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Computerized Detection of Pulmonary Nodules in Chest Radiographs Based on Morphological Features and Wavelet Snake Model by Bilgin Keserci; Hiroyuki Yoshida is a Medicine article available to read on EtoBox.

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We have developed a new computer-aided diagnosis scheme for automated detection of lung nodules in digital chest radiographs based on a combination of morphological features and the wavelet snake. In our scheme, two processes were applied in parallel to reduce the false-positive detections after initial nodule candidates were selected. One process consisted of adaptive filtering for enhancement of nodules and suppression of normal lung structures, followed by extraction of conventional morphological features. The other process consisted of a novel approach for elimination of false positives called the edge-guided wavelet snake model. In the latter process, multiscale edges of the candidate nodules were extracted to yield parts of the nodule boundaries. A wavelet snake was then used for fitting of these multiscale edges for approximation of the true boundaries of nodules. A boundary feature called the weighted overlap between the snake and the multiscale edges was calculated and used for elimination of false positives. Finally, the weighted overlap and the morphological features were combined by use of an artificial neural network for efficient reduction of false positives. Our sche

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Author

Bilgin Keserci; Hiroyuki Yoshida

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 1361-8415)

Published

2002

Language

EN

Field

Medicine (Physical Sciences)