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פותח על ידי קלירמאש פתרונות בע"מ -
Chest x-ray characterization: From organ identification to pathology categorization
Year:
2010
Authors :
שרון, מיכל
;
.
Volume :
Co-Authors:
Avni, U., BioMedical Engineering, Tel-Aviv University, Israel
Goldberger, J., Engineering School, Bar-Ilan University, Israel
Sharon, M., Diagnostic Imaging Dept., Sheba Medical Center, France
Konen, E., Diagnostic Imaging Dept., Sheba Medical Center, France
Greenspan, H., BioMedical Engineering, Tel-Aviv University, Israel, IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95129, United States
Facilitators :
From page:
155
To page:
163
(
Total pages:
9
)
Abstract:
This work presents a novel approach to chest x-ray characterization. It is based on the generation of a visual words dictionary to represent x-ray images, and similarity-based categorization with a kernel based SVM classifier. Two main tasks are addressed: First, the extraction of chest images from a large radiograph archive, i.e. an organ identification task; Second, the detection and identification of chest pathologies, i.e. shifting from the organ level to a pathology level analysis. We used a large generic archive of 12,000 radiographs (IRMA) to tune the system parameters. We demonstrate automated organ detection on the IRMA collection as well as the generalization to a new data collection. The application is shown to discriminate between healthy and pathology cases, as well as identify specific pathologies on a set of 223 chest radiographs taken from a routine hospital examination. Results indicate detection of pathology at a sensitivity of 88.4% and a specificity of 81%. This is a first step towards similarity-based categorization that has a major clinical importance in computer-assisted diagnostics. Copyright 2010 ACM.
Note:
Related Files :
Chest radiographs
Data collection
image analysis
Main tasks
Medical image classification
Pathology
X-ray image
עוד תגיות
תוכן קשור
More details
DOI :
10.1145/1743384.1743414
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר מתוך כינוס
;
.
Language:
אנגלית
Editors' remarks:
ID:
28365
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:38
Scientific Publication
Chest x-ray characterization: From organ identification to pathology categorization
Avni, U., BioMedical Engineering, Tel-Aviv University, Israel
Goldberger, J., Engineering School, Bar-Ilan University, Israel
Sharon, M., Diagnostic Imaging Dept., Sheba Medical Center, France
Konen, E., Diagnostic Imaging Dept., Sheba Medical Center, France
Greenspan, H., BioMedical Engineering, Tel-Aviv University, Israel, IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95129, United States
Chest x-ray characterization: From organ identification to pathology categorization
This work presents a novel approach to chest x-ray characterization. It is based on the generation of a visual words dictionary to represent x-ray images, and similarity-based categorization with a kernel based SVM classifier. Two main tasks are addressed: First, the extraction of chest images from a large radiograph archive, i.e. an organ identification task; Second, the detection and identification of chest pathologies, i.e. shifting from the organ level to a pathology level analysis. We used a large generic archive of 12,000 radiographs (IRMA) to tune the system parameters. We demonstrate automated organ detection on the IRMA collection as well as the generalization to a new data collection. The application is shown to discriminate between healthy and pathology cases, as well as identify specific pathologies on a set of 223 chest radiographs taken from a routine hospital examination. Results indicate detection of pathology at a sensitivity of 88.4% and a specificity of 81%. This is a first step towards similarity-based categorization that has a major clinical importance in computer-assisted diagnostics. Copyright 2010 ACM.
Scientific Publication
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