文章基本信息

标题：Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis
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作者：Kohei Arai ; Yeni Herdiyeni ; Hiroshi Okumura 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2012
卷号：3
期号：4
DOI：10.14569/IJACSA.2012.030416
出版社：Science and Information Society (SAI)
摘要：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.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; lung cancer detection; local binary pattern; probabilistic neural network.