Name
Title | ||
|---|---|---|
A High-Capacity Reversible Watermarking Scheme Based On Shape Decomposition For Medical Images |
Abstract | ||
|---|---|---|
We present a high-capacity reversible, fragile, and blind watermarking scheme for medical images in this paper. A bottom-up saliency detection algorithm is applied to automatically locate the multiple arbitrarily-shaped regions of interest (ROIs). The iterative square-production algorithm is developed to generate different sizes of squares for shape decomposition on the regions of noninterest (RONIs). This scheme of combining the frequency-domain watermarking and arbitrarily-shaped ROI methods can significantly increase the watermarking capacity, whereas the embedded image fidelity is preserved. Extensive experiments were carried out on the OASIS medical image dataset, which consists of a cross-sectional collection of 416 subjects, aged from 18 to 96 years old. The results show that the proposed scheme outperforms six existing state-of-the-art schemes in terms of watermarking capacity and embedded image fidelity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1142/S0218001419500010 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Image watermarking, arbitrary shape, region of interest, medial axis transform | Digital watermarking,Pattern recognition,Salience (neuroscience),Artificial intelligence,Region of interest,Mathematics | Journal |
Volume | Issue | ISSN |
33 | 1 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Zhong | 1 | 11 | 4.69 |
| Frank Y. Shih | 2 | 1103 | 89.56 |