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Automatic heart localization and radiographic index computation in chest x-rays.
This study proposes a novel automated method for cardiomegaly detection in chest X-rays (CXRs). The algorithm has two main stages: i) heart and lung region localization on CXRs, and ii) radiographic index extraction from the heart and lung boundaries. We employed a lung detection algorithm and extended it to automatically compute the heart boundaries. The typical models of heart and lung regions are learned using a public CXR dataset with boundary markings. The method estimates the location of these regions in candidate ('patient') CXR images by registering models to the patient CXR. For the radiographic index computation, we implemented the traditional and recently published indexes in the literature. The method is tested on a database with 250 abnormal, and 250 normal CXRs. The radiographic indexes are combined through a classifier, and the method successfully classifies the patients with cardiomegaly with a 0:77 accuracy, 0:77 sensitivity and 0:76 specificity.