Detecting tuberculosis in radiographs using combined lung masks.
Tuberculosis (TB) is a major health threat in many regions of the world, while diagnosing tuberculosis still remains a challenge. Mortality rates of patients with undiagnosed TB are high. Modern diagnostic techniques are often too slow or too expensive for highly-populated developing countries that bear the brunt of the disease. In an effort to reduce the burden of the disease, this paper presents an automated approach for detecting TB on conventional posteroanterior chest radiographs. The idea is to provide developing countries, which have limited access to radiological services and radiological expertise, with an inexpensive detection system that allows screening of large parts of the population in rural areas. In this paper, we present results produced by our TB screening system. We combine a lung shape model, a segmentation mask, and a simple intensity model to achieve a better segmentation mask for the lung. With the improved masks, we achieve an area under the ROC curve of more than 83%, measured on data compiled within a tuberculosis control program.
National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. stefan.jaeger@nih.gov
Stefan
S
Jaeger
Alexandros
A
Karargyris
Sameer
S
Antani
George
G
Thoma
D000465:Algorithms; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008403:Mass Screening / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014397:Tuberculosis, Pulmonary / Q000000981:diagnostic imaging*