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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >EEMD Domain AR Spectral Method for Mean Scatterer Spacing Estimation of Breast Tumors From Ultrasound Backscattered RF Data
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EEMD Domain AR Spectral Method for Mean Scatterer Spacing Estimation of Breast Tumors From Ultrasound Backscattered RF Data

机译：EEMD域AR频谱方法从超声反向散射RF数据中估计乳腺肿瘤的平均散射距离

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摘要

We present a novel method for estimating the mean scatterer spacing (MSS) of breast tumors using ensemble empirical mode decomposition (EEMD) domain analysis of deconvolved backscattered radio frequency (RF) data. The autoregressive (AR) spectrum from which the MSS is estimated is obtained from the intrinsic mode functions (IMFs) due to regular scatterers embedded in RF data corrupted by the diffuse scatterers. The IMFs are chosen by giving priority to the presence of an enhanced fundamental harmonic and the presence of a greater number of higher harmonics in the AR spectrum estimated from the IMFs. The AR model order is chosen by minimizing the mean absolute percentage error (MAPE) criterion. In order to ensure that the backscattered data is indeed from a source of coherent scattering, we begin by performing a non-parametric Kolmogorov-Smirnov (K–S) classification test on the backscattered RF data. Deconvolution of the backscattered RF data, which have been classified by the K–S test as sources of significant coherent scattering, is done to reduce the system effect. EEMD domain analysis is then performed on the deconvolved data. The proposed method is able to recover the harmonics associated with the regular scatterers and overcomes many problems encountered while estimating the MSS from the AR spectrum of raw RF data. Using our technique, a mean absolute percentage error (MAPE) of 5.78% is obtained while estimating the MSS from simulated data, which is lower than that of the existing techniques. Our proposed method is shown to outperform the state of the art techniques, namely, singular spectrum analysis, generalized spectrum (GS), spectral autocorrelation (SAC), and modified SAC for different simulation conditions. The MSS for in vivo normal breast tissue is found to be 0.69 ± 0.04 mm; for benign and malignant tumors it is found to be 0.73 ± 0.03 and 0.79 ± 0.04 mm, respectively. The separation between the MSS values of normal and benign tissues for our proposed method is similar to the separations obtained for the conventional methods, but the separation between the MSS values for benign and malignant tissues for our proposed method is slightly higher than that for the conventional methods. When the MSS is used to classify breast tumors into benign and malignant, for a threshold-based classifier, the increase in specificity, accuracy, and area under curve are 18%, 19%, and 22%, respectively, and that for statistical classifiers are 9%, 13%, and 19%, respectively, from that of the next best existing technique.
机译：我们提出了一种新的方法，通过对反卷积后向散射射频（RF）数据进行整体经验模态分解（EEMD）域分析来估计乳腺肿瘤的平均散射体间距（MSS）。从固有模式函数（IMF）获得估计MSS的自回归（AR）频谱，这是由于嵌入在RF数据中的规则散射体被散射体破坏了，这是由固有模式函数（IMF）获得的。通过优先考虑从IMF估计的AR频谱中存在增强的基本谐波和存在更多数量的高次谐波来选择IMF。通过最小化平均绝对百分比误差（MAPE）准则来选择AR模型的顺序。为了确保反向散射数据确实来自相干散射源，我们首先对反向散射RF数据执行非参数Kolmogorov-Smirnov（KS）分类测试。进行反向散射RF数据的反卷积（已被K–S测试归类为显着的相干散射源），以减少系统影响。然后，对反卷积数据执行EEMD域分析。所提出的方法能够恢复与常规散射体相关的谐波，并克服了从原始RF数据的AR频谱估计MSS时遇到的许多问题。使用我们的技术，从模拟数据估计MSS时，获得的平均绝对百分比误差（MAPE）为5.78％，低于现有技术。我们提出的方法表现出优于最新技术的水平，即奇异频谱分析，广义频谱（GS），频谱自相关（SAC）和针对不同模拟条件的改进SAC。发现体内正常乳腺组织的MSS为0.69±0.04 mm。对于良性和恶性肿瘤，发现分别为0.73±0.03和0.79±0.04mm。我们提出的方法的正常和良性组织的MSS值之间的间隔与常规方法获得的间隔相似，但是我们提议的方法的良性和恶性组织的MSS值之间的间隔略高于常规方法方法。当使用MSS将乳腺肿瘤分为良性和恶性时，对于基于阈值的分类器，特异性，准确性和曲线下面积的增加分别为18％，19％和22％，而对于统计分类器则分别为18％，19％和22％。分别是次优技术的9％，13％和19％。

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