Research and Development Centre, Bharathiar University, Coimbatore, India.
Department of Radiation Physics, Kidwai Memorial Institute of Oncology, Bengaluru, India.
Asian Pac J Cancer Prev. 2023 Jun 1;24(6):2061-2072. doi: 10.31557/APJCP.2023.24.6.2061.
To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures.
Imaging Biomarker Explorer (IBEX) expansion for the abbreviation IBEX extracted 51 radiomic features of 4 categories from 11 textures image regions of interest (ROI) of the phantom. 19 software pre-processing algorithms processed each CCR phantom ROI. All ROI texture processed image features were retrieved. Pre-processed CT image radiomic features were compared to non-processed features to measure its textural influence. Wilcoxon T-tests measured the pre-processing relevance of CT radiomic features on various textures. Hierarchical cluster analysis (HCA) was performed to cluster processer potency and texture impression likeness.
The pre-processing filter, CT texture Cartridge, and feature category affect the CCR phantom CT image's radiomic properties. Pre-processing is statistically unaltered by Gray Level Run Length Matrix (GLRLM ) expansion for the abbreviation GLRLM and Neighborhood Intensity Difference matrix (NID) expansion for the abbreviation NID feature categories. The 30%, 40%, and 50% honeycomb are regular directional textures and smooth 3D-printed plaster resin, most of the image pre-processing feature alterations exhibited significant p-values in the histogram feature category. The Laplacian Filter, Log Filter, Resample, and Bit Depth Rescale Range pre-processing algorithms hugely influenced histogram and Gray Level Co-occurrence Matrix (GLCM) image features.
We found that homogenous intensity phantom inserts, CT radiomic feature, are less sensitive to feature swaps during pre-processing than normal directed honeycomb and regular projected smooth 3D-printed plaster resin CT image textures. Because they lose fewer information during image enhancement, This feature concentration empowerment of the images also enhances texture pattern recognition.
利用 Credence 墨盒放射组学（CCR）体模纹理，研究不同纹理模式下预处理时 CT 放射组学特征的稳定性。
成像生物标志物探索者（IBEX）扩展了缩写词 IBEX，从体模的 11 个感兴趣区域（ROI）的 4 个类别的 11 个纹理 ROI 中提取了 51 个放射组学特征。19 种软件预处理算法处理了每个 CCR 体模 ROI。检索了所有 ROI 纹理处理后的图像特征。将预处理后的 CT 图像放射组学特征与非处理特征进行比较，以测量其纹理影响。Wilcoxon T 检验测量了 CT 放射组学特征在不同纹理上的预处理相关性。进行了层次聚类分析（HCA）以聚类处理能力和纹理印象相似性。
预处理滤波器、CT 纹理 Cartridge 和特征类别会影响 CCR 体模 CT 图像的放射组学特性。对于灰度游程长度矩阵（GLRLM）扩展缩写 GLRLM 和邻域强度差矩阵（NID）扩展缩写 NID 特征类别，预处理在统计上不会改变。30%、40%和 50%的蜂窝是规则的方向纹理，而光滑的 3D 打印石膏树脂，大多数图像预处理特征变化在直方图特征类别中表现出显著的 p 值。拉普拉斯滤波器、对数滤波器、重采样和位深度重采样范围预处理算法对直方图和灰度共生矩阵（GLCM）图像特征有很大影响。
我们发现，均匀强度体模插入物的 CT 放射组学特征在预处理过程中对特征交换的敏感性低于正常定向蜂窝和规则投影光滑 3D 打印石膏树脂 CT 图像纹理。由于它们在图像增强过程中丢失的信息较少，因此图像的这种特征集中增强也增强了纹理模式识别。
