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Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods
Salt Lake City, Utah June 22-June 23
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.5619th IEEE Symposium on Computer-Based ...
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Daniel Smutek, Akinobu Shimiz, Ludvik Tesar, Hidefumi Kobatake, Shigeru Nawano, Stepan Svacina, "Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods," Computer-Based Medical Systems, IEEE Symposium on, pp. 405-412, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006.
 
 
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@article{ 10.1109/CBMS.2006.56,
author = {Daniel Smutek and Akinobu Shimiz and Ludvik Tesar and Hidefumi Kobatake and Shigeru Nawano and Stepan Svacina},
title = {Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods},
journal ={Computer-Based Medical Systems, IEEE Symposium on},
volume = {0},
year = {2006},
issn = {1063-7125},
pages = {405-412},
doi = {http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.56},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer-Based Medical Systems, IEEE Symposium on
TI - Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods
SN - 1063-7125
SP405
EP412
A1 - Daniel Smutek,
A1 - Akinobu Shimiz,
A1 - Ludvik Tesar,
A1 - Hidefumi Kobatake,
A1 - Shigeru Nawano,
A1 - Stepan Svacina,
PY - 2006
KW - null
VL - 0
JA - Computer-Based Medical Systems, IEEE Symposium on
ER -
 
Daniel Smutek, Charles University, Czech Republic
Akinobu Shimiz, Tokyo University of Agriculture and Technology, Japan
Ludvik Tesar, Tokyo University of Agriculture and Technology, Japan
Hidefumi Kobatake, Tokyo University of Agriculture and Technology, Japan
Shigeru Nawano, National Cancer Center Hospital East, Japan
Stepan Svacina, Charles University, Czech Republic
Goal: To develop a computer-aided diagnostic system for diagnosing different internal medicine diseases based on imaging methods. We focus on focal liver lesions in CT images. Methods: The diagnosing process follows the learning phase from known images. For image description, 22 first-order and 108 secondorder texture features are used. They are used as input for network of Bayes classifiers. Results: The best value of 100% success of classification between hepatocellular carcinoma and non-parasitic solitary liver cysts was achieved. Conclusion: The method allows discriminating between different liver diseases based on computer imaging. The method may be very useful in cases where any internal images of patients already diagnosed are available.
Citation:
Daniel Smutek, Akinobu Shimiz, Ludvik Tesar, Hidefumi Kobatake, Shigeru Nawano, Stepan Svacina, "Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods," cbms, pp.405-412, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006
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