Conference proceeding
Sparse sampling and unsupervised learning of lung texture patterns in pulmonary emphysema: MESA COPD study
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Vol.2015-, pp.109-113
04/2015
DOI: 10.1109/ISBI.2015.7163828
Abstract
Pulmonary emphysema is defined morphologically by enlargement of alveolar airspaces and manifests as textural differences on thoracic computed tomography (CT). This work presents an unsupervised approach to extract the most dominant local lung texture patterns on CT scans. Since the method does not use manually annotated labels restricted to predefined emphysema subtypes, it can be used for discovery of novel image-based phenotypes with greater efficiency and reliability. This study demonstrates the applicability of the learned patterns for content-based image retrieval.
Details
- Title: Subtitle
- Sparse sampling and unsupervised learning of lung texture patterns in pulmonary emphysema: MESA COPD study
- Creators
- Yrjo Hame - Columbia UniversityElsa D Angelini - Columbia UniversityMegha A Parikh - Columbia UniversityBenjamin M Smith - Columbia UniversityEric A Hoffman - University of IowaR. Graham Barr - Columbia UniversityAndrew F Laine - Columbia University
- Resource Type
- Conference proceeding
- Publication Details
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Vol.2015-, pp.109-113
- Publisher
- IEEE
- DOI
- 10.1109/ISBI.2015.7163828
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2015
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Internal Medicine; Radiology
- Record Identifier
- 9984318789902771
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