Noise-Reduced 3D Organ Modeling from CT Images Using Median Filtering for Anatomical Preservation in Medical 3D Printing - Chotikunnan, Phichitphon; Chotikunnan, Rawiphon; Puttasakul, Tasawan; Khotakham, Wanida; Imura, Pariwat; Prinyakupt, Jaroonrut; Thongpance, Nuntachai; Srisiriwat, Anuchart (Universitas Muhammadiyah Yogyakarta, 2025)
This study offers a systematic approach to improving the reconstruction of three-dimensional anatomical models from CT imaging data. The main difficulty tackled is the maintenance of internal bone features during denoising, essential for producing clinically relevant models. A nonlinear filtering strategy was implemented, utilizing a 3×3 median filter alongside manual refinement to eliminate salt-and-pepper noise while preserving anatomical information. The study presents a reproducible image-processing pipeline that improves structural clarity and enables material-efficient 3D printing while preserving internal bone integrity. A publicly available dataset including 813 anonymized chest CT scans (512×512 pixels, 16-bit grayscale) from Zenodo was employed. Preprocessing included grayscale normalization, brightness adjustment, and the application of median filters with kernel sizes from 3×3 to 9×9, followed by artifact removal using FlashPrint software before STL conversion. The 3×3 median filter achieved an excellent balance between noise reduction and anatomical clarity, outperforming mean filtering and larger kernels in maintaining edge detail. Although statistical evaluation was not conducted, visual analysis validated an 18.07 percent decrease in print time and a 17.88 percent reduction in filament consumption. The technology exhibited actual efficacy in generating high-quality anatomical models. Future endeavors will incorporate automated segmentation and sophisticated denoising methodologies to enhance applicability in surgical simulation, clinical education, and personalized healthcare planning.