title: Locality-constrained Subcluster Representation Ensemble for lung image classification.
abstract: In this paper, we propose a new Locality-constrained Subcluster Representation Ensemble (LSRE) model, to classify high-resolution computed tomography (HRCT) images of interstitial lung diseases (ILDs). Medical images normally exhibit large intra-class variation and inter-class ambiguity in the feature space. Modelling of feature space separation between different classes is thus problematic and this affects the classification performance. Our LSRE model tackles this issue in an ensemble classification construct. The image set is first partitioned into subclusters based on spectral clustering with approximation-based affinity matrix. Basis representations of the test image are then generated with sparse approximation from the subclusters. These basis representations are finally fused with approximation- and distribution-based weights to classify the test image. Our experimental results on a large HRCT database show good performance improvement over existing popular classifiers.
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia. Electronic address: yson1723@uni.sydney.edu.au.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.affiliation: Department of Computer Science and Engineering, University of Texas, Arlington, TX 76019, USA.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia.
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Clustering; Ensemble classification; Locality-constrained linear coding; Medical image classification; Sparse representation