A New Watermarking Algorithm Based on Human Visual System for Content Integrity Verification of Region of Interest

This paper proposes a semi-fragile, robust-to-JPEG2000 compression watermarking method which is based on the Human Visual System (HVS). This method is designed to verify the content integrity of Region of Interest (ROI) in tele-radiology images. The design of watermarking systems based on HVS leads to the possibility of embedding watermarks in places that are not obvious to the human eye. In this way, notwithstanding increased capacity and robustness, it becomes possible to hide more watermarks. Based on perceptual model of HVS, we propose a new watermarking scheme that embeds the watermarks using a replacement method. Thus, the proposed method not only detects the watermarks but also extracts them. The novelty of our ROI-based method is in the way that we interpret the obtained coefficients of the HVS perceptual model: instead of interpreting these coefficients as weights, we assume them to be embedding locations. In our method, the information to be embedded is extracted from inter-subband statistical relations of ROI. Then, the semi-fragile watermarks are embedded in the obtained places in level 3 of the DWT decomposition of the Region of Background (ROB). The compatibility of the embedded signatures and extracted watermarks is used to verify the content of ROI. Our simulations confirm improved fidelity and robustness

A New 2D Corner Detector for Extracting Landmarks from Brain MR Images

Point-based registration of images strongly depends on the extraction of suitable landmarks. Recently, various 2D operators have been proposed for the detection of corner points but most of them are not effective for medical images that need a high accuracy. In this paper we have proposed a new automatic corner detector based on the covariance between the small region of support around a central pixel and its rotated one. The main goal of this paper is medical images so we especially focus on extracting brain MR image’s control points which play an important role in accuracy of registration. This approach has been improved by refined localization through a differential edge intersection approach proposed by Karl Rohr. This method is robust to rotation, transition and scaling and in comparison with other grayscale methods has better results particularly for the brain MR images and also has acceptable robustness to distortion which is a common incident in brain surgeries. In the first part of this paper we describe the algorithm and in the second part we investigate the results of this algorithm on different MR images and its ability to detect corresponding points under elastic deformation and noise. It turns out that this method: 1)detect larger number of corresponding points that the other operators, 2)its performance on the basis of the statistical measures is better, and 3)by choosing a suitable region of support, it can significantly decrease the number of false detection