A Copyright- and Privacy-Protected Diabetic Retinopathy Diagnosis Network
Article Sidebar
Main Article Content
Abstract
This paper proposes a copyright- and privacyprotected diabetic retinopathy (DR) diagnosis network. In the network, DR lesions are automatically detected from a fundus image by firstly estimating non-uniform illumination of the image, and then the lesions are detected from the balanced image by using level-set evolution without re-initialization. The lesions are subsequently marked by using contours. The lesion-marked fundus image is subsequently shared for intra or inter hospital network diagnosis with copyright and privacy protection. Watermarking technique is used for image copyright protection, and visual encryption is used for privacy protection. Sign scrambling of two dimensional (2D) discrete cosine transform (DCT) and one dimensional (1D) DCT is proposed for lesion-marked fundus image encryption. The proposed encryption methods are compared with other transform-based encryption methods, i.e., discrete Fourier transform (DFT) amplitude-only images (AOIs), DCT AOIs, and JPEG 2000-based discrete wavelet transform (DWT) sign scrambling which were proposed for image trading system. Since the encryption is done after DR diagnosis, contours used for DR marking must also be visually encrypted. The proposed encryption methods are effective for strong-edge images that are suitable for lesion-marked fundus images, while random sign-based JPEG 2000, DFT AOIs, and DCT AOIs encrypt the images imperfectly. Moreover, the proposed methods are better in terms of image quality. In addition, watermarking performance and compression performance are confirmed by experiments.
Article Details
References
Technol. Biomed., vol. 12, no. 1, pp. 118–130, Jan. 2008.
J. Nayak, P. S. Bhat, R. A. U., and M. S. Kumar, “Efficient storage and transmission of digital fundus images with patient information using reversible watermarking technique and error control codes,” Journal of medical systems, vol. 33, issue 3, pp. 163–171, Jun. 2009.
M. Nergui, U. S. Acharya, U. R. Acharya, W. Yu, “Reliable and robust transmission and storage techniques for medical images with patient information,” Journal of Medical systems vol. 34, issue 6, pp. 1129–1139, 2010.
W. Sae-Tang, M. Fujiyoshi, and H. Kiya, “A Generation Method of Amplitude-Only Images with Low Intensity Ranges,” IEICE Trans. Fundamentals, vol. E96-A, no. 6, pp. 1323–1330, Jun. 2013.
W. Sae-Tang, S. LIU, M. FUJIYOSHI, and H. KIYA, “1D Frequency Transformation-Based Amplitude-Only Images for Copyright- and Privacy-Protection in Image Trading Systems,” ECTI-CIT, vol. 8, no. 2, Nov. 2014.
W. Sae-Tang, S. Liu, M. Fujiyoshi, and H. Kiya, “A copyright- and privacy-protected image trading system using fingerprinting in discrete wavelet domain with JPEG 2000,” IEICE Trans. Fundamentals, vol. E97-A, no. 11, Nov. 2014.
W. Sae-Tang, W. Chiracharit, and W. Kumwilaisak, “Exudates Detection in Fundus Images Using Non-uniform Illumination Background Subtraction,” Proc. IEEE TENCON2010, Fukuoka, Japan, pp. 204–209, Nov. 21-24, 2010.
[Online]. Available: http://www.mastereyeassociates.com/presbyopia.
S. Ravishankar, A. Jain, and A. Mittal, “Automated feature extraction for early detection of diabetic retinopathy in fundus images,” Proc. IEEE CVPR, Miami, FL, 2009.
H. Narasimha-Iyer, A. Can, B. Roysam, Charles V. Stewart, Howard L. Tanenbaum, A. Majerovics, and H. Singh, “Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy,” IEEE Trans. Biomed. Eng., vol. 53, no. 6, pp. 1084–1098, Jun. 2006.
H. Narasimha-Iyer, A. Can, B. Roysam, Howard L. Tanenbaum, and A. Majerovics, “Integrated analysis of vascular and nonvascular changes 122 ECTI TRANSACTIONS ON COMPUTER AND INFORMATION TECHNOLOGY VOL.10, NO.2 November 2016 from color retinal fundus image sequences,” IEEE Trans. Biomed. Eng., vol. 54, no. 8, pp. 1436–1445, Aug. 2007.
A. Sopharak, B. Uyyanonvara, and S. Barman, “Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy c-means clustering,” Sensors, pp. 2148–2161, 2009.
M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Medical Image Analysis, vol. 3, no. 9, pp. 179–190, 2005.
E. Grisan, A. Giani, E. Ceseracciu, and A. Ruggeri, “Model-based Illumination Correction in Retinal Images,” IEEE Int. Sym. on Biomed. Imag., pp. 984–987, Apr. 2006.
W. Sae-Tang, W. Chiracharit, S. Kiattisin, and W. Kumwilaisak, “Non-Uniform Illumination Estimation in Fundus Images Using Bounded Surface Fitting,” The international journal on applied biomedical engineering (IJABME), vol. 5, no. 1, pp. 37–45, 2012.
C. Li, C. Xu, C. Gui, and M. D. Fox, “Level-set evolution without re-initialization: A new variational formulation,” IEEE CVPR’05, 2005.
University of LINCOLN, Department of Computing and Informatics (MHAC MC3201), Retinal Vessel Image set for Estimation of Widths (REVIEW) databases, [Online]. Available: http://aldiri.info.
T. Kauppi, V. Kalesnykiene, J-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, et al. DIARETDB1: diabetic retinopathy database and evaluation protocol. In: Medical image understanding and analysis (MIUA), pp. 61–65, 2007.
A. Sopharak, B. Uyyanonvara, S. Barman, S. Vongkittirux, and N. Wongkamchang, “Fine exudate detection using morphological reconstruction enhancement,” The international journal on applied biomedical engineering (IJABME), vol. 1, no. 1, pp. 45–50, 2010.
T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathydetection of exudates in color fundus images of the human retina,” IEEE Trans. Med. Imag., vol. 21, no. 10, pp. 1236–1243, Oct. 2002.
D. Welfer, J. Scharcanski, and D. R. Marinho, “A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images,” Computerized Medical Imaging and Graphics, pp. 228-235, 2010.
G. Kande, P. Subbaiah, and T. Savithri, “Segmentation of exudates and optic disk in retinal images,” IEEE Sixth Indian Conference on Computer Vision, Graphic & Image Processing, 2008.
S. Pereira, S. Voloshynoskiy, and T. Pun, “Optimal transform domain watermark embedding via linear programing,” Signal Processing 81, pp. 1251–1260, 2001.
H. Inoue, A. Miyazaki, and T. katsura, “An image watermarking method based on the wavelet transform,” Proc. IEEE ICIP, pp. 296–300, 1999.
Z. Zhang, Q. Sun, and W. Wong, “A novel lossyto-lossless watermarking scheme for JPEG2000 images,” Proc. IEEE ICIP, pp. 573–576, 2004.
Y. Chen and H. Huang, “A progressive image watermarking scheme for JPEG2000,” Proc. IEEE IIH-MSP, pp. 230–233, 2012.
D. Taubman, “Kakadu software-a comprehensive framework for JPEG2000,” 2005.