Chest radiography is one of the key techniques for investigating suspected tuberculosis (TB). Computerized reading of chest radiographs (CXRs) is an appealing concept because there is a severe shortage of human experts trained to interpret CXRs in countries with a high prevalence of TB. This paper presents a comprehensive computerized system for the detection of abnormalities in CXRs and evaluates the system on digital data from a TB prevalence survey in The Gambia. The system contains algorithms to normalize the images, segment the lung fields, analyze the shape of the segmented lungs, detect textural abnormalities, measure bluntness of the costophrenic angles and quantify the asymmetry in the lung fields. These subsystems are combined with a Random Forest classifier into an overall score indicating the abnormality of the radiograph. The results approach the performance of an independent human reader.
Automated Scoring of Chest Radiographs for Tuberculosis Prevalence Surveys: A Combined Approach
B. van Ginneken, R. Philipsen, L. Hogeweg, P. Maduskar, J. Melendez, C. Sánchez, R. Maane, B. dei Alorse, U. d'Alessandro and I. Adetifa
The Fifth International Workshop on Pulmonary Image Analysis 2013:9-19.