Name
Title | ||
|---|---|---|
Sparse sampling and unsupervised learning of lung texture patterns in pulmonary emphysema: MESA COPD study |
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISBI.2015.7163828 | Biomedical Imaging |
Keywords | Field | DocType |
computerised tomography,diseases,image retrieval,image sampling,image texture,lung,medical image processing,unsupervised learning,CT scans,MESA COPD study,alveolar airspace enlargement,content-based image retrieval,dominant local lung texture patterns,emphysema subtypes,image-based phenotypes,learned patterns,pulmonary emphysema,sparse sampling,textural differences,thoracic computed tomography,unsupervised learning,CT,clustering,emphysema,texture | Lung,Computer science,Image retrieval,Unsupervised learning,Artificial intelligence,Computed tomography,COPD,Computer vision,Pattern recognition,Feature extraction,Sampling (statistics),Radiology,Mesa | Conference |
ISSN | Citations | PageRank |
1945-7928 | 2 | 0.46 |
References | Authors | |
6 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yrjö Tapio Hame | 1 | 29 | 3.50 |
| Elsa D. Angelini | 2 | 740 | 60.44 |
| Megha A. Parikh | 3 | 2 | 0.46 |
| Benjamin M. Smith | 4 | 4 | 1.54 |
| Eric A. Hoffman | 5 | 2 | 0.80 |
| R. Graham Barr | 6 | 7 | 2.35 |
| Andrew F. Laine | 7 | 747 | 83.01 |