Abstract
We propose a new technique to extract a pulmonary nodule from helical thoracic CT scans and estimate its diameter. The technique is based on a novel segmentation, or label-assignment, framework called competition-diffusion (CD), combined with robust ellipsoid fitting (EF). The competition force defined by replicator equations draws one dominant label at each voxel, and the diffusion force encourages spatial coherence in the segmentation map. CD is used to reliably extract foreground structures, and nodule like objects are further separated from attached structures using EF. Using ground-truth measured manually over 1300 nodules taken from more than 240 CT volumes, the performance of the proposed approach is evaluated in comparison with two other techniques: Local Density Maximum algorithm and the original EF. The results show that our approach provides the most accurate size estimates.
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Kubota, T., Okada, K. (2005). Estimating Diameters of Pulmonary Nodules with Competition-Diffusion and Robust Ellipsoid Fit. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_33
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DOI: https://doi.org/10.1007/11569541_33
Publisher Name: Springer, Berlin, Heidelberg
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