Open AccessA Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
Author(s) -
Shaekh Hasan Shoron,
Monamy Islam,
Jia Uddin,
Dongkoo Shon,
Ki Chun Im,
Jeong-Ho Park,
Dong-Sun Lim,
Byungtae Jang,
JongMyon Kim
Publication year - 2019
Publication title -
electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.36
H-Index - 36
ISSN - 2079-9292
DOI - 10.3390/electronics8090975
Subject(s) - digital watermarking , watermark , peak signal to noise ratio , artificial intelligence , computer science , quantization (signal processing) , discrete wavelet transform , computer vision , robustness (evolution) , otsu's method , fuzzy logic , digital image , pattern recognition (psychology) , wavelet , mathematics , wavelet transform , image (mathematics) , image processing , image segmentation , biochemistry , chemistry , gene
Digital watermarking is a process of giving security from unauthorized use. To protect the data from any kind of misuse while transferring, digital watermarking is the most popular authentication technique. This paper proposes a novel digital watermarking scheme for biomedical images. In the model, initially, the biomedical image is preprocessed using improved successive mean quantization transform (SMQT) which uses the Otsu’s threshold value. In the next phase, the image is segmented using Otsu and Fuzzy c-means. Afterwards, the watermark is embedded in the image using discrete wavelet transform (DWT) and inverse DWT (IDWT). Finally, the watermark is extracted from the biomedical image by executing the inverse operation of the embedding process. Experimental results exhibit that the proposed digital watermarking scheme outperforms the typical models in terms of effectiveness and imperceptibility while maintaining robustness against different attacks by demonstrating a lower bit error rate (BER), a cross-correlation value closer to one, and higher values of structural similarity index measures (SSIM) and peak signal-to-noise ratio (PSNR).