Noise-Reduced 3D Organ Modeling from CT Images Using Median Filtering for Anatomical Preservation in Medical 3D Printing

Authors

  • Phichitphon Chotikunnan Rangsit University https://orcid.org/0000-0002-6617-6805
  • Rawiphon Chotikunnan Rangsit University
  • Tasawan Puttasakul Rangsit University
  • Wanida Khotakham Rangsit University
  • Pariwat Imura Rangsit University
  • Jaroonrut Prinyakupt Rangsit University
  • Nuntachai Thongpance Rangsit University
  • Anuchart Srisiriwat Pathumwan Institute of Technology

DOI:

https://doi.org/10.18196/jrc.v6i4.26665

Keywords:

Median Filtering, Medical Image Processing, STL File Generation, DICOM

Abstract

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.

Author Biographies

Phichitphon Chotikunnan, Rangsit University

Assoc. Prof. Acting Sub LT. Phichitphon Chotikunnan is a Lecturer of the Biomedical Engineering Program at the College of Biomedical Engineering, Rangsit University. He has expertise in robotics, embedded systems, fuzzy logic control, and iterative learning control. He holds a Doctor of Engineering degree in Electrical and Information Engineering and a Master of Engineering in Electrical Engineering, both from King Mongkut's University of Technology Thonburi. He also has a Bachelor of Engineering in Mechatronics Engineering from Pathumwan Institute of Technology.

He has published in international journals and conferences, and he has been involved in various research projects. His work experiences include positions as a Teaching Assistant, Control and Instrumentation Engineer, R&D Embedded Applications, Lecturer, and R&D Consultant. He has also participated in numerous training programs and workshops, and he has received several awards for his research excellence.

Rawiphon Chotikunnan, Rangsit University

He is a Lecturer in the Biomedical Engineering Program at the College of Biomedical Engineering, Rangsit University. With a Master of Engineering in Biomedical Engineering from Rangsit University and a Bachelor of Information Technology in Interactive Design and Game Development from Dhurakij Pundit University, his Research Interests Include Interactive Media, Medical Image Processing, Robots, and Control Systems.

Tasawan Puttasakul, Rangsit University

She obtained her Bachelor of Science in Physics-Electronics with Second Class Honors from Naresuan University, Thailand, her Master of Engineering in Biomedical Electronics Engineering, and her Doctor of Philosophy in Biomedical Engineering from King Mongkut’s Institute of Technology Ladkrabang, Thailand. Since 2009, she has held the position of lecturer in the College of Biomedical Engineering, Rangsit University. Her pedagogical and research interests encompass biomedical signal processing, biomedical image processing, and biosensors. She has authored many publications in international journals and conferences, concentrating on medical sensors, robotic control, and biomedical image processing.

Wanida Khotakham, Rangsit University

She obtained her Bachelor of Engineering in Automation Engineering from King Mongkut's University of Technology Thonburi, Thailand and her Master of Science in Data Science from Newcastle University, UK. Currently, she is a lecturer in the College of Biomedical Engineering at Rangsit University, where she teaches courses on software design, health information technology, data analytics, and automation engineering.

Pariwat Imura, Rangsit University

He serves as a Lecturer in the Biomedical Engineering Program at the College of Biomedical Engineering, Rangsit University. He holds a Master of Engineering in Biomedical Engineering from Rangsit University and completed his Bachelor of Science Program in Computer Science at Rajamangala University of Technology Lanna. His research interests span Medical Imaging Systems, Fundamental Principles of Computer Communication Networks and Database Management, Smart Medical Systems, Big Data Analytics in Medical, Medical Artificial Intelligence, and embedded systems.

Jaroonrut Prinyakupt, Rangsit University

She earned her Bachelor of Engineering in Electrical Engineering from Prince of Songkla University, Thailand; her Master of Science in Biomedical Instrumentation from Mahidol University, Thailand; and her Doctor of Philosophy in Electrical Engineering from Chulalongkorn University, Thailand.  She presently holds the position of assistant professor in the College of Biomedical Engineering at Rangsit University, concentrating on pedagogy and research in biomedical instruments, electronic systems, image processing, and programming.

Nuntachai Thongpance, Rangsit University

He currently holds the position of Associate Professor and Dean of the College of Biomedical Engineering at Rangsit University. He established undergraduate and graduate courses in medical instrumentation and biomedical engineering at Rangsit University. Nuntachai earned his Master of Engineering in nuclear technology from Chulalongkorn University in 1987 and his Bachelor of Science in physics with second-class honors from Prince of Songkla University in 1984. His research interests encompass medical devices, biomedical engineering, and healthcare management engineering.

Anuchart Srisiriwat, Pathumwan Institute of Technology

He holds the position of Associate Professor in Electrical Engineering at Pathumwan Institute of Technology, Thailand. He obtained his Ph.D. in electrical engineering from King Mongkut’s University of Technology North Bangkok. His scholarly pursuits encompass fuel cell systems, renewable energy, control systems, and engineering education. He had vast expertise in academic administration and has received many national awards and royal honors for his services to education and society.

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2025-06-19

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[1]
P. Chotikunnan, “Noise-Reduced 3D Organ Modeling from CT Images Using Median Filtering for Anatomical Preservation in Medical 3D Printing”, J Robot Control (JRC), vol. 6, no. 4, pp. 1600–1611, Jun. 2025.

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Vol. 6 No. 4 (2025)

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Copyright (c) 2025 Phichitphon Chotikunnan, Rawiphon Chotikunnan, Tasawan Puttasakul, Wanida Khotakham, Pariwat Imura, Jaroonrut Prinyakupt, Nuntachai Thongpance, Anuchart Srisiriwat

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This journal is based on the work at https://journal.umy.ac.id/index.php/jrc under license from Creative Commons Attribution-ShareAlike 4.0 International License. You are free to:

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The licensor cannot revoke these freedoms as long as you follow the license terms, which include the following:

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