חיפוש מתקדם
Avni, U., BioMedical Engineering, Tel-Aviv University, Israel
Greenspan, H., BioMedical Engineering, Tel-Aviv University, Israel
Sharon, M., Diagnostic Imaging Department, Sheba Medical Center, Israel
Konen, E., Diagnostic Imaging Department, Sheba Medical Center, Israel
Goldberger, J., School of Engineering, Bar-Ilan University, Israel
We present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology presented is based on local patch representation of the image content and a bag-offeatures approach for defining image categories, with a kernel based SVM classifier. In a recent international competition the system was ranked as one of the top schemes in discriminating orientation and body regions in x-ray images, and in medical visual retrieval. A detailed description of the method (not previously published) is presented, along with its most recent results. In addition to organ-level discrimination, we show initial results of pathology-level categorization of chest x-ray data. On a set of 102 chest radiographs taken from routine hospital examination, the system detects pathology with sensitivity of 94% and specificity of 91%. We view this as a first step towards similarity-based categorization with clinical importance in computer-assisted diagnostics. © 2009 IEEE.
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תנאי שימוש
X-Ray image categorization and retrieval using patch-based visualwords representation
Avni, U., BioMedical Engineering, Tel-Aviv University, Israel
Greenspan, H., BioMedical Engineering, Tel-Aviv University, Israel
Sharon, M., Diagnostic Imaging Department, Sheba Medical Center, Israel
Konen, E., Diagnostic Imaging Department, Sheba Medical Center, Israel
Goldberger, J., School of Engineering, Bar-Ilan University, Israel
X-Ray image categorization and retrieval using patch-based visualwords representation
We present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology presented is based on local patch representation of the image content and a bag-offeatures approach for defining image categories, with a kernel based SVM classifier. In a recent international competition the system was ranked as one of the top schemes in discriminating orientation and body regions in x-ray images, and in medical visual retrieval. A detailed description of the method (not previously published) is presented, along with its most recent results. In addition to organ-level discrimination, we show initial results of pathology-level categorization of chest x-ray data. On a set of 102 chest radiographs taken from routine hospital examination, the system detects pathology with sensitivity of 94% and specificity of 91%. We view this as a first step towards similarity-based categorization with clinical importance in computer-assisted diagnostics. © 2009 IEEE.
Scientific Publication
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