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Chapter 60 Discrimination of Solitary Pulmonary Nodules on CT Images Based on a Novel Automatic Weighted FCM

机译:基于新型自动加权FCM,第60章孤独肺结核对CT图像的判断

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A novel automatic feature assessment and weighting Fuzzy C-Means (FCM) algorithm was proposed for the classification of solitary pulmonary nodules (SPN). Six pulmonary nodule features were extracted from computed tomography (CT) images, which were normalized and combined into feature sequence. The feature assessment method was used to calculate discriminative criterion of categories, where the sensitive features were selected and weighted to discriminate between benign and suspicious malignant pulmonary nodules. Forty CT slices of twenty three patients are selected to evaluate the proposed method. The experimental results show that the accuracy of discrimination is 86.3 %, the sensitiveness is 87.5 %, and the specificity is 80 %, which illustrate that the method is feasible, and have good accuracy and sensitivity.
机译:提出了一种新的自动特征评估和加权模糊C-MATIOM(FCM)算法,用于孤立肺结节(SPN)的分类。从计算机断层扫描(CT)图像中提取六种肺结节特征,其被归一化并组合成特征序列。该特征评估方法用于计算类别的判别标准,其中选择敏感特征和加权以区分良性和可疑恶性肺结核。选择四十只CT切片23名患者以评估所提出的方法。实验结果表明,歧视的准确性为86.3%,敏感性为87.5%,特异性为80%,说明该方法是可行的,并且具有良好的准确性和敏感性。

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