This letter introduces a lossless reversible data hiding method. The original image is recovered without any distortion from the marked image following the extraction of the hidden data. The main algorithm is based on the relocation of zeros (or minima) and peaks of the histograms of the blocks of the original image to embed the data. It leads to the modification of the grey values of some pixels. It can embed more data than many of the existing reversible data hiding algorithms.The PSNR of the proposed method is better than many of the existing reversible data hiding techniques. Some of the huge experimental results are presented to prove its validity.

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In this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI's) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI's are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI's. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.