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
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.
תפריט נגישות
ניגודיות עדינה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם
