基于抗噪图像特征的甲状腺纹理表示

Eystratios G. Keramidas, Dimitrios K. Iakovidis, D. Maroulis, N. Dimitropoulos
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引用次数: 51

摘要

纹理特征对散斑噪声的鲁棒性对超声成像至关重要。本文提出了一套新的甲状腺超声纹理模糊特征,并对其进行了分析和评价。纹理特征提取方案是基于局部二值模式的模糊化方法。所提出的特征在75名患者获得的b型甲状腺超声图像的注释数据集上进行评估。实验结果表明,这些特征可以准确地表示甲状腺纹理。它们可以有效地用于甲状腺结节检测,优于最近在文献中提出的其他甲状腺纹理表示方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thyroid Texture Representation via Noise Resistant Image Features
The robustness of textural features on speckle noise is of vital importance for ultrasound imaging. A set of novel fuzzy features for thyroid ultrasound texture representation, demonstrating noise-resistant properties, is presented, analyzed and evaluated in this study. The textural feature extraction scheme is based on the fuzzyfication of the local binary pattern approach. The proposed features are evaluated on an annotated dataset of B-mode thyroid ultrasound images acquired from 75 patients. The experimental results illustrate that these features provide accurate representation of the thyroid texture. They can be effectively utilized for thyroid nodule detection outperforming other thyroid texture representation approaches that have been recently proposed in the literature.
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