一种无监督的自动纹理分类方法,用于甲状腺结节的超声图像
Chenzhuo Lu1,2, Zhuang Fu1,2, Jian Fei3,4,5,6
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
Physics in medicine and biology
|January 3, 2025
概括
这项研究引入了一种无监督的方法,用于从超声波图像中分类甲状腺结节纹理,克服了手动标签的需要. 该方法准确地识别了不同的结节纹理,有助于创建数据集和医疗图像分析.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 超声波技术 超声波技术 超声波技术
背景情况:
- 超声波是甲状腺结节评估的主要成像方法,依靠纹理分析进行诊断.
- 在为甲状腺结节纹理创建像素级标记数据集存在重大挑战,这是由于经验丰富的医生需要大量的手工努力.
- 这种稀缺性阻碍了对甲状腺结节的自动诊断工具的开发.
研究的目的:
- 从超声波图像中开发一种无监督的方法,用于从超声波图像中对甲状腺结节的自动纹理分类.
- 减少对手工标记数据集的依赖,从而节省大量的人力.
- 提供一种工具,帮助医生解释甲状腺结节的超声波图像.
主要方法:
- 开发一个空间映射网络,将1D像素值空间转换为一个高维特征空间.
- 适用于集群应用程序的特征选择原则的实施.
- 关于像素级聚类算法的建议,其中包括区域增长模式和纹理集的新型距离评估方法.
主要成果:
- 实现了高像素级分类准确度:0.931 (囊性/固体),0.870 (低声),0.959 (异声) 和0.961 (高声).
- 证明了算法的有效性和与人类专家观察的一致性.
- 视觉化了良性和恶性甲状腺结节内的不同纹理的分布.
结论:
- 开发的无监督方法有效地在像素级别上对甲状腺结节纹理进行分类.
- 这种方法有助于自动生成像素级标签,有助于创建全面的纹理数据集.
- 该方法为医疗专业人员提供了宝贵的图像分析见解,支持甲状腺结节诊断.
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