Increasing transmission of medical images across multiple user systems raises concerns for image security. Hiding watermark information in medical image data files is one solution for enhancing security and privacy protection of data. Medical image watermarking however is not a widely studied area, due partially to speculations on loss in viewer performance caused by degradation of image information. Such concerns are addressed if the amount of information lost due to watermarking can be kept at minimal levels and below visual perception thresholds. This paper describes experiments where three alternative visual quality metrics were used to assess the degradation caused by watermarking medical images. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) medical images were watermarked using different methods: Block based Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with various embedding strengths. The visual degradation of each watermarking parameter setting was assessed using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Measure (SSIM) and Steerable Visual Difference Predictor (SVDP) numerical metrics. The suitability of each of the three numerical metrics for medical image watermarking visual quality assessment is noted. In addition, subjective test results from human observers are used to suggest visual degradation thresholds.
The growth of internet communications, multimedia storage capacity, and software sophistication triggered the need to protect intellectual property in digital media. Digital watermark can be inserted into images for copyright protection, copy protection, tamper detection and authentication. Unfortunately, geometrical robustness in digital image watermarking remains a challenging issue because consumer software enables rotational, scaling and translational attacks on the watermark with little image quality degradation. To balance robustness requirements and computation simplicity, we propose a method to re-synchronize watermark information for its effective detection. The method uses scale normalization and flowline curvature in embedding and detection processes. Scale normalization with unit aspect ratio and predefined area offers scale invariance and translation invariance. Rotational robustness is achieved using the flowline curvature properties of extracted robust corners. The watermark is embedded in Discrete Fourier Transform (DFT) domain of the normalized image using fixed strength additive embedding. Geometric properties recovery is simplified using flowline curvature properties and robust corners as reference points prior to watermark detection. Despite the non-blind nature and vulnerability to local transformations of this approach, experimental results indicate its potential application in robust image watermarking.