• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical image analysis >Computer-aided colorectal tumor classification in NBI endoscopy: Using local features
【24h】

Computer-aided colorectal tumor classification in NBI endoscopy: Using local features

机译：NBI内窥镜检查中计算机辅助大肠肿瘤分类：使用局部特征

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

An early detection of colorectal cancer through colorectal endoscopy is important and widely used in hospitals as a standard medical procedure. During colonoscopy, the lesions of colorectal tumors on the colon surface are visually inspected by a Narrow Band Imaging (NBI) zoom-videoendoscope. By using the visual appearance of colorectal tumors in endoscopic images, histological diagnosis is presumed based on classification schemes for NBI magnification findings. In this paper, we report on the performance of a recognition system for classifying NBI images of colorectal tumors into three types (A, B, and C3) based on the NBI magnification findings. To deal with the problem of computer-aided classification of NBI images, we explore a local feature-based recognition method, bag-of-visual-words (BoW), and provide extensive experiments on a variety of technical aspects. The proposed prototype system, used in the experiments, consists of a bag-of-visual-words representation of local features followed by Support Vector Machine (SVM) classifiers. A number of local features are extracted by using sampling schemes such as Difference-of-Gaussians and grid sampling. In addition, in this paper we propose a new combination of local features and sampling schemes. Extensive experiments with varying the parameters for each component are carried out, for the performance of the system is usually affected by those parameters, e.g. the sampling strategy for the local features, the representation of the local feature histograms, the kernel types of the SVM classifiers, the number of classes to be considered, etc. The recognition results are compared in terms of recognition rates, precision/recall, and F-measure for different numbers of visual words. The proposed system achieves a recognition rate of 96% for 10-fold cross validation on a real dataset of 908 NBI images collected during actual colonoscopy, and 93% for a separate test dataset.
机译：通过大肠内窥镜及早发现大肠癌是重要的，并已在医院广泛用作标准医疗程序。在结肠镜检查期间，通过窄带成像（NBI）变焦视频内窥镜目测检查结肠表面上结肠直肠肿瘤的病变。通过使用内窥镜图像中结直肠肿瘤的视觉外观，可以基于NBI放大结果的分类方案进行组织学诊断。在本文中，我们报告了基于NBI放大结果将大肠肿瘤的NBI图像分为三种类型（A，B和C3）的识别系统的性能。为了解决NBI图像的计算机辅助分类问题，我们探索了一种基于局部特征的识别方法，即视觉词袋（BoW），并在各种技术方面提供了广泛的实验。实验中使用的拟议原型系统由局部特征的视觉效果词袋表示，后跟支持向量机（SVM）分类器。通过使用采样方案（例如高斯差分和网格采样）可以提取许多局部特征。另外，在本文中，我们提出了局部特征和采样方案的新组合。由于系统的性能通常受那些参数的影响，例如，对于每个组件，进行了广泛的实验，其中每个组件的参数都不同。局部特征的采样策略，局部特征直方图的表示形式，SVM分类器的内核类型，要考虑的类数等。根据识别率，精度/召回率和针对不同数量的视觉单词的F量度。对于在实际结肠镜检查期间收集的908张NBI图像的真实数据集，对10倍交叉验证而言，提出的系统可实现96％的识别率，对于单独的测试数据集，可达到93％的识别率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号