Abstract
Automated lung nodule detection through computed tomography (CT) image acquisition is a new and exciting research area of medical image processing. Lung nodules are potentially cancerous growths in the lungs that often appear in CT images as distinct, high intensity spherical objects. We have developed a nodule detection system. The first stage of the nodule detection technique automatically segments the lung regions using a unique 3D region growing approach. The next stage identifies regions of interests (ROIs) by using adaptive multi-level thresholding (MLT) based on the cumulative density function (CDF) of the lung volume. The last stage reduces false positives (FPs) by using unique features such as vessel and lung wall connectivity, a modified bounding box and 3D compaction to compensate for partial volume artifacts due to thick CT slices. We obtain a sensitivity of 80% with approximately 3.05 FPs per slice.
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© 2005 Springer-Verlag Berlin Heidelberg
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Dajnowiec, M., Alirezaie, J., Babyn, P. (2005). An Adaptive Rule Based Automatic Lung Nodule Detection System. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_85
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DOI: https://doi.org/10.1007/11552499_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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