Department of Imaging, Gustave Roussy Cancer Campus, Villejuif Cedex, France.
IR4M-UMR8081, CNRS, Univ Paris Sud, University Paris Saclay, Orsay Cedex, France.
Med Phys. 2018 Apr;45(4):1529-1536. doi: 10.1002/mp.12809. Epub 2018 Mar 13.
Texture analysis is an emerging tool in the field of medical imaging analysis. However, many issues have been raised in terms of its use in assessing patient images and it is crucial to harmonize and standardize this new imaging measurement tool. This study was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images and to determine their redundancy.
For the reproducibility and discriminatory analysis, eight identical CT acquisitions were performed on a phantom including one homogeneous insert and two close heterogeneous inserts. Texture indices were selected for their high reproducibility and capability of discriminating different textures. For the redundancy analysis, 39 acquisitions of the same phantom were performed using varying acquisition parameters and a correlation matrix was used to explore the 2 × 2 relationships. LIFEx software was used to explore 34 different parameters including first order and texture indices.
Only eight indices of 34 exhibited high reproducibility and discriminated textures from each other. Skewness and kurtosis from histogram were independent from the six other indices but were intercorrelated, the other six indices correlated in diverse degrees (entropy, dissimilarity, and contrast of the co-occurrence matrix, contrast of the Neighborhood Gray Level difference matrix, SZE, ZLNU of the Gray-Level Size Zone Matrix).
Care should be taken when using texture analysis as a tool to characterize CT images because changes in quantitation may be primarily due to internal variability rather than from real physio-pathological effects. Some textural indices appear to be sufficiently reliable and capable to discriminate close textures on CT images.
纹理分析是医学影像分析领域的一种新兴工具。然而，在评估患者图像时，已经提出了许多关于其使用的问题，因此协调和标准化这一新的成像测量工具至关重要。本研究旨在评估包括重复性研究在内的 CT 图像纹理指数的可靠性，评估在 CT 医学图像中潜在相关的指数的区分能力，并确定其冗余性。
为了进行重复性和区分性分析，在一个包括一个均匀插入物和两个靠近的异质插入物的体模上进行了 8 次相同的 CT 采集。选择纹理指数是因为它们具有较高的可重复性和区分不同纹理的能力。为了进行冗余性分析，对同一体模进行了 39 次不同采集参数的采集，并使用相关矩阵来探索 2×2 关系。使用 LIFEx 软件探索了 34 个不同的参数，包括一阶和纹理指数。
在 34 个指数中，只有 8 个指数表现出较高的可重复性，并能区分不同的纹理。直方图中的偏度和峰度与其他 6 个指数独立，但相互关联，其他 6 个指数在不同程度上相关（共生矩阵的对比度、不相似性和熵，邻域灰度差矩阵的对比度，灰度大小区域矩阵的 SZE 和 ZLNU）。
在将纹理分析作为一种工具来描述 CT 图像时，应谨慎使用，因为定量变化可能主要是由于内部变异性而不是真实的生理病理效应。一些纹理指数似乎足够可靠，能够区分 CT 图像上的相近纹理。
