A High-Capacity Reversible Watermarking Scheme Based on Shape Decomposition for Medical Images

Xin Zhong, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Article number1950001
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume33
Issue number1
DOIs
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Image watermarking
  • arbitrary shape
  • medial axis transform
  • region of interest

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