Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lung nodule diagnosis using 3D template matching
Computers in Biology and Medicine
False Positive Reduction in Breast Mass Detection Using Two-Dimensional PCA
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Palmprint recognition based on improved 2DPCA
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Automatic lung nodule detection using template matching
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
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The main purpose of pulmonary nodule detection is to classify nodule from the lung computed tomography (CT) images. The variability of class is mainly expected to the grey-level variance, texture differences and shape. The purpose of this study is to develop a nodule detector based on Two-dimensional Principal Component Analysis (2DPCA). We extract the features using 2DPCA from nodule candidate images. Nodule candidates are then classified using threshold. The proposed method significantly reduces false positive (FP) rate. We applied it to Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The experimental results show the effectiveness and efficiency of the proposed method. The proposed method achieved 80.60% detection rate with 0.0391 FPs per slice.