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Locality-constrained Subcluster Representation Ensemble for Lung Image Classification
- Publication Year :
- 2015
- Publisher :
- Elsevier, 2015.
-
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.
- Subjects :
- Feature vector
Health Informatics
Sensitivity and Specificity
Pattern Recognition, Automated
Machine Learning
Humans
Radiology, Nuclear Medicine and imaging
Cluster analysis
Representation (mathematics)
Lung
Mathematics
Radiological and Ultrasound Technology
Basis (linear algebra)
Contextual image classification
Standard test image
business.industry
Pattern recognition
Sparse approximation
Computer Graphics and Computer-Aided Design
Spectral clustering
Radiographic Image Enhancement
Radiographic Image Interpretation, Computer-Assisted
Radiography, Thoracic
Computer Vision and Pattern Recognition
Artificial intelligence
Lung Diseases, Interstitial
Tomography, X-Ray Computed
business
Algorithms
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....236718c7f41a73747f855ed95c8e8acf