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首页> 外文期刊>Journal of Digital Imaging >Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography
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Feature Selection and Performance Evaluation of Support Vector Machine (SVM)-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography

机译：基于支持向量机的良性和恶性肺结节分类器的特征选择和性能评价

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There are lots of work being done to develop computer-assisted diagnosis and detection (CAD) technologies and systems to improve the diagnostic quality for pulmonary nodules. Another way to improve accuracy of diagnosis on new images is to recall or find images with similar features from archived historical images which already have confirmed diagnostic results, and the content-based image retrieval (CBIR) technology has been proposed for this purpose. In this paper, we present a method to find and select texture features of solitary pulmonary nodules (SPNs) detected by computed tomography (CT) and evaluate the performance of support vector machine (SVM)-based classifiers in differentiating benign from malignant SPNs. Seventy-seven biopsy-confirmed CT cases of SPNs were included in this study. A total of 67 features were extracted by a feature extraction procedure, and around 25 features were finally selected after 300 genetic generations. We constructed the SVM-based classifier with the selected features and evaluated the performance of the classifier by comparing the classification results of the SVM-based classifier with six senior radiologists′ observations. The evaluation results not only showed that most of the selected features are characteristics frequently considered by radiologists and used in CAD analyses previously reported in classifying SPNs, but also indicated that some newly found features have important contribution in differentiating benign from malignant SPNs in SVM-based feature space. The results of this research can be used to build the highly efficient feature index of a CBIR system for CT images with pulmonary nodules. Key words Feature selection - content-based image retrieval - classification - CT images - lung diseases
机译：开发计算机辅助诊断和检测（CAD）技术和系统以提高肺结节诊断质量的工作很多。提高新图像诊断准确性的另一种方法是从已确认诊断结果的存档历史图像中调用或查找具有相似特征的图像，为此已提出了基于内容的图像检索（CBIR）技术。在本文中，我们提出了一种方法，用于查找和选择通过计算机断层扫描（CT）检测到的孤立肺结节（SPN）的纹理特征，并评估基于支持向量机（SVM）的分类器在区分良性和恶性SPN方面的性能。这项研究包括了77例活检证实的SPN CT病例。通过特征提取程序共提取了67个特征，经过300代遗传后最终选择了约25个特征。我们构建了具有选定特征的基于SVM的分类器，并通过将基于SVM的分类器的分类结果与六位高级放射科医生的观察结果进行比较，评估了分类器的性能。评估结果不仅表明大多数选定的特征是放射科医生经常考虑的特征，而且以前用于对SPN进行分类的CAD分析中也使用了这些特征，而且还表明一些新发现的特征在基于SVM的恶性SPN鉴别中具有重要的贡献。功能空间。这项研究的结果可用于为带有肺结节的CT图像建立CBIR系统的高效特征指标。关键词特征选择基于内容的图像检索分类CT图像肺部疾病

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