Breast Tissue Image Classification Based on Semi-supervised Locality Discriminant Projection with Kernels

Authors:
Jun-Bao Li;Yang Yu;Zhi-Ming Yang;Lin-Lin Tang
Affiliations:
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China 150080;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China 150080;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China 150080;School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
Venue:
Journal of Medical Systems
Year:
2012

Citing 18
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Abstract

Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels for breast cancer classification. The contributions of this work lie in: 1) Semi-supervised learning is used into Locality Preserving Projections (LPP) to enhance its performance using side-information together with the unlabelled training samples, while current algorithms only consider the side-information but ignoring the unlabeled training samples. 2) Kernel trick is applied into Semi-supervised LPP to improve its ability in the nonlinear classification. 3) The framework of breast cancer classification with Semi-supervised LPP with kernels is presented. Many experiments are implemented on four breast tissue databases to testify and evaluate the feasibility and affectivity of the proposed scheme.