• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >A comparison of autoregressive spectral estimation algorithms andorder determination methods in ultrasonic tissue characterization
【24h】

A comparison of autoregressive spectral estimation algorithms andorder determination methods in ultrasonic tissue characterization

机译：超声组织表征中自回归谱估计算法和阶次确定方法的比较

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

Several autoregressive (AR) methods for spectral estimation werenapplied toward the task of estimating ultrasonic backscatterncoefficients from small volumes of tissue. Data were acquired from anhomogeneous tissue-mimicking phantom and from a normal human liver innvivo. AR methods performed better at short record lengths than thentraditional DFT (discrete Fourier Transform) approach. The DFT methodnconsistently underestimated backscatter coefficients at small gatenlengths. Burg's algorithm, the Modified Covariance algorithm, and thenRecursive Maximum Likelihood Estimation algorithm performed comparably.nThe Yule-Walker algorithm did not perform as well as these but offered anslight improvement over the DFT. Several order determination methodsnwere tested. These included residual variance (RV), final predictionnerror (FPE), Akaike information criterion (AIC), and Minimum DescriptionnLength (MDL). The AIC and MDL produced misleading results at highernorders. The RV and FPE yielded better results. The autoregressive methodnoffers promise for enhanced spatial resolution and accuracy innultrasonic tissue characterization and nondestructive evaluation ofnmaterials
机译：几种用于频谱估计的自回归（AR）方法未应用于从少量组织中估计超声反向散射系数的任务。从非均质组织模拟体模和正常人肝脏体内获得数据。与传统的DFT（离散傅立叶变换）方法相比，AR方法在较短的记录长度上表现更好。 DFT方法在小栅长时始终低估了反向散射系数。 Burg的算法，改进的协方差算法和递归最大似然估计算法的性能相当。nYule-Walker算法的性能不尽如人意，但在DFT上却有一定的改进。测试了几种订单确定方法。这些包括残差方差（RV），最终预测误差（FPE），Akaike信息准则（AIC）和最小描述长度（MDL）。 AIC和MDL在更高级别上产生了误导性结果。 RV和FPE产生更好的结果。自回归方法现在有望提高空间分辨率和精度，对超声组织进行表征并对材料进行无损评估

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