...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Digital Imaging >SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors
【24h】

SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors

机译：基于SVM的小波包纹理描述符对肝脏超声图像的表征

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

A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.
机译：本文提出了一种表征正常肝，肝硬化肝和肝硬化肝细胞癌（HCC）的系统。这项研究是使用从56位受试者拍摄的56张真实超声图像（15张正常，16张肝硬化和25张HCC肝脏图像）进行的。由经验丰富的参与放射科医生提取总共180个非重叠的感兴趣区域（ROI），即每个图像类别中的60个。通过使用各种紧凑的支持小波滤波器（包括Haar，Daubechies（db4和db6），双正交（bior3.1，bior3.3和bior4）），从所有180个ROI计算多分辨率小波包纹理描述符，即均值，标准差和能量特征。 .4），symlets（sym3和sym5）和coiflets（coif1和coif2）。可以观察到，组合长度为48的纹理描述符特征向量由16个均值，16个标准偏差和16个能量特征组成，这些特征向量是根据二维小波包变换通过二级分解得到的所有16个子带特征图像（小波包）估计的。使用Haar小波滤波器可提供86.6％的最佳表征性能。通过遗传算法-支持向量机方法进行特征选择，将分类准确率提高到88.8％，检测正常和肝硬化病例的敏感性为90％，对HCC病例的敏感性为86.6％。考虑到B型超声检测肝硬化肝癌的敏感性有限，通过建议的计算机辅助诊断系统获得的HCC病变敏感性为86.6％，这是非常有前途的，并建议该建议的系统可用于临床环境支持放射科医生进行病灶解释。

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