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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Tissue characterization using the continuous wavelet transform. II. Application on breast RF data
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Tissue characterization using the continuous wavelet transform. II. Application on breast RF data

机译:使用连续小波变换进行组织表征。二。在乳房射频数据上的应用

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For pt.I see ibid., vol.48, no.2, p.356-63 (2001). In the first part of this work (Georgiou and Cohen 2000), a wavelet-based decomposition algorithm of the RF echo into its coherent and diffuse components was introduced. In this paper, the proposed algorithm is used to estimate structural parameters of the breast tissue such as the number and energy of coherent scatterers, the energy of the diffuse scatterers, and the correlation between them. Based on these individual parameters, breast tissue characterization is performed. The database used consists of 155 breast scans from 42 patients. The results are presented in terms of empirical receiver operating characteristics (ROC) curves. The results of this study are discussed in relation to the tissue microstructure. Individual estimated parameters are able to differentiate reliably between normal and fibroadenoma or fibrocystic or cancerous tissue (area under the ROC A/sub z/>0.93). Also, the differentiation between malignant and benign (normal, fibrocystic, and fibroadenoma) tissue was possible (A/sub z/>0.89).
机译:关于第一部分,见同上,第48卷,第2期,第356-63页(2001年)。在这项工作的第一部分(Georgiou和Cohen 2000),介绍了一种基于小波的RF回波分解为相干分量和扩散分量的分解算法。在本文中,所提出的算法用于估计乳腺组织的结构参数,例如相干散射体的数量和能量,弥散散射体的能量以及它们之间的相关性。基于这些单独的参数,进行乳房组织表征。使用的数据库包括来自42位患者的155次乳房扫描。结果以经验接收器工作特性(ROC)曲线表示。讨论了这项研究的结果与组织的微观结构。各个估计参数能够可靠地区分正常组织和纤维腺瘤或纤维囊性或癌性组织(ROC A / sub z /> 0.93以下的区域)。同样,在恶性和良性(正常,纤维囊性和纤维腺瘤)组织之间的分化是可能的(A / sub z /> 0.89)。

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