Abstract
The success of lung nodule detection depends on the quality of nodule models. This paper presents a novel approach for nodule modeling which is data-driven. Low dose CT (LDCT) scans of clinical chest data are used to create the required statistics for the models based on modern computer vision techniques. The new models characterize the tissue characteristics of typical lung nodules as specified by human experts. These models suit various machine learning approaches for nodule detection including simulated annealing, genetic algorithms, SVM, AdaBoost, and Bayesian methods. The quality of the new nodule models are studied with respect to parametric models, and are tested on clinical data showing significant improvements in sensitivity and specificity.
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Farag, A., Graham, J., Farag, A., Falk, R. (2009). Lung Nodule Modeling – A Data-Driven Approach. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_33
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DOI: https://doi.org/10.1007/978-3-642-10331-5_33
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