Secured medical image compression using DES encryption technique in Bandelet multiscale transform

2018 ◽  
Vol 16 (04) ◽  
pp. 1850028 ◽  
Author(s):  
S. P. Raja
Keyword(s):  
Performance Evaluation ◽  
Image Compression ◽  
Data Storage ◽  
Medical Image ◽  
Mean Squared Error ◽  
Similarity Index ◽  
Data Encryption ◽  
Unequal Error Protection ◽  
Set Partitioning ◽  
Medical Image Compression

An efficient and secure platform is needed for data storage and transmission, specially for the multimedia content like audio, image and video. In this paper, a new methodology is proposed for the secured medical image compression. Initially, Bandelet transform is applied to the input medical image to obtain the coefficients. Using unequal error protection (UEP) method, the obtained coefficients are prioritized. These coefficients are encrypted using data encryption standard (DES) and will be encoded using listless set partitioning embedded block (Listless SPECK) technique. For the performance evaluation, peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM), image quality index (IQI), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE) are taken. From the experimental results and performance evaluation, it is shown that the proposed approach is producing good results.

Multiscale transform-based secured joint efficient medical image compression-encryption using symmetric key cryptography and ebcot encoding technique

2019 ◽  
Vol 17 (05) ◽  
pp. 1950034 ◽  
Author(s):  
S. P. Raja
Keyword(s):  
Image Compression ◽  
Medical Image ◽  
Mean Squared Error ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Data Encryption ◽  
Security And Privacy ◽  
Multimedia Content ◽  
Medical Image Compression ◽  
Symmetric Key

Due to the huge advancement in technology, digitizing the multimedia content like text, images and videos has become easier. Everyday huge amounts of multimedia content are shared through the social networks using internet. Sometimes this multimedia content can be hacked by the hackers. This will lead to the misuse of the data. On the other hand, the medical content needs high security and privacy. Motivated by this, joint secured medical image compression–encryption mechanisms are proposed in this paper using multiscale transforms and symmetric key encryption techniques. The multiscale transforms involved in this paper are wavelet transform, bandelet transform and curvelet transform. The encryption techniques involved in this paper are international data encryption algorithm (IDEA), Rivest Cipher (RC5) and Blowfish. The encoding technique used in this paper is embedded block coding with truncation (EBCOT). Experimental results are done for the proposed works and evaluated by using various parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Image Quality Index (IQI) and Structural Similarity Index (SSIM), Average Difference (AD), Normalized Cross-correlation (NK), Structural Content (SC), Maximum difference (MD), Laplacian Mean Squared Error (LMSE) and Normalized Absolute Error (NAE). It is justified that the proposed approaches in this paper yield good results.

scholarly journals A TWO COMPONENT MEDICAL IMAGE COMPRESSION TECHNIQUES FOR DICOM IMAGES

2013 ◽  
pp. 226-229
Author(s):  
K. S. SELVANAYAKI
Keyword(s):  
Image Compression ◽  
Digital Imaging ◽  
Medical Image ◽  
High Speed ◽  
Medical Images ◽  
Set Partitioning ◽  
The Body ◽  
Compression Technique ◽  
Medical Image Compression ◽  
Two Component

To meet the demand for high speed transmission of image, efficient image storage, remote treatment an efficient image compression technique is essential. Wavelet theory has great potential in medical image compression. Most of the commercial medical image viewers do not provide scalability in image compression. This paper discusses a medical application that contains a viewer for digital imaging and communications in medicine (DICOM) images as a core module. Progressive transmission of medical images through internet has emerged as a promising protocol for teleradiology applications. The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Recent image compression techniques have increased the viability by reducing the bandwidth requirement and cost-effective delivery of medical images for primary diagnosis. This paper presents an effective algorithm to compress and reconstruct Digital Imaging and Communications in Medicine (DICOM) images. DICOM is a standard for handling, storing, printing and transmitting information in medical imaging. These medical images are volumetric consisting of a series of sequences of slices through a given part of the body. DICOM image is first decomposed by Haar Wavelet Decomposition Method. The wavelet coefficients are encoded using Set Partitioning in Hierarchical Trees (SPIHT) algorithm. Discrete Cosine Transform (DCT) is performed on the images and the coefficients are JPEG coded. The quality of the compressed image by different method are compared and the method exhibiting highest Peak Signal to Noise Ratio (PSNR) is retained for the image. The performance of the compression of medical images using the above said technique is studied with the two component medical image compression techniques.

Wavelet-Based Image Compression Encoding Techniques — A Complete Performance Analysis

2020 ◽  
Vol 20 (02) ◽  
pp. 2050008
Author(s):  
S. P. Raja
Keyword(s):  
Image Compression ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Set Partitioning ◽  
Complete Analysis ◽  
Structural Content ◽  
Image Quality Index

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).

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