Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study

Palani, Dharmendran; Ganesh, Kadirampatti M.; Karunagaran, Lavanya; Govindaraj, Kesavan; Shanmugam, Senthilkumar

doi:10.31557/APJCP.2023.24.6.2061

Statistical Analysis on Impact of Image Preprocessing of CT Texture Patterns and Its CT Radiomic Feature Stability: A Phantom Study

Document Type : Research Articles

Authors

¹ Research and Development Centre, Bharathiar University, Coimbatore, India.

² Department of Radiation Physics, Kidwai Memorial Institute of Oncology, Bengaluru, India.

³ Department of Oral and Maxillofacial Pathology,Asan Memorial Dental College and Hospital, Chennai, India.

⁴ Department of Radiotherapy, Vadamalayan Hospitals Integrated Cancer Centre, Madurai, India.

⁵ Department of Radiotherapy Government Rajaji Hospital & Madurai Medical College, Madurai, India.

10.31557/APJCP.2023.24.6.2061

Abstract

Aim: To examine computed tomography (CT) radiomic feature stability on various texture patterns during pre-processing utilizing the Credence Cartridge Radiomics (CCR) phantom textures. Materials and Methods: 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. Results: 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. Conclusion: 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.

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