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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis
Digital Object Identifier (DOI) : 10.14569/IJACSA.2012.030416
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 3 Issue 4, 2012.
Abstract: Comparative study between 2D and 3D Local Binary Patter (LBP) methods for extraction from Computed Tomography (CT) imagery data in lung cancer diagnosis is conducted. The lung image classification is performed using probabilistic neural network (PNN) with histogram similarity as distance measure. The technique is evaluated on a set of CT lung images from Japan Society of Computer Aided Diagnosis of Medical Images. Experimental results show that 3D LBP has superior performance in accuracy compare to 2D LBP. The 2D LBP and 3D LBP achieved a classification accuracy of 43% and 78% respectively.
Keywords: lung cancer detection, local binary pattern, probabilistic neural network.
Kohei Arai, Yeni Herdiyeni and Hiroshi Okumura, “Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis” International Journal of Advanced Computer Science and Applications(IJACSA), 3(4), 2012. http://dx.doi.org/10.14569/IJACSA.2012.030416
@article{Arai2012,
title = {Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2012.030416},
url = {http://dx.doi.org/10.14569/IJACSA.2012.030416},
year = {2012},
publisher = {The Science and Information Organization},
volume = {3},
number = {4},
author = {Kohei Arai and Yeni Herdiyeni and Hiroshi Okumura}
}
