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Proceedings Paper

False positive reduction for pulmonary nodule detection using two-dimensional principal component analysis
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Paper Abstract

Pulmonary nodule detection is a binary classification problem. The main objective is to classify nodule from the lung computed tomography (CT) images. The intra class variability is mainly due to the grey-level variance, texture differences and shape. The purpose of this study is to develop a novel nodule detection method which is based on Two-dimensional Principal Component Analysis (2DPCA). We extract the futures using 2DPCA from nodule candidate images. Nodule candidates are classified using threshold. The proposed method reduces False Positive (FP) rate. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The experimental results demonstrate the effectiveness and efficiency of the proposed method. The proposed method achieved 85.11% detection rate with 1.13 FPs per scan.

Paper Details

Date Published: 2 September 2009
PDF: 8 pages
Proc. SPIE 7443, Applications of Digital Image Processing XXXII, 744322 (2 September 2009); doi: 10.1117/12.827252
Show Author Affiliations
Wook-Jin Choi, Gwangju Institute of Science and Technology (Korea, Republic of)
Tae-Sun Choi, Gwangju Institute of Science and Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 7443:
Applications of Digital Image Processing XXXII
Andrew G. Tescher, Editor(s)

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