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Image fusion is used to enhance the quality of images by combining two images of same scene obtained from different techniques. In medical diagnosis by combining the images obtained by Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) we get more information and additional data from fused image. This paper presents a hybrid technique using curvelet and wavelet transform used in medical diagnosis. In this technique the image is segmented into bands using wavelet transform, the segmented image is then fused into sub bands using curvelet transform which breaks the bands into overlapping tiles and efficiently converting the curves in images using straight lines. These tiles are integrated together using inverse wavelet transform to produce a highly informative fused image. Wavelet based fusion extracts spatial details from high resolution bands but its limitation lies in the fusion of curved shapes. Therefore for better information and higher resolution on curved shapes we are blending wavelet transform with curvelet transform as we know that curvelet transform deals effectively with curves areas, corners and profiles. These two fusion techniques are extracted and then fused implementing hybrid image fusion algorithm, findings shows that fused image has minimum errors and present better quality results. The peak signal to noise ratio value for the hybrid method was higher in comparison to that of wavelet and curvelet transform fused images. Also we get improved statistics results in terms of Entropy, Peak signal to noise ratio, correlation coefficient, mutual information and edge association. This shows that the quality of fused image was better in case of hybrid method.

The proposed scheme embedded the watermark during the differential pulse code modulation process and extracted through decoding the entropy details. This technique utilize the moving picture expert groups standard

The human visual system exhibits reduced sensitivity to distortions in the regions of an image where the rate of change is significant. This entails that a watermark with significant value can be robust and if it resides near around edges and textured areas of an image, it would be imperceptible as well. The present work exploits this characteristic of the Human Visual system to embed a robust and imperceptible watermark in transform domain using edge detection. The embedding is done at block level in either the Discrete Hartley Transform (DHT) domain or in Discrete Cosine Transform (DCT) domain. The watermark is embedded block by block in different blocks of the image. The decision whether to embed in DHT domain or in DCT domain is based on the number of edges which exist in a given block in the image to be watermarked. Hence the threshold number of edges acts as a key in this algorithm and isused in the embedding as well in the extraction process of the watermark. The results demonstrate the robustness of scheme against common image processing operations like cropping, low pass filter, noise and lossy JPEG compression with various quality index factor. Results also illustrate that the watermark is perceptually transparent, recoverable, recognizable and robust even after the watermarked image has been passed through severe attacks.