Abstract
In this study, we make a system which automatically discriminates the severity of prostate tumor. This system includes the automatic gland extraction and the support vector machine as discriminant analysis. The automatic gland extraction is difficult in recent studies because of ill-defined of glands. Also, support vector machine makes up for a fault of discriminant analysis, Mahalanobis' generalized distance which was used in recent studies. As the result of that, the rate of accuracy for discriminant is individually 96.0% as the first step , and 80.9% as the second step, so we confirm the usefulness of the algorism used in this study.
| Original language | English |
|---|---|
| Title of host publication | IFMBE Proceedings |
| Pages | 720-723 |
| Number of pages | 4 |
| Volume | 25 |
| Edition | 4 |
| DOIs | |
| Publication status | Published - 2009 |
| Event | World Congress on Medical Physics and Biomedical Engineering: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics - Munich, Germany Duration: 2009 Sep 7 → 2009 Sep 12 |
Other
| Other | World Congress on Medical Physics and Biomedical Engineering: Image Processing, Biosignal Processing, Modelling and Simulation, Biomechanics |
|---|---|
| Country | Germany |
| City | Munich |
| Period | 09/9/7 → 09/9/12 |
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Keywords
- Automatic extraction
- Prostate tumor
- Support vector machine
- Texture analysis
ASJC Scopus subject areas
- Biomedical Engineering
- Bioengineering
Cite this
Discriminant system for severity of prostate tumor. / Tanaka, Toshiyuki; Suzuki, A.
IFMBE Proceedings. Vol. 25 4. ed. 2009. p. 720-723.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Discriminant system for severity of prostate tumor
AU - Tanaka, Toshiyuki
AU - Suzuki, A.
PY - 2009
Y1 - 2009
N2 - In this study, we make a system which automatically discriminates the severity of prostate tumor. This system includes the automatic gland extraction and the support vector machine as discriminant analysis. The automatic gland extraction is difficult in recent studies because of ill-defined of glands. Also, support vector machine makes up for a fault of discriminant analysis, Mahalanobis' generalized distance which was used in recent studies. As the result of that, the rate of accuracy for discriminant is individually 96.0% as the first step , and 80.9% as the second step, so we confirm the usefulness of the algorism used in this study.
AB - In this study, we make a system which automatically discriminates the severity of prostate tumor. This system includes the automatic gland extraction and the support vector machine as discriminant analysis. The automatic gland extraction is difficult in recent studies because of ill-defined of glands. Also, support vector machine makes up for a fault of discriminant analysis, Mahalanobis' generalized distance which was used in recent studies. As the result of that, the rate of accuracy for discriminant is individually 96.0% as the first step , and 80.9% as the second step, so we confirm the usefulness of the algorism used in this study.
KW - Automatic extraction
KW - Prostate tumor
KW - Support vector machine
KW - Texture analysis
UR - http://www.scopus.com/inward/record.url?scp=77950153574&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950153574&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03882-2-192
DO - 10.1007/978-3-642-03882-2-192
M3 - Conference contribution
AN - SCOPUS:77950153574
SN - 9783642038815
VL - 25
SP - 720
EP - 723
BT - IFMBE Proceedings
ER -