TY - JOUR
T1 - X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words
AU - Avni, Uri
AU - Greenspan, Hayit
AU - Konen, Eli
AU - Sharon, Michal
AU - Goldberger, Jacob
PY - 2011/3
Y1 - 2011/3
N2 - 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.
AB - 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.
KW - Chest radiography
KW - X-ray
KW - computer-aided diagnosis (CAD)
KW - disease labeling
KW - image categorization
KW - image patches
KW - image retrieval
KW - visual words
UR - http://www.scopus.com/inward/record.url?scp=79952162060&partnerID=8YFLogxK
U2 - 10.1109/TMI.2010.2095026
DO - 10.1109/TMI.2010.2095026
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AN - SCOPUS:79952162060
SN - 0278-0062
VL - 30
SP - 733
EP - 746
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 3
M1 - 5643927
ER -