Bölütlenen beyin bölgelerinin tıbbi görüntü steganografi için değerlendirilmesi

Öz

Tıbbi görüntü steganografisinde, görüntülere veri gizlemenin neden olduğu bozulmanın sonucunda bir hastalığın tanı ve tedavisi etkilenebilir. Bu sebeple, veri görüntülerde elle ya da eşikleme gibi temel tekniklerle belirlenen ilgi olmayan bölgelerde gizlenmektedir ve bu yöntemlerin hiçbiri tümör gibi dokuları bölütlemeyi içermemektedir. Bu çalışma, bir hastalığın tanı ve tedavisinde kullanılan verilerin, bölütleme tabanlı steganografi yöntemi ile görüntüleri bozmadan tek bir ortamda birleştirilerek gizlenmesini amaçlamaktadır. Ayrık dalgacık dönüşümü (ADD) ve k-ortalama kümeleme tabanlı bölütleme yöntemi ile epilepsi hastalarının Manyetik Rezonans (MR) görüntüleri, arka plan, gri madde, beyaz madde ve tümör olarak ayrıştırılmıştır. Gizli mesaj, hasta kişisel bilgilerini, doktor yorumunu, seçilen Elektroansefalogram (EEG) sinyalini ve EEG’ye ait sağlık raporunu içermektedir. Kaotik ve hash fonksiyonlarını kullanan DNA kodlama ile şifrelenen ve ardından sıkıştırılan yüksek kapasiteli mesaj, görüntülerin tümör olmayan piksellerinin en az anlamlı bitlerinde gizlenmiştir. Çalışmada, taşıyıcı ve stego görüntüler arasındaki farklılık, sinyalin gürültü tepe oranı, yapısal benzerlik ölçümü, evrensel kalite indeksi ve korelasyon katsayısı ile tespit edilmiştir. Bu değerler sırasıyla 64,0334 desibel (dB), 0,9979, 0,99701, 0,9993 olarak elde edilmiştir. Analiz sonuçları önerilen yöntemin hastaların yüksek kapasiteli verilerini tek bir dosyada birleştirdiğini ve tıbbi verilerin hem güvenliğini hem de kayıt alanını arttırdığını göstermiştir.

Anahtar Kelimeler

• tıbbi görüntü steganografisi,ayrık dalgacık dönüşümü,k-ortalama kümeleme,tıbbi görüntü bölütleme

Kaynakça

1. Karakis R., Guler I., Chapter 22: Steganography and Medical Data Security, Cryptographic and Information Security Approaches for Images and Videos, Editor Ramakrishnan, S., CRC Press, 627-660, ISBN: 9781138563841, 2019.
2. Karakis R., Guler I., Capraz I., Bilir E., A novel fuzzy logic based image steganography method to ensure medical data security, Computers in Biology and Medicine, 67, 172-183, 2015.
3. Coatrieux G., Maitre H., Sankur B., Rolland Y., Collorec R., Relevance of Watermarking in Medical Imaging, Information Technology Applications in Biomedicine, Proceedings of IEEE EMBS International Conference on, 250-255, 2000.
4. Nyeem H., Boles W., Boyd C., A Review of Medical Image Watermarking Requirements for Teleradiology, J. Digit. Imaging, 26, 326-343, 2013.
5. Coatrieux G., Lecornu L., Sankur B., Roux C., A Review of Image Watermarking Applications in Healthcare, Engineering in Medicine and Biology Society. EMBS’ 06. 28th Annual International Conference of the IEEE, 4691-4694, 2006.
6. Kuang L.-Q., Zhang Y., Han X., Watermarking Image Authentication in Hospital Information System, Information Engineering and Computer Science ICIECS, 1-4, 2009.
7. Haidekker M., Image Storage, Transport, and Compression, Wiley-IEEE Press. Edition: 1, 386-412, 2011.
8. About DICOM, The National Electrical Manufacturers Association (NEMA), http://medical.nema.org/Dicom/about-DICOM.html, Erişim Tarihi 15 Haziran 2020.

9. Oosterwijk H., The DICOM standard, overview and characteristics, http://www.ringholm.com/docs/02010_en.htm, Erişim tarihi 15 Haziran 2020.
10. Cheddad A., Condell J., Curran K., McKevitt P., Digital image steganography: Survey and analysis of current methods, Signal Processing, 90, 727-752, 2010.
11. Li B., He J., Huang J., Shi Y.Q., A Survey on Image Steganography and Steganalysis, Journal of Information Hiding and Multimedia Signal Processing, 2 (2), 142-172, 2011.
12. Nambakhsh M.S., Ahmadian A., Zaidi H., A contextual based double watermarking of PET images by patient ID and ECG signal, Computer Methods and Programs in Biomedicine, 104 (3), 418-425, 2011.
13. Nambakhsh M.S., Ahmadian A., Ghavami M., Dilmaghani R.S., Karimi-Fard S., A Novel Blind Watermarking of ECG Signals on Medical Images Using EZW Algorithm, Proceedings of the 28th IEEE-EMBS Annual International Conference New York City, USA, 3274-3277, 2006.
14. Karakis R., Capraz I., Bilir E., Guler I., EEG Source Localization Using a Genetic Algorithm Based Artificial Neural Network, Recent Patents on Biomedical Engineering, 6, 188-194, 2014.
15. Smith S.J.M, EEG in the diagnosis, classification, and, management of patients with epilepsy, J Neurol. Neurosurg. Psychiatry, 76, (Suppl 2), ii2–ii7, 2005.
16. Cendes F., Theodore W.H., Brinkmann B.H., Sulc V., Cascino G.D., Neuroimaging of epilepsy, Handb. Clin Neurol, 136, 985-1014, 2016.
17. Miaou S.-G., Hsu C.-M., Tsai Y.-S., Chao H.-M., A secure data hiding technique with heterogeneous data-combining capability for electronic patient records, Engineering in Medicine and Biology Society. Proceedings of the 22nd Annual International Conference of the IEEE, 1, 280-283, 2000.
18. Giakoumaki A., Pavlopoulos S., Koutsouris D., Secure and efficient health data management through multiple watermarking on medical images, Medical and Biological Engineering and Computing, 44(8), 619-631, 2006.
19. Anand D., Niranjan U.C., Watermarking medical images with patient information, Engineering in Medicine and Biology Society. in: Proceedings of the 20th Annual International Conference of the IEEE, 2, 703-706, 1998.
20. Acharya U.R., Subbanna Bhat P., Kumar S., Min L.C., Transmission and storage of medical images with patient information, Comput Biol Med., 33(4), 303-10, 2003.
21. Acharya U.R., Niranjan U.C., Iyengar S.S, Kannathal N., Min L.C., Simultaneous storage of patient information with medical images in the frequency domain, Computer Methods and Programs in Biomedicine, 76(1), 13-19, 2004.
22. Karakis R., Gurkahraman K., Cigdem B., Oztoprak I., Topaktas A.S., Hiding Patient Information into Magnetic Resonance Images Using DNA Based Wavelet Transform, The Conferences of International Journal of Arts & Sciences’ (IJAS) Montreal-Canada, Academic Journal of Science, CD-ROM. ISSN: 2165-6282., 08(02), 161–170, 2018.
23. Memon N.A., Gilani S.A.M., Watermarking of chest CT scan medical images for content authentication, International Journal of Computer Mathematics, 88(2), 265-280, 2011.
24. Zain J.M., Fauzi A.R.M., Aziz A.A., Clinical Evaluation of Watermarked Medical Images, 28th IEEE EMBS Annual International Conference, New York City, USA, 5459- 5462, 2006.
25. Nyeem H., Boles W., Boyd C., Content-independent embedding scheme for multi-modal medical image watermarking, BioMedical Engineering OnLine 2015, 14(7), 1-19, 2015.
26. Al-Dmour H., Al-Ani A., Quality optimized medical image steganography based on edge detection and hamming code, IEEE 12th International Symposium on Biomedical Imaging (ISBI), 1486-1489, 2015.
27. Ravali K., Kumar A.P., Asadi S., Carrying Digital Watermarking for Medical Images using Mobile Devices, IJCSET, 1(7), 366-369, 2011.
28. Shukla A., Singh C., Medical Image Authentication Through Watermarking, International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014), 2(2), 292-295, 2014.
29. Fatemizadeh E., Maneshi M., A New Watermarking Algorithm Based On Human Visual System for Content Integrity Verification of Region of Interest, Computing and Informatics, 31, 877-899, 2012.
30. Rathi S.C., Inamdar V.S., Medical Images Authentication Through Watermarking Preserving ROI, Health Informatics - An International Journal (HIIJ), 1(1), 27-42, 2012.
31. Alsaade F.W., Watermarking System for the Security of Medical Image Databases used in Telemedicine, Res. J. Inform. Technol., 8(3), 88-97, 2016.
32. Al-Haj A., Mohammad A., Amer A., Crypto-Watermarking of Transmitted Medical Images, Journal of Digital Imaging, 30(1), 26–38, 2016. 33. Rahimi F., Rabbani H., A dual adaptive watermarking scheme in contourlet domain for DICOM image, BioMedical Engineering OnLine, 10(53), 1-18, 2011.
33. Guesmi R., Farah M.A.B., Kachouri A., Samet M., A novel chaos-based image encryption using DNA sequence operation and Secure Hash Algorithm SHA-2, Nonlinear Dyn., 83, 1123-1136, 2016.
34. Satheesh P., MATLAB Central File Exchange, DNA crytography with encoding and decoding text message, https://www.mathworks.com/matlabcentral/fileexchange/68817-dna-crytography-with-encoding-and-decoding-text-message, 2020, Erişim tarihi 15.06.2020.
35. Sezgin M., Sankur B., Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging, 13(1), 146–165, 2004.
36. Gonzales R., Woods R.E., Digital Image Processing, Pearson Prentice Hall, USA, 2008. 38. Gordillo N., Montseny E., Sobrevillac P., State of the art survey on MRI brain tumor segmentation, Magnetic Resonance Imaging, 31(8), 1426-1438, 2013.
37. Smistad E., Falch T.L., Bozorgi M., Elster A.C., Lindseth F., Medical image segmentation on GPUs-A comprehensive review, Medical Image Analysis, 20(1), 1-18, 2015.
38. Despotović I., Goossens B., Philips W., MRI Segmentation of the Human Brain: Challenges, Methods, and Applications, Computational and Mathematical Methods in Medicine, Article ID 450341, 1-23, 2015.
39. Abdel-Maksoud E., Elmogy M., Al-Awadi R., Brain tumor segmentation based on a hybrid clustering technique, Egyptian Informatics Journal, 16, 71–81, 2015.
40. Wu M., Lin M., Chang C., Brain tumor detection using color-based k-means clustering segmentation, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 245-250, 2007.
41. Nimeesha K.M., Gowda R.M., Brain Tumour Segmentation Using K-Means and Fuzzy C-Means Clustering Algorithm, International Journal of Computer Science & Information Technology Research Excellence, 3(2), 60-65, 2013.
42. Dhanalakshmi P., Kanimozhi T., Automatic segmentation of brain tumor using K-Means clustering and its area calculation, International Journal of advanced electrical and Electronics Engineering, 2(2), 130-134, 2013.
43. Perona P., Malik J., Scale-space and edge detection using anisotropic Diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629-639, 1990.
44. Wang Z., Bovik A.C., A Universal Image Quality Index, IEEE Signal Processing Letters, 9(3), 81-84, 2002.