Technology, Temple University, Philadelphia, PA;Center for Infonnation Science and Technology, Temple University, Philadelphia, PA;Center for Infonnation Science and Technology, Temple University, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA;Department of Radiology, University of Pennsylvania, Philadelphia, PA;Data Engineering Laboratory, Temple University, Philadelphia, PA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

In this paper we propose using histogram intersection for mammographic image classification. First, we use the bag-of-words model for image representation, which captures the texture information by collecting local patch statistics. Then, we propose using normalized histogram intersection (HI) as a similarity measure with the K-nearest neighbor (KNN) classifier. Furthermore, by taking advantage of the fact that HI forms a Mercer kernel, we combine HI with support vector machines (SVM), which further improves the classification performance. The proposed methods are evaluated on a galactographic dataset and are compared with several previously used methods. In a thorough evaluation containing about 288 different experimental configurations, the proposed methods demonstrate promising results.