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首页> 外文期刊>Ultrasound in Medicine and Biology >Breast tissue characterization using FARMA modeling of ultrasonic RF echo.
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Breast tissue characterization using FARMA modeling of ultrasonic RF echo.

机译：使用FARMA超声RF回波建模对乳腺组织进行表征。

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A number of empirical and analytical studies demonstrated that the ultrasound RF echo reflected from tissue exhibits 1/f characteristics. In this paper, we propose to model 1/f characteristics of the ultrasonic RF echo by a novel parsimonious model, namely the fractional differencing auto regressive moving average (FARMA) process, and evaluated diagnostic value of model parameters for breast cancer malignancy differentiation. FARMA model captures the fractal and long term correlated nature of the backscattered speckle texture and facilitates robust efficient estimation of fractal parameters. In our study, in addition to the computer generated FARMA model parameters, we included patient age and radiologist's prebiopsy level of suspicion (LOS) as potential indicators of malignant and benign masses. We evaluated the performance of the proposed set of features using various classifiers and training methods using 120 in vivo breast images. Our study shows that the area under the receiver operating characteristics (ROC) curve of FARMA model parameters alone is superior to the area under the ROC curve of the radiologist's prebiopsy LOS. The area under the ROC curve of the three sets of features yields a value of 0.87, with a confidence interval of [0.85, 0.89], at a significance level of 0.05. Our results suggest that the proposed method of ultrasound RF echo model leads to parameters that can differentiate breast tumors with a relatively high precision. This set of RF echo features can be incorporated into a comprehensive computer-aided diagnostic system to aid physicians in breast cancer diagnosis. (E-mail: ).
机译：大量的经验和分析研究表明，从组织反射的超声RF回波具有1 / f特性。在本文中，我们建议通过一种新颖的简约模型，即分数差分自回归移动平均（FARMA）过程，对超声RF回波的1 / f特性进行建模，并评估模型参数对乳腺癌恶性分化的诊断价值。 FARMA模型捕获了反向散射斑点纹理的分形和长期相关性质，并有助于对分形参数进行有效的有效估计。在我们的研究中，除了计算机生成的FARMA模型参数外，我们还将患者年龄和放射科医生的活检前怀疑水平（LOS）包括为恶性和良性肿块的潜在指标。我们使用各种分类器和使用120个体内乳腺图像的训练方法评估了建议功能集的性能。我们的研究表明，仅FARMA模型参数的接收器工作特征（ROC）曲线下方的面积要优于放射科医生的活检前LOS的ROC曲线下方的面积。这三组特征的ROC曲线下面积得出的值为0.87，置信区间为[0.85，0.89]，显着性水平为0.05。我们的结果表明，所提出的超声RF回波模型方法可产生能够以较高的精度区分乳腺肿瘤的参数。可以将这组RF回波功能整合到一个全面的计算机辅助诊断系统中，以帮助医生进行乳腺癌诊断。 （电子邮件：）。

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