Hippocampal Shape Classification Using Redundancy Constrained Feature Selection

摘要：

Landmark-based 3D hippocampal shape classification involves high-dimensional descriptor space, many noisy and redundant features, and a very small number of training samples. Feature selection becomes critical in this situation, because it not only improves classification performance, but also identifies the regions that contribute more to shape discrimination. This work identifies the drawbacks of SVM-RFE, and proposes a novel class-separability-based feature selection approach to overcome them. We formulate feature selection as a constrained integer optimization and develop a new algorithm to efficiently and optimally solve this problem. Theoretical analysis and experimental study on both synthetic data and real hippocampus data demonstrate its superior performance over the prevailing SVM-RFE. Our work provides a new efficient feature selection tool for hippocampal shape classification.

作者: Luping Zhou Lei Wang Chunhua Shen Nick Barnes

作者单位: School of Engineering, The Australian National University Embedded Systems Theme, National ICT, Australia

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 266-273

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）