Reversible Data Hiding Citra MRI T1-Weighted Menggunakan Spatial Fuzzy C-Means dan Selective Histogram Shifting
- Aufa Fadholi Suharyoto Universitas Dian Nuswantoro, Semarang, Indonesia
-
Elkaf Rahmawan Pramudya
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
(*) Corresponding Author
Abstract
The transmission of medical images over telemedicine networks increases the risk of data leakage and manipulation of sensitive information. This study develops a Reversible Data Hiding framework that integrates Spatial Fuzzy C-Means, Selective Histogram Shifting, and a measurable Distortion Control Mechanism for securing T1-weighted brain MRI images. The proposed method prioritizes the preservation of Region of Interest intensity characteristics and full reversibility over embedding capacity. SFCM is employed to generate Region of Interest and Non-Region of Interest mappings based on intensity distribution, with adaptive parameter adjustment for each slice. Data embedding is performed selectively on NROI using histogram shifting, while ROI areas remain unmodified. An Adaptive Feedback Control mechanism monitors image quality metrics SNR, CNR, GLCM with conservative thresholds (ΔSNR ≤ 2.0%, ΔCNR ≤ 1.0%) to ensure ROI stability. Experimental evaluation on the OASIS-1 dataset shows that the proposed method achieves an average PSNR of 54.13 dB, SSIM of 0.9996, and NCC of 0.9999, with an embedding capacity of 630 bits per slice (BPP 0.007-0.013 within NROI). Reversibility verification confirms perfect recovery (maximum difference = 0) for all samples. Batch testing on five slices demonstrates consistent performance across varying intensity characteristics, with ΔSNR and ΔCNR remaining at 0.0%. These results indicate that the method is capable of maintaining ROI technical integrity and pixel-perfect reversibility, although with a limited capacity suitable for lightweight metadata such as integrity hashes and patient identifiers. Limitations of the study include the technical-only evaluation without radiologist clinical validation and testing restricted to T1-weighted MRI modality.
Downloads
References
M. Magdy, K. M. Hosny, N. I. Ghali, and S. Ghoniemy, “Security of medical images for telemedicine: a systematic review,” Multimed Tools Appl, vol. 81, no. 18, pp. 25101–25145, Jul. 2022, doi: 10.1007/s11042-022-11956-7.
R. Verma, A. Kumari, A. Anand, and V. S. S. Yadavalli, “Revisiting Shift Cipher Technique for Amplified Data Security,” Journal of Computational and Cognitive Engineering, vol. 3, no. 1, pp. 8–14, Feb. 2024, doi: 10.47852/bonviewJCCE2202261.
W. S. Admass, Y. Y. Munaye, and A. A. Diro, “Cyber security: State of the art, challenges and future directions,” Jan. 01, 2024, KeAi Communications Co. doi: 10.1016/j.csa.2023.100031.
B. Jafrasteh, M. Lubián-Gutiérrez, S. P. Lubián-López, and I. Benavente-Fernández, “Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images,” Neuroinformatics, vol. 22, no. 4, pp. 407–420, Oct. 2024, doi: 10.1007/s12021-024-09661-x.
C. Singh, S. K. Ranade, D. Kaur, and A. Bala, “An Intuitionistic Fuzzy C-Means and Local Information-Based DCT Filtering for Fast Brain MRI Segmentation,” Journal of Imaging Informatics in Medicine, vol. 37, no. 5, pp. 2287–2310, Oct. 2024, doi: 10.1007/s10278-023-00899-6.
F. Ren, Y. Liu, X. Zhang, and Q. Li, “Reversible information hiding scheme based on interpolation and histogram shift for medical images,” Multimed Tools Appl, vol. 82, no. 18, pp. 28445–28471, Jul. 2023, doi: 10.1007/s11042-022-14300-1.
B. A. Shtayt, N. H. Zakaria, and N. H. Harun, “A comprehensive review on medical image steganography based on LSB technique and potential challenges,” Baghdad Science Journal, vol. 18, pp. 957–974, Jun. 2021, doi: 10.21123/bsj.2021.18.2(Suppl.).0957.
M. A. Hameed, M. Hassaballah, R. Abdelazim, and A. K. Sahu, “A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 379–397, Jan. 2024, doi: 10.1016/j.ijcce.2024.08.002.
C. Jatmoko et al., “Optimized Visualization of Digital Image Steganography using Least Significant Bits and AES for Secret Key Encryption,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Jul. 2025, doi: 10.22219/kinetik.v10i3.2252.
M. M. Eltoukhy, F. S. Alsubaei, M. M. Abdel-Aziz, and K. M. Hosny, “RDHNet: Reversible Data Hiding Method for Securing Colour Images Using AlexNet and Watershed Transform in a Fusion Domain,” CAAI Trans Intell Technol, Oct. 2025, doi: 10.1049/cit2.70038.
K. Chen et al., “Restoration-Enhanced Reversible Information Steganography Network for CT Images in the Internet of Medical Things,” IEEE Trans Instrum Meas, vol. 74, p. 4011615, Feb. 2025, doi: 10.1109/TIM.2025.3542878ï.
O. Veselska and R. Ziubina, “Reversible image steganography using transformer-based latent embedding,” Advances in Science and Technology Research Journal, vol. 19, no. 8, pp. 148–164, Jun. 2025, doi: 10.12913/22998624/204419.
A. N. Shilpa and C. S. Veena, “A Hybrid Compressive Sensing Network for ROI-based Medical Image Recovery,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 3, pp. 8–19, Mar. 2023, doi: 10.14445/23488379/IJEEE-V10I3P102.
H. Ye, K. Su, X. Cheng, and S. Huang, “Research on reversible image steganography of encrypted image based on image interpolation and difference histogram shift,” IET Image Process, vol. 16, no. 7, pp. 1959–1972, May 2022, doi: 10.1049/ipr2.12461.
H. Ragab, H. Shaban, K. Ahmed, and A. E. Ali, “Digital Image Steganography and Reversible Data Hiding: Algorithms, Applications and Recommendations,” Journal of Image and Graphics (United Kingdom), vol. 13, no. 1, pp. 90–114, Feb. 2025, doi: 10.18178/joig.13.1.90-114.
L. C. Huang, S. F. Chiou, and M. S. Hwang, “A Reversible Data Hiding Based on Histogram Shifting of Prediction Errors for Two-Tier Medical Images,” Informatica (Netherlands), vol. 32, no. 1, pp. 69–84, Jun. 2020, doi: 10.15388/20-INFOR422.
D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, “Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” J Cogn Neurosci, vol. 19, no. 9, pp. 1498–1507, Sep. 2007, doi: 10.1162/jocn.2007.19.9.1498.
A. S. Saracoglu, A. M. Acilar, and O. Cicek Erdas, “ADNet: A CNN MODEL FOR ALZHEIMER’S DISEASE DIAGNOSIS ON OASIS-1 DATASET,” Kahramanmaras Sutcu Imam University Journal of Engineering Sciences, Dec. 2024, doi: 10.17780/ksujes.1534327.
K. T. Chui, B. B. Gupta, W. Alhalabi, and F. S. Alzahrani, “An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning,” Diagnostics, vol. 12, no. 7, Jul. 2022, doi: 10.3390/diagnostics12071531.
N. A. Ali, A. El Abbassi, and O. Bouattane, “Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation,” Multimed Tools Appl, vol. 82, no. 5, pp. 6787–6805, Feb. 2023, doi: 10.1007/s11042-022-13635-z.
J. Simarro et al., “A deep learning model for brain segmentation across pediatric and adult populations,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-61798-6.
M. Li, J. Zhou, D. Wang, P. Peng, and Y. Yu, “Application of Clustering-Based Analysis in MRI Brain Tissue Segmentation,” Comput Math Methods Med, vol. 2022, Aug. 2022, doi: 10.1155/2022/7401184.
R. Abbasi, J. Chen, Y. Al-Otaibi, A. Rehman, A. Abbas, and W. Cui, “RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction,” Multimed Syst, vol. 27, no. 2, pp. 177–189, Apr. 2021, doi: 10.1007/s00530-020-00718-w.
C. F. Lin, Y. X. Lin, and S. H. Chang, “Medical Image Encryption Using Chaotic Mechanisms: A Study †,” Jul. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/bioengineering12070734.
G. Peter, A. Sherine, Y. Teekaraman, R. Kuppusamy, and A. Radhakrishnan, “Histogram Shifting-Based Quick Response Steganography Method for Secure Communication,” Wirel Commun Mob Comput, vol. 2022, Mar. 2022, doi: 10.1155/2022/1505133.
B. A. Wahyudi, I. Palupi, and M. Fadhlan, “REVERSIBLE DATA HIDING PADA GAMBAR DIGITAL DENGAN SISTEM OTENTIKASI TERINTEGRASI,” vol. 12, no. 5, pp. 2355–7699, Oct. 2025, doi: https://doi.org/10.25126/jtiik.2025125.
H. T. Wu, X. Cao, R. Jia, and Y. M. Cheung, “Reversible Data Hiding With Brightness Preserving Contrast Enhancement by Two-Dimensional Histogram Modification,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 11, pp. 7605–7617, Nov. 2022, doi: 10.1109/TCSVT.2022.3180007.
G. D. Su, C. C. Lin, and C. C. Chang, “Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation,” J Healthc Eng, vol. 2021, Jul. 2021, doi: 10.1155/2021/5709513.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Reversible Data Hiding Citra MRI T1-Weighted Menggunakan Spatial Fuzzy C-Means dan Selective Histogram Shifting
Pages: 2168-2180
Copyright (c) 2026 Aufa Fadholi Suharyoto, Elkaf Rahmawan Pramudya
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
