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X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words
Year:
2011
Authors :
Sharon, Michal
;
.
Volume :
30
Co-Authors:
Avni, U., Department of Biomedical Engineering, Tel-Aviv University, 69978 Tel Aviv, Israel
Greenspan, H., Department of Biomedical Engineering, Tel-Aviv University, 69978 Tel Aviv, Israel, Multimedia for Healthcare Group, IBM Almaden Research Center, San Jose, CA 95120, United States
Konen, E., Diagnostic Imaging Department, Sheba Medical Center, 52621 Tel Hashomer, Israel
Sharon, M., Diagnostic Imaging Department, Sheba Medical Center, 52621 Tel Hashomer, Israel
Goldberger, J., School of Engineering, Bar-Ilan University, 52900 Ramat-Gan, Israel
Facilitators :
From page:
733
To page:
746
(
Total pages:
14
)
Abstract:
In this study we present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a bag of visual words approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X-ray images. In addition to organ-level discrimination, we show an application to pathology-level categorization of chest X-ray data, the most popular examination in radiology. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics. © 2006 IEEE.
Note:
Related Files :
computer assisted diagnosis
Pathology
Pattern Recognition, Automated
Radiology
Show More
Related Content
More details
DOI :
10.1109/TMI.2010.2095026
Article number:
5643927
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
26474
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:23
Scientific Publication
X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words
30
Avni, U., Department of Biomedical Engineering, Tel-Aviv University, 69978 Tel Aviv, Israel
Greenspan, H., Department of Biomedical Engineering, Tel-Aviv University, 69978 Tel Aviv, Israel, Multimedia for Healthcare Group, IBM Almaden Research Center, San Jose, CA 95120, United States
Konen, E., Diagnostic Imaging Department, Sheba Medical Center, 52621 Tel Hashomer, Israel
Sharon, M., Diagnostic Imaging Department, Sheba Medical Center, 52621 Tel Hashomer, Israel
Goldberger, J., School of Engineering, Bar-Ilan University, 52900 Ramat-Gan, Israel
X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words
In this study we present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a bag of visual words approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X-ray images. In addition to organ-level discrimination, we show an application to pathology-level categorization of chest X-ray data, the most popular examination in radiology. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics. © 2006 IEEE.
Scientific Publication
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