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利用频率分集和 Nakagami 统计对乳腺超声 B 模式图像进行分类

Classification of ultrasonic B mode images of the breast using frequency diversity and Nakagami statistics.

作者信息

Dumane V A, Shankar P M, Piccoli C W, Reid J M, Genis V, Forsberg F, Goldberg B B

出版信息

IEEE Trans Ultrason Ferroelectr Freq Control. 2002 May;49(5):664-8. doi: 10.1109/tuffc.2002.1002466.

Abstract

The parameters of the Nakagami distribution have been utilized in the past to classify lesions in breast tissue as benign or malignant. To avoid the effect of operatorgain settings on the parameters of the Nakagami distribution, normalized parameters were utilized for the classification. The normalized parameter was defined as the ratio of the parameter at the site of the lesion to its average value over several regions away from the site. This technique, however, was very time consuming. In this paper, the application of frequency diversity and compounding is explored to achieve this normalization. Lesions are classified using these normalized parameters at the site. A receiver operating characteristic (ROC) analysis of the parameters of the Nakagami distribution has been conducted before and after compounding on a data set of 60 benign and 65 malignant lesions. The ROC results indicate that this technique can reasonably classify lesions in breast tissue as benign or malignant.

摘要

过去曾利用 Nakagami 分布的参数将乳腺组织中的病变分类为良性或恶性。为避免操作员增益设置对 Nakagami 分布参数的影响,分类时使用了归一化参数。归一化参数定义为病变部位的参数与其在远离该部位的几个区域的平均值之比。然而,该技术非常耗时。本文探讨了利用频率分集和复合来实现这种归一化。利用这些归一化参数在病变部位进行分类。在一个包含 60 个良性病变和 65 个恶性病变的数据集上,对复合前后的 Nakagami 分布参数进行了受试者操作特征(ROC)分析。ROC 结果表明,该技术能够合理地将乳腺组织中的病变分类为良性或恶性。

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