loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2008 21st IEEE International Symposium on Computer-Based Medical Systems
Lung Tissue Classification in HRCT Data Integrating the Clinical Context
June 17-June 19
ISBN: 978-0-7695-3165-6
ASCII Text
x 
 
Adrien Depeursinge, Jimison Iavindrasana, Gilles Cohen, Alexandra Platon, Pierre-Alexandre Poletti, Henning M?, "Lung Tissue Classification in HRCT Data Integrating the Clinical Context," Computer-Based Medical Systems, IEEE Symposium on, pp. 542-547, 2008 21st IEEE International Symposium on Computer-Based Medical Systems, 2008.
 
 
BibTex
x
 
@article{ 10.1109/CBMS.2008.112,
author = {Adrien Depeursinge and Jimison Iavindrasana and Gilles Cohen and Alexandra Platon and Pierre-Alexandre Poletti and Henning M?},
title = {Lung Tissue Classification in HRCT Data Integrating the Clinical Context},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2008},
issn = {1063-7125},
pages = {542-547},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2008.112},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - Lung Tissue Classification in HRCT Data Integrating the Clinical Context
SN - 1063-7125
SP542
EP547
A1 - Adrien Depeursinge,
A1 - Jimison Iavindrasana,
A1 - Gilles Cohen,
A1 - Alexandra Platon,
A1 - Pierre-Alexandre Poletti,
A1 - Henning M?,
PY - 2008
KW - multimodal classification
KW - machine learning
KW - image processing
KW - high-resolution computed tomography
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
In this paper, we investigate the influence of the clinical context of high–resolution computed tomography (HRCT) images of the chest on tissue classification. Evaluation of the classification performance is based on high–quality visual data extracted from clinical routine. The clinical attributes with highest information gain ratio show to be relevant and consistent for the classification of lung tissue patterns. A combination of visual and clinical attributes allowed a mean of 93% correct predictions of testing instances among the five classes of lung tissue with optimized support vector machines (SVM), which represents a significant benefit of 8% compared to a pure visually–based classification.
Index Terms:
multimodal classification, machine learning, image processing, high-resolution computed tomography
Citation:
Adrien Depeursinge, Jimison Iavindrasana, Gilles Cohen, Alexandra Platon, Pierre-Alexandre Poletti, Henning M?, "Lung Tissue Classification in HRCT Data Integrating the Clinical Context," cbms, pp.542-547, 2008 21st IEEE International Symposium on Computer-Based Medical Systems, 2008
Usage of this product signifies your acceptance of the Terms of Use.