数字图像中可计算图像纹理特征的影响因素及鲁棒性评估。

IF 2.2 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Diego Andrade, Howard C Gifford, Mini Das
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引用次数: 0

摘要

背景/目标:使用纹理特征来提取隐藏的基于图像的信息是非常有趣的。在使用放射组学、人工智能或个性化医疗的医学成像应用中,追求的是提取患者或疾病特定信息,同时对其他系统或处理变量不敏感。虽然我们使用数字乳房断层合成(DBT)来显示这些效果,但我们的结果通常适用于更广泛的其他成像方式和应用。方法:我们研究了纹理估计方法中的因素,如量化、像素距离偏移和感兴趣区域(ROI)大小,这些因素会影响这些易于计算和广泛使用的图像纹理特征(特别是Haralick的灰度共生矩阵(GLCM)纹理特征)的大小。结果:我们的研究结果表明,量化是这些参数中影响最大的,因为它控制着GLCM的大小和取值范围。我们提出了一种新的多分辨率归一化(通过固定ROI大小或像素偏移),可以显着减少量化幅度差异。我们显示特征值的平均差异减少了几个数量级;例如,在保持趋势的同时,在8-128的量化之间将其降低到7.34%。结论:当将来自多个供应商的图像组合在一起进行共同分析时,由于滤镜等后处理方法的差异,纹理大小可能会出现很大的变化。我们表明,GLCM量级变化的显著变化可能仅仅是由于过滤器类型或强度而引起的。这些趋势也可能根据估计变量(如偏移距离或ROI)而变化,这可能进一步使分析和稳健性复杂化。我们展示了降低由于估计方法引起的这种变化的敏感性的途径,同时增加了对患者特定信息(如乳腺密度)的期望敏感性。最后,我们证明了从模拟DBT图像中获得的结果与应用于临床DBT图像时所看到的结果是一致的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick's gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8-128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images.

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来源期刊
Tomography
Tomography Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
2.70
自引率
10.50%
发文量
222
期刊介绍: TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.
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