Title: Iterated Large-Margin Discriminant Analysis for feature Dimensionality Reduction in medical image retrieval

Authors: Jingyan Wang; Yongping Li; Elena Marchiori; Chao Wang

Addresses: Shanghai Institute of Applied Physics, Chinese Academy of Science, 2019 Jialuo Road, Jiading District, Shanghai 201800, China. ' Shanghai Institute of Applied Physics, Chinese Academy of Science, 2019 Jialuo Road, Jiading District, Shanghai 201800, China. ' Faculty of Science, Department of Computer Science, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands. ' Department of Biomedical Engineering, Oregon Health and Science University, 20000 NW Walker Rd., Beaverton, OR 97006, USA

Abstract: Feature Dimensionality Reduction (DR) is an important topic in medical image retrieval. However, most medical image retrieval systems use naive principal component analysis for DR, which is not optimal. Recently, the class conditional nearest neighbour relation over pairs of points was introduced. In this paper, we extend it to class conditional K-nearest neighbour (ccknn). Based on ccknn, we de?ne the within-class and between-class graph for Large-Margin Discriminat Analysis (LMDA). Moreover, an iterative expectation-maximisation framework is applied to LMDA to boost the performance. The experimental results demonstrate that the proposed approach yields signi?cant improvements over the state-of-the-art DR algorithms.

Keywords: medical image retrieval; dimensionality reduction; class conditional neighbouring graph; iterated large-margin discriminant analysis; expectation-maximisation framework; feature dimensionality; medical imaging.

DOI: 10.1504/IJBET.2011.043174

International Journal of Biomedical Engineering and Technology, 2011 Vol.7 No.2, pp.116 - 134

Published online: 21 Jan 2015 *

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