Wavelet, subband, and block transforms in communications and multimedia

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Abbate, J. Frankel, R. Reed, and P. Das, Ultrasonic gauging and wavelet image processing for wear and erosion mapping, Proc. Google Scholar. Das, Wavelet image processing for wear and erosion mapping using an ultrasonic gauging technique, Proc. Frankel, and P.

Das, Application of wavelet image processing for ultrasonic gaging, Proc. Akansu and R. Akansu, P. Duhamel, X. Lin, and M. Signal Process. CrossRef Google Scholar. Akansu and M. Medley eds. Baraniecki and S. Karim, Computational algorithms for discrete wavelet transforms, Proc.

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Das and C. DeCusatis, A.

Abbate, and P. Das, Wavelet transform based image processing using acousto-optics correlators, Proc. Das, Scale and rotation invariant pattern recognition using a wavelet image processor, Annual Meeting of the Optical Society of America , Abbate, D. Litynski, and P. Das, Wavelet image processing for optical pattern recognition and feature extraction, Proc.

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Wavelet, Subband and Block Transforms in Communications and Multimedia

Inbunden Engelska, Spara som favorit. Skickas inom vardagar. Laddas ned direkt. Sixth in the book series, Advances in Image Communication , which documents the rapid advancements of recent years in image communication technologies, this volume provides a comprehensive exploration of subband coding.

Originally, subband coding and transform coding were developed separately. The former, however, benefitted considerably from the earlier evolution of transform coding theory and practice. Retaining their own terminology and views, the two methods are closely related and this book indeed aims to unify the approaches. Specifically, the volume contributes effectively to the understanding of frequency domain coding techniques. Many images from coding experiments are presented, enabling the reader to consider the properties of different coders.

The result shows that the watermark is retrieved successfully without any errors, the NC value is 1. The four types of medical images are subject to different attacks to measure the robustness of the watermarking algorithm.

Sub-Band Coding - Discrete Time Signal Processing

Analysis of the results shows that our proposed scheme has high degree of robustness to sharpen and automatic equalization attacks. However, with variance of 0. The watermark can be recovered with no BER for value 2 while for value 0. On the other side, the patient information is correctly recovered without any error in the case of contrast attack by factor Before extracting the watermark, the image is re-rotated to its original position. In this paper, a new blind watermarking scheme has been introduced and developed to embed patient information in a private and secure manner.

The proposed scheme satisfies the security of medical patient information and allows sharing the medical information remotely and manageably without extra cost or storage space and without any effect on medical image quality. We examined the security of the scheme by applying some attacks and measuring the robustness of the scheme by BER and NC and examining the visual quality of medical image by PSNR.

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  • Experimental results show that the proposed scheme is robust against common attacks such as Gaussian noise, gamma correction, automatic equalization, contrast adjustment, rotation, and sharpening, median filter and JPEG compression with different QF. Also it has been noticed that the Radiological image is the most robust type of medical image against attacks. Although the scheme resists most attacks, there is some none satisfactory performance in the area of median filtering and contrast adjustment by factor In the future research, we will try to enhance our algorithm in order to obtain watermarked medical images with less degradation and to have recovered watermark with better accuracy.

    National Center for Biotechnology Information , U. Open Biomed Eng J.

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    Published online Apr Salwa A. Tolba , 3 F. Abdelkader , 1 and Hisham M. Elhindy 1. Hisham M. Author information Article notes Copyright and License information Disclaimer. Abstract The last decade has witnessed an explosive use of medical images and Electronics Patient Record EPR in the healthcare sector for facilitating the sharing of patient information and exchange between networked hospitals and healthcare centers. Open in a separate window. Watermark Generation As shown in Fig.

    Akansu, Ali N. 1958-

    Watermark Embedding Method The main steps for watermark embedding as shown in Fig. Step 3 Prepare a greyscale reference image R whose size is equal to nxn size of sub-band. Step 4 One bit of the watermark is embedded per block. Step 5 The process in step 4 is repeated up to the length of the watermark bitstream.

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    Step 2 Choose the same two sub-bands with middle energy used in the watermark embedding and divide them into 4x4 blocks b r,k. Step 3 Use the greyscale reference image R with size nxn. Step 5 Apply the steps of the watermark generation section in reverse order to obtain the patient information. Table 2 Gaussian Noise Attack. Table 3 Sharpen Attack. Table 8 Automatic Equalization Attack. Table 4 Median Filter Attack.

    Table 5 Gamma Correction Attack. Table 6 Contrast Attack. Table 7 Rotate Attack.