Abstract
The detection of pulmonary nodules in radiological or CT images has been widely investigated in the field of medical image analysis due to the high degree of difficulty it presents. The traditional approach is to develop a multistage CAD system that will reveal the presence or absence of nodules to the radiologist. One of the stages within this system is the detection of ROIs (regions of interest) that may possibly be nodules, in order to reduce the scope of the problem. In this article we evaluate clustering algorithms that use different classification strategies for this purpose. In order to evaluate these algorithms we used high resolution CT images from the LIDC (Lung Internet Database Consortium) database.
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Castro, A., Bóveda, C., Rey, A., Arcay, B. (2010). An Analysis of Different Clustering Algorithms for ROI Detection in High Resolutions CT Lung Images. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_27
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DOI: https://doi.org/10.1007/978-3-642-15910-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15909-1
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