Automated lung outline reconstruction in ventilationperfusion scans using principal component analysis techniques
The present work addresses the development of an automated software-based system utilized in order to create an outline reconstruction of lung images from ventilationperfusion scans for the purpose of diagnosing pulmonary embolism. The proposed diagnostic software procedure would require a standard set of digitized ventilationperfusion scans in addition to correlated chest X-rays as key components in the identification of an ideal template match used to approximate and reconstruct the outline of the lungs. These reconstructed lung images would then be used to extract the necessary PIOPED-compliant features which would warrant a pulmonary embolism diagnosis. In order to evaluate this issue, two separate principal component analysis (PCA) algorithms were employed independently, including Eigenlungs, which was adapted from the Eigenfaces method, and an artificial neural network. The results obtained through MATLABTM simulation indicated that lung outline reconstruction through the PCA approach carries significant viability.
ventilationperfusion scans principal component analysis pulmonary embolism template matching artificial neural network pioped criteria