Locality-constrained Subcluster Representation Ensemble for lung image classification

Yang Song, Weidong Cai, Heng Huang, Yun Zhou, Yue Wang, David Dagan Feng

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)102-113
Number of pages12
JournalMedical Image Analysis
Volume22
Issue number1
DOIs
StatePublished - May 1 2015

Keywords

  • Clustering
  • Ensemble classification
  • Locality-constrained linear coding
  • Medical image classification
  • Sparse representation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology

Fingerprint Dive into the research topics of 'Locality-constrained Subcluster Representation Ensemble for lung image classification'. Together they form a unique fingerprint.

Cite this