Analysis of DWT-DCT watermarking algorithm on digital medical imaging

Rajkumar Soundrapandiyan, Kannadasan Rajendiran, Arunkumar Gurunathan,Akila Victor,Ramani Selvanambi

JOURNAL OF MEDICAL IMAGING(2024)

引用 0|浏览4
暂无评分
摘要
Purpose: Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images. Approach: In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping 8x8 blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR. Results: The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76 dB. Conclusions: The proposed model works well for clinical medical images when compared with the existing techniques.
更多
查看译文
关键词
medical image,multiple watermarks,quantization,region of interest,non-interest region,discrete wavelet transform,discrete cosine transform,zigzag sort,Arnold transform,logistic encryption,peak signal-to-noise ratio,structural similarity index measure,mean square error
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要