它是模糊的还是纹理的? 在医学图像分析中常用的计算模糊和纹理测量的分析
Andrew William Chen1, Murray H Loew1
1The George Washington University, Department of Biomedical Engineering, Washington, District of Columbia, United States.
Journal of medical imaging (Bellingham, Wash.)
|May 26, 2023
概括
纹理测量 (TMs) 可以改善乳房影像中模糊的检测,优于传统的模糊测量 (BMs). 这对于识别影响微化可见性的微妙图像退化至关重要.
科学领域:
- 医疗成像医学成像
- 图像处理 图像处理
- 计算病理学计算病理学
背景情况:
- 乳房摄影的解释依赖于对图像质量的视觉评估,其中微妙的模糊可能会阻碍微化检测.
- 目前的模糊度量 (BMs) 通常不会因其对图像纹理的敏感性而被评估.
- 低水平的模糊是特别有问题的,因为它很难检测,但可以显著影响诊断的准确性.
研究的目的:
- 调查图像纹理对乳房影像模糊测量 (BMs) 的表现的影响.
- 为了确定纹理测量 (TMs) 是否可以增强微妙图像模糊的检测.
- 评估TMs与BMs相比的有效性,用于分类乳房像图像中的模糊度.
主要方法:
- 使用三个不同的图像数据集构建了与TM相关的BM响应线性模型.
- 通过在所有数据集中删除非显著的TM来改进模型.
- 在计算机生成的集群块状背景 (CLB) 图像上应用了五个级别的高斯模糊,以评估模糊分类中的BM和TM性能.
主要成果:
- 许多经常在线性模型中使用的TM反映了他们模拟的BM的特征.
- 虽然没有一个单一的BM能够区分CLB图像中的所有模糊度,但一组TM的子集显示了这种能力.
- 这些有效的TM很少被选择用于BM建模,这表明它们利用了不同的图像信息.
结论:
- 图像纹理显著影响传统模糊度量 (BMs) 的响应.
- 一个特定的TM组在CLB图像中的模糊度分类方面表现优于所有BM.
- 传统的BMs可能不是最佳的模糊分类在哺乳镜;TMs提供了一个有希望的替代方案.
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