PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words
Uri Avni, Hayit Greenspan, Eli Konen, Michal Sharoon and Jacob Goldberger
IEEE Trans. medical Imaging Volume 30, Number 3, pp. 733-746, 2011.

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 non-linear 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:7744
Deposited By:Jacob Goldberger
Deposited On:17 March 2011