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.
Department of Biomedical Engineering, Tel-Aviv University, 69978 Tel Aviv, Israel. uriavni@gmail.com
Uri
U
Avni
Hayit
H
Greenspan
Eli
E
Konen
Michal
M
Sharon
Jacob
J
Goldberger
D000465:Algorithms; D004282:Documentation / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011873:Radiology Information Systems; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D009626:Terminology as Topic