This paper describes a system for detecting pulmonary nodules in CT images. It aims to label individual image voxels in accordance to one of a number of anatomical (pulmonary vessels or junctions), pathological (nodules), or spurious (noise) events. The approach is orthodoxly Bayesian, with particular care taken in the objective establishment of prior probabilities and the incorporation of relevant medical knowledge. We provide, under explicit modeling assumptions, closed-form expressions for all the probability distributions involved. The technique is applied to real data, and we present a discussion of its performance. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Mendonça, P. R. S., Bhotika, R., Zhao, F., & Miller, J. V. (2007). Lung nodule detection via Bayesian voxel labeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4584 LNCS, pp. 134–146). Springer Verlag. https://doi.org/10.1007/978-3-540-73273-0_12
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