Trends in Medical Image Compression

Abstract

This paper presents an overview on the state-of-the-art in the field of medical image coding. After a summary of the more representative 2D and 3D compression algorithms, a versatile model-based coding scheme for threedimensional medical data is introduced. The potential of the proposed system is in the fact that it copes with many of the requirements characteristic of the medical imaging field without sacrificing compression efficiency. Among the most interesting features are progressively refinable up-to-lossless quality of the decoded information, object-based functionalities and the possibility to decode a single 2D image of the dataset. Furthermore, such features can be combined enabling a swift access to any two-dimensional object of any image of interest with refinable quality. The price to pay is a slight degradation of the compression performance. Though, the possibility to focus the decoding process on a specific region of a certain 2D image allows a very efficient access to the information of interest, which can be recovered to the desired up-to lossless quality. This is believed to be an important feature for a coding system meant to be used for medical applications, which largely compensates for the eventual loss in compression that could be implied.

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

“…There are many additional advantages of decomposition into subbands for lossless and lossy compression, for instance, it allows for various kinds of progressive coding. For brevity, like in the previous works [9,10,12], below we describe only the lifting-based reversible DWT variant with Cohen-Daubechies-Feauveau (5,3) wavelet filter that is exploited in the lossless JP3D, reduced to essentials. For further details and more general characteristics of different variants of DWT, their implementation in JP3D/JPEG 2000 standards, and these standards, the reader is referred to [4][5][6][7][8]17].…”

“…Lossless compression algorithms allow for significantly reducing the size of image files, to the same extent decreasing the demand of PACSs for mass storage capacity and transmission media bandwidth. Although much better compression ratios may be obtained by lossy algorithms, the use of lossy compression for medical images is disputable, guidelines that are issued by various professional bodies recommend different lossy compression ratios, and in some cases lossy compression of such images is forbidden by the law [ 1 , 2 , 3 ].…”

The Current Role of Image Compression Standards in Medical Imaging

“…However, in this paper, we limit our discussion to medical images). It is important to note that there have been earlier reviews of medical image compression techniques [5][6][7][8][9][10][11][12][13][14]. In this paper, we focus on the image compression standards including the more recent standards that have not been considered in these earlier reviews.…”

Symmetry based perceptually lossless compression of 3D medical images in spatial domain

“…Currently these imaging modalities have become part of standard health care, but they also increase the amount of digital images generated every day in the clinical environment [1], [2]. In present practice, Picture Archiving and Communication Systems (PACS), which contain collection of dedicated networks and computational resources are frequently used for economical storage, convenient retrieval, distribution and visualization of medical images from multiple modalities.…”