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Classification of Pulmonary Nodules in Lung CT Images using Shape and Texture Features

机译:利用形状和纹理特征对肺部CT图像中的肺结节进行分类

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Differentiation of malignant and benign pulmonary nodules is important for prognosis of lung cancer. In this paper, benign and malignant nodules are classified using support vector machine. Several shape-based and texture-based features are used to represent the pulmonary nodules in the feature space. A semi-automated technique is used for nodule segmentation. Relevant features are selected for efficient representation of nodules in the feature space. The proposed scheme and the competing technique are evaluated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The nodules with composite rank of malignancy "1","2" are considered as benign and "4","5" are considered as malignant. Area under the receiver operating characteristics curve is 0.9465 for the proposed method. The proposed method outperforms the competing technique.
机译:恶性和良性肺结节的分化对于肺癌的预后很重要。在本文中,使用支持向量机对良性和恶性结节进行分类。几个基于形状和基于纹理的特征用于表示特征空间中的肺结节。半自动技术用于结节分割。选择相关特征以有效表示特征空间中的结节。在肺图像数据库联盟和图像数据库资源倡议的542个结节的数据集上评估了提出的方案和竞争技术。具有恶性综合等级“ 1”,“ 2”的结节被认为是良性的,而“ 4”,“ 5”被认为是恶性的。该方法在接收器工作特性曲线下的面积为0.9465。所提出的方法优于竞争技术。

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