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Is early detection of liver and breast cancers from ultrasound scans possible?

机译:可以通过超声波扫描早期发现肝癌和乳腺癌吗?

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This paper presents an integral approach for the tissue characterization problem. Such an approach includes a model, estimation algorithms and an evaluation method. This work focuses on liver and breast tissue characterization but it may be applicable to other tissue types after proper modifications. Liver and breast tissue is composed of two major kinds of scattering structure, i.e., the liver and breast parenchyma, which is relatively large and thus resolvable using the current ultrasonic transducers, and liver and breast cells which are not resolvable. In this work, we propose a decomposition approach for the RF echo into two components, namely the coherent and diffuse component, which are related to the resolvable and unresolvable scatterers in the liver and breast structure, respectively. Structural differences between the liver and breast, related to the resolvable scatterers properties, led us to develop two different decomposition algorithms. The first algorithm was developed for the liver RF echo and was based on the quasi-periodic structure of the liver lobules. Breast tissue decomposition was based on a more general model for the resolvable scatterers echo, because the breast tissue parenchyma is far from regular. By using the proposed decomposition we were able to estimate structural parameters of the liver and breast such as the average spacing of the liver lobules, the energy of the resolvable and unresolvable scatterers, and the correlation between neighboring unresolvable scatterers in the tissue. Empirical receiver operating characteristics analysis was applied to the parameters estimated from a large database of liver and breast B-scan images, to evaluate their diagnostic power. Single parameters of the liver and breast tissue showed good discriminating power between cancerous and normal liver and breast tissue, and also between malignant and benign breast tissue. The ability to identify small breast lesions (4 mm) is also demonstrated.
机译:本文提出了一种解决组织表征问题的综合方法。这种方法包括模型,估计算法和评估方法。这项工作侧重于肝和乳腺组织的表征,但经过适当修改后,它可能适用于其他组织类型。肝和乳腺组织由两种主要的散射结构组成,即肝脏和乳腺实质,相对较大,因此使用当前的超声换能器可分辨,而肝和乳腺细胞则无法分辨。在这项工作中,我们提出了一种将射频回波分解为两个分量的分解方法,即相干分量和扩散分量,这两个分量分别与肝脏和乳房结构中的可分辨和不可分辨散射体有关。肝脏和乳房之间的结构差异与可解决的散射体属性有关,导致我们开发了两种不同的分解算法。针对肝射频回波开发了第一种算法,该算法基于肝小叶的准周期结构。乳房组织的分解基​​于可分辨散射回波的更通用模型,因为乳房组织实质远非常规。通过使用提议的分解,我们能够估计肝脏和乳房的结构参数,例如肝小叶的平均间距,可分辨和不可分辨散射体的能量,以及组织中相邻不可分辨散射体之间的相关性。对从肝脏和乳房B扫描图像的大型数据库估计的参数进行经验接收器工作特性分析,以评估其诊断能力。肝和乳腺组织的单个参数显示出在癌性和正常肝与乳腺组织之间以及恶性和良性乳腺组织之间的良好区分能力。还证明了识别微小乳腺病变(4毫米)的能力。

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