System for pathology categorization and retrieval in chest radiographs

Uri Avni; Hayit Greenspan; Eli Konen; Michal Sharon; Jacob Goldberger

doi:10.1117/12.879884

4 March 2011 System for pathology categorization and retrieval in chest radiographs

Uri Avni, Hayit Greenspan, Eli Konen, Michal Sharon, Jacob Goldberger

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79630M (2011) https://doi.org/10.1117/12.879884
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

In this paper we present an overview of a system we have been developing for the past several years for efficient image categorization and retrieval in large radiograph archives. The methodology is based on local patch representation of the image content, using a bag of visual words approach and similarity-based categorization with a kernel based SVM classifier. We show an application to pathology-level categorization of chest x-ray data, the most popular examination in radiology. Our study deals with pathology detection and identification of individual pathologies including right and left pleural effusion, enlarged heart and cases of enlarged mediastinum. The input from a radiologist provided a global label for the entire image (healthy/pathology), and the categorization was conducted on the entire image, with no need for segmentation algorithms or any geometrical rules. An automatic diagnostic-level categorization, even on such an elementary level as healthy vs pathological, provides a useful tool for radiologists on this popular and important examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics.

Citation Download Citation

Uri Avni, Hayit Greenspan, Eli Konen, Michal Sharon, and Jacob Goldberger "System for pathology categorization and retrieval in chest radiographs", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630M (4 March 2011); https://doi.org/10.1117/12.879884

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