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Characterizing Colonic Detections in CT Colonography Using Curvature-Based Feature Descriptor and Bag-of-Words Model

机译:使用基于曲率的特征描述符和词袋模型表征CT结肠造影中的结肠检测

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We present a method based on the content-based image retrieval (CBIR) paradigm to enhance the performance of computer aided detection (CAD) in computed tomographic colonography (CTC). The method explores curvature-based feature descriptors in conjunction with bag-of-words (BoW) models to characterize colonic detections. The diffusion distance is adopted to improve feature matching and clustering. Word selection is also applied to remove non-informative words. A representative database is constructed to categorize different types of detections. Query detections are compared with the database for classification. We evaluated the performance of the system by using digital phantoms of common structures in the colon as well as real CAD detections. The results demonstrated the potential of our technique for distinguishing common structures within the colon as well as for classifying true and false-positive CAD detections.
机译:我们提出了一种基于内容的图像检索(CBIR)范例的方法,以增强计算机断层扫描(CTC)中计算机辅助检测(CAD)的性能。该方法结合词袋(BoW)模型探索基于曲率的特征描述符,以表征结肠检测。采用扩散距离以改善特征匹配和聚类。单词选择也适用于删除非信息性单词。构建代表性数据库以对不同类型的检测进行分类。将查询检测结果与数据库进行比较以进行分类。我们通过使用结肠中常见结构的数字体模以及实际的CAD检测来评估系统的性能。结果证明了我们的技术在区分结肠内常见结构以及对正确和错误阳性CAD检测进行分类方面的潜力。

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