On the correlation between second order texture features and human observer detection performance in digital images
On the correlation between second order texture features and human observer detection performance in digital images
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数字图像中二阶纹理特征与人类观察者检测性能的相关性
DOI：
10.1038/s41598-020-69816-z
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发表时间：
2020
影响因子：
4.6
通讯作者：
Das, Mini
中科院分区：
文献类型：
--
作者：
Nisbett, William H.;Kavuri, Amar;Das, Mini
图像纹理是图像中强度值的相对空间排列，它编码了有关场景的有价值的信息。就目前情况而言，这些潜在信息中的大部分仍未被开发。了解如何破译纹理细节将提供另一种从图像中提取物理世界知识的方法。在这项工作中，我们试图弥合定量纹理分析和纹理视觉感知之间的研究差距。图像纹理的变化对人类在复杂数字图像中执行信号检测和定位任务的能力的影响尚不清楚。我们通过研究基于任务的人类观察者在检测和定位乳腺断层图像中的信号的性能来研究这一关键问题。我们还研究了这些变化如何影响二阶图像织构的形成。我们使用数字乳房断层合成(DBT)，一种FDA批准的断层X光乳房成像方法作为我们初步结果的选择。我们的人类观察者研究包括用于DBT低对比度肿块检测的定位ROC(LROC)研究。使用模拟图像是因为它们提供了已知地面事实的好处。我们的结果证明，系统几何结构或处理方式的改变会导致图像纹理大小的变化。我们表明，在数字图像中估计的几个众所周知的纹理特征的变化与人类对嵌入其中的信号的观察者检测-定位性能相关。这种洞察力可以使有效和实用的技术通过检查这些纹理特征的变化来识别最佳成像系统设计和算法或过滤工具。这种将纹理特征估计和基于任务的图像质量评估联系起来的概念也可以扩展到其他几种成像方式和应用。它还可以在系统和算法设计中提供反馈，以提高感知效益。更广泛的影响可能涉及广泛的领域，包括成像系统设计、图像处理、数据科学、机器学习、计算机视觉、感知和视觉科学。我们的结果还指出，将这些纹理特征用作基于图像的放射学特征或作为风险评估的预测性标记时必须谨慎，因为它们对系统或图像处理变化敏感。
Image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. The impact of changes in image texture on human observer’s ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. Our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection–localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. It can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. Our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes.
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影响因子：
3.8
作者：
Gifford,HowardC;Liang,Zhihua;Das,Mini
通讯作者：
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DOI：
10.1117/12.2294981
发表时间：
2018
期刊：
SPIE Medical Imaging : Physics of Imaging
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作者：
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DOI：
10.1117/12.2256113
发表时间：
2017
期刊：
The Journal of Nuclear Medicine
影响因子：
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作者：
William H. Nisbett;Amareswararao Kavuri;N. Fredette;M. Das
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DOI：
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发表时间：
2008
期刊：
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影响因子：
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作者：
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通讯作者：
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影响因子：
3.8
作者：
Zheng, Yuanjie;Keller, Brad M.;Kontos, Despina
通讯作者：
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