Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images
Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images
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DOI：
10.1117/12.535032
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发表时间：
2004-04
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影响因子：
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通讯作者：
Y. Kawata;N. Niki;H. Ohamatsu;M. Kusumoto;R. Kakinuma;K. Mori;Kozo Yamada;H. Nishiyama;K. Eguchi;M. Kaneko;N. Moriyama
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文献类型：
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作者：
Y. Kawata;N. Niki;H. Ohamatsu;M. Kusumoto;R. Kakinuma;K. Mori;Kozo Yamada;H. Nishiyama;K. Eguchi;M. Kaneko;N. Moriyama
计算机辅助诊断（CAD）已被研究，为医生提供定量信息，如恶性可能性的估计，以帮助在肺癌筛查中检测到的异常分类。本研究的目的是开发一种分类结节密度模式的方法，提供有关结节状态（如病变阶段）的信息。该方法包括三个步骤：结节分割、结节内CT密度直方图分析、基于直方图模式将结节分为五种类型。在本文中，我们引入了与结节中心距离和结节内CT密度相关的二维（2-D）关节直方图，并探讨了与关节直方图的形状和位置相关的数值特征。
Computer-aided diagnosis (CAD) has been investigated to provide physicians with quantitative information, such as estimates of the malignant likelihood, to aid in the classification of abnormalities detected at screening of lung cancers. The purpose of this study is to develop a method for classifying nodule density patterns that provides information with respect to nodule statuses such as lesion stage. This method consists of three steps, nodule segmentation, histogram analysis of CT density inside nodule, and classifying nodules into five types based on histogram patterns. In this paper, we introduce a two-dimensional (2-D) joint histogram with respect to distance from nodule center and CT density inside nodule and explore numerical features with respect to shape and position of the joint histogram.