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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Analysis and classification of tissue with scatterer structure templates
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Analysis and classification of tissue with scatterer structure templates

机译：具有散射结构模板的组织分析和分类

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摘要

Back-scattered ultrasonic signals provide scatterer structure information. Large-scale structures, such as tissue and tumor boundaries, typically create significant amplitude differences that reveal boundaries in conventional intensity images. Small-scale structures typically result in textures observed over regions of the intensity image. This paper describes the generalized spectrum (GS) for characterizing small-scale scatterer structures and applies it to analyze scatterer structures in a class of malignant and benign breast masses. Methods are presented for scaling and normalizing the GS to reduce effects from system response, overlaying tissue, and variability from noncritical structures. Results from a limited clinical study demonstrate an application of using the GS to discriminate between benign and malignant breast masses that contain internal echoes. Sections of rf A-scans in 41 breast mass regions were taken from 26 patients. A GS analysis was applied to determine critical structural properties between a class of fibroadenoma and carcinoma masses. Classifiers designed using significant structure differences identified by the GS analysis achieved approximately 82% true-positive and 10% false-positive rates.
机译：反向散射超声信号提供散射体结构信息。大型结构（例如组织和肿瘤边界）通常会产生明显的幅度差异，从而揭示常规强度图像中的边界。小规模的结构通常会导致在强度图像区域上观察到纹理。本文描述了用于表征小型散射体结构的广义光谱（GS），并将其应用于分析一类恶性和良性乳腺肿块中的散射体结构。提出了缩放和标准化GS的方法，以减少来自系统响应，组织覆盖和非关键结构变异的影响。有限的临床研究结果表明，使用GS区分包含内部回声的良性和恶性乳腺肿块的应用。取自26名患者的41个乳腺肿块区域的rf A扫描切片。应用GS分析来确定一类纤维腺瘤和癌肿之间的关键结构特性。使用通过GS分析确定的重大结构差异设计的分类器，实现了大约82％的真阳性率和10％的假阳性率。

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