Medical image compression with set partitioning in hierarchical trees

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.

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

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691318500285 ◽

2018 ◽

Vol 16 (04) ◽

pp. 1850028 ◽

Cited By ~ 2

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.

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Hybrid medical image compression method using quincunx wavelet and geometric actif contour

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i1.1675 ◽

2020 ◽

Vol 9 (1) ◽

pp. 146-159

Author(s):

Haouam Imane ◽

Beladgham Mohammed ◽

Bouida Ahmed

Keyword(s):

Image Compression ◽

Medical Image ◽

Active Contour Model ◽

Region Of Interest ◽

Set Partitioning ◽

Optimal Method ◽

Medical Image Compression ◽

Information Characteristics ◽

Spiht Algorithm ◽

Geometric Active Contour

The purpose of this article is to find an efficient and optimal method of compression by reducing the file size while retaining the information for a good quality processing and to produce credible pathological reports, based on the extraction of the information characteristics contained in medical images. In this article, we proposed a novel medical image compression that combines geometric active contour model and quincunx wavelet transform. In this method it is necessary to localize the region of interest, where we tried to localize all the part that contain the pathological, using the level set for an optimal reduction, then we use the quincunx wavelet coupled with the set partitioning in hierarchical trees (SPIHT) algorithm. After testing several algorithms we noticed that the proposed method gives satisfactory results. The comparison of the experimental results is based on parameters of evaluation.

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