Algorithms 2010, 3(2), 125-144; doi:10.3390/a3020125
Article

Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes

1,* email, 2email and 3email
Received: 1 February 2010; in revised form: 16 February 2010 / Accepted: 22 March 2010 / Published: 31 March 2010
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: The present paper describes a novel recognition method of pulmonary nodules (i.e., cancer candidates) in thoracic computed tomography scans by use of three-dimensional spherical and cylindrical models that represent nodules and blood vessels, respectively. The anatomical validity of these object models and their fidelity to computed tomography scans are evaluated based on the Bayes theorem. The nodule recognition is employed by the maximum a posteriori estimation. The proposed method is applied to 26 actual computed tomography scans, and experimental results are shown.
Keywords: recognition of pulmonary nodules; thoracic computed tomography scans; three-dimensional deformable object models; Bayes theorem; maximum a posteriori estimation
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MDPI and ACS Style

Takizawa, H.; Yamamoto, S.; Shiina, T. Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes. Algorithms 2010, 3, 125-144.

AMA Style

Takizawa H, Yamamoto S, Shiina T. Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes. Algorithms. 2010; 3(2):125-144.

Chicago/Turabian Style

Takizawa, Hotaka; Yamamoto, Shinji; Shiina, Tsuyoshi. 2010. "Recognition of Pulmonary Nodules in Thoracic CT Scans Using 3-D Deformable Object Models of Different Classes." Algorithms 3, no. 2: 125-144.

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