A review of compression methods for medical images in PACS

Transcription

1 International Journal of Medical Informatics 52 (1998) A review of compression methods for medical images in PACS Michael G. Strintzis * Department of Electrical and Computer Engineering, Aristotle Uni ersity of Thessaloniki, Thessaloniki, Greece Abstract As an introduction to Section C2 (medical imaging) of track C (Images and PACS) of MIE 97, an appropriate and timely topic concerns the coding for the transmission of medical images in PACS. Speed limitations of existing networks along with the explosive growth of image modalities with extremely high volume outputs have combined to make the issue of medical image coding one of the key considerations in the design of future PACS systems. Both lossless and lossy compression schemes are reviewed and compared, and the compression demands are presented of the main digital medical image modalities Elsevier Science Ireland Ltd. All rights reserved. Keywords: Medical Imaging; Lossless compresion; PACS compression 1. Introduction Medical services today rely heavily on imaging technology, including X-ray computed tomography, magnetic resonance imaging ultrasound and nuclear medicine examinations. As these examinations become more and more common and more medical equipment, i.e. ultrasound in hospitals, the volume of acquired data increases to the point of becoming excessive. Picture archiving and communication systems (PACS) have been proposed as tools for converting the * Tel.: ; fax: ; strintzi@eng.auth.gr data in digital form and for handling the resulting amount of information. In a typical large hospital it is estimated that the annual volume of image data is over one million images requiring more than two terabytes of storage capacity. Thus, PACS is designed so as to accommodate the transfer of huge amounts The design of a PACS for the next decade must meet the following functional specifications: It must accommodate the variety of present-day image archiving and communication needs of digital image formation methods in radiology (CT, DSA, MRI, MRA, computed radiography) and nu /98/$ - see front matter 1998 Elsevier Science Ireland Ltd. All rights reserved. PII S (98)00135-X

2 160 M.G. Strintzis / International Journal of Medical Informatics 52 (1998) clear medicine (planar scientigraphy, SPECT, PET). It must be prepared to adapt to new imaging needs, particularly the need for 3D medical imagery. In fact, the technological coming of age of 3D medical visualization and its growing acceptance as a tool for diagnosis is expected to dramatically alter the volume of medical data that need to be routinely displayed in a hospital. This volume is so large that even the most simple transactions require extraordinarily powerful compression techniques to approach execution in real time. The system must accommodate the needs of image transmission where the receiver is the more powerful workstation. For example, it must be able to efficiently transmit a full 3D data volume from a radiology department workstation to a diagnostic workstation. In this case, the receiving workstation will be powerful enough to perform, if needed, the actual visualization. The system must also cater to the needs of a physician at a different place in the hospital, with only a simple personal computer at his disposal. Therefore, it must communicate efficiently images visualized at the transmitter site and viewed interactively using a simple PC or even a simple high-resolution monitor. Finally, the system must accommodate the growing need for registration of many imaging modalities and also fusion of imaging modalities with other data useful for diagnostic purposes. The transmission of large stores of data through limited capacity channels in limited time is the common characteristic of the above requirements. Thus, efficient compression and coding is a basic goal of the design of a PACS. 2. State of the art: medical image coding and compression The field of image data compression is experiencing a vivid interest from the medical imaging community. This interest has been aroused by the growing impact of digital image formation methods in radiology (CT, DSA, MRI, MRA, computed radiography) and nuclear medicine (planar scientigraphy, SPECT, PET). Film-based methods are slowly but surely losing ground in both image formation and image archiving. In fact, a filmless nuclear medicine department has already been reported as being viable [1,6]. The aim of image data compression methods is to represent images efficiently, i.e. at low bit rates. This achievement would allow data storage at relatively low cost and data transmission at relatively high speed, both of which are essential in the design of picture archiving and communication systems. In addition to reducing the costs of disk space, efficient storage increases the on-line availability of patient data, which is particularly useful in a fully digital radiological or nuclear medicine image system. Furthermore, in some acquisition systems, notably those involving temporal image sequences such as digital angiography, the flow of image data is so large that compression is required for adequate disk access [2 4]. Image compression comes in two forms: (1) irreversible, or lossy, or information reducing compression; and (2) reversible, of lossless, or error-free, or information preserving compression. Compression methods are in the second class only if the original data can be exactly reconstructed from the compressed representation. Reversible compression is often preferred for the following reasons: Legal safeguards or regulation. Whether or not mandated by standing law, it may

3 M.G. Strintzis / International Journal of Medical Informatics 52 (1998) be advisable for legal reasons to keep records of all original patient data for a number of years. In fact, it may be inadvisable to dispense with any-even seemingly useless-information contained in the images. Postprocessing. Irreversible compression methods are capable of representing images at considerably reduced storage costs with hardly perceivable degradation. However, the losses thus introduced may be greatly enhanced by postprocessing operations (e.g. contour detection) applied to the images. An additional advantage of lossless compression methods is that a comparison of their performance can be made solely on the basis of their efficiency, as contrasted with lossy compression methods, which require a subjective evaluation to indicate whether or not the coding losses are acceptable. However, some needs of medical imaging are also well served with nonreversible compression. While for example the region of diagnostic interest (ROI) must out of necessity be coded reversibly, the remainder of the image may be coded nonreversibly if this produces no visible deterioration of the image. It must be noted that while reversible or lossless compression has an upper limit of 4:1 ratio, medical image compression as high as 20:1 is routinely possible with nonreversible or lossy compression, without visible image degradation. As mentioned, often the flow of image data is so large that nonreversible compression may be needed simply to digitally retrieve the data in real time. Thus, a number of extremely powerful digital diagnostic techniques based on simultaneous displays (especially of moving images) such as registration and fusion, may be inefficient unless realized in part with nonreversible compression Methods used exclusi ely for re ersible (lossless) compression Information preserving methods can achieve image compression by exploiting the usually high degree of correlation between neighbouring pixels. In the majority of methods, the pixel values are first decorrelated, after which the resulting data are encoded using a variable length coder, e.g. Huffman coding or arithmetic coding. In other methods (Lempel Ziv coding), the decorrelation and the variable length coding are combined. Huffman coding is a variable length coder in which the length of a code word is based on the probability of the symbol (i.e. decorrelated image value). This method is optimal, given the severe constraint that an integral number of bits be assigned to each symbol. The bit rate has a lower bound of 1 bit/pixel. Arithmetic coding [8 10] represents the decorrelated image values as a whole by one realvalued number between 0 and 1. Each symbol is allotted a subinterval of [0,1] with a size proportional to its probability. The first symbol thus comprises a subinterval of this subinterval, etc. Each symbol coded narrows the interval, until the final intervals is obtained, which represents the symbol set uniquely. Any number within this interval may be used for storage or transmission. Lempel Ziv coding maps substrings of input symbols into fixed length output codes. It detects high usage patterns and symbol repetition, rather than symbol frequency distribution. Consequently, Lempel Ziv coding is particularly suited for low entropy signals, which have a high probability of containing high usage patterns. This method is occasionally useful for the coding of some nuclear medicine images. Several refinements and improvements of these basic techniques have been reported in the literature, such as adaptive arithmetic

4 162 M.G. Strintzis / International Journal of Medical Informatics 52 (1998) coding and other improved variations of the original algorithms. The last two years in particular have seen a tremendous new wave of interest in lossless coding. Specifically, two categories of techniques are intensively explored. (a) Context-based techniques. Prime examples of these techniques are those presented in [13 18] (b) Predictive decorrelation and interpolatory decorrelation. In this category belong a great many extremely successful and popular methods which, apart from achieving high lossless compression, have the additional advantage to be able to produce progressive image coding where a lossy version of the image is sent first, with precision gradually increasing until the desired quality emerges. In this way, it is possible to preview or search very fast a large image data base, and receive a lossless copy of an image only if it is the desired one. The well known HINT technique [1,12] is the classical interpolative method used for lossless and progressive image coding. New and very efficient techniques have been developed in [11,12,19 22,24,25,27,29, 32 36,40 46]. The predictive S +P algorithm in [11] and the interpolative reduced pyramid method in [45] are currently the most efficient methods for progressive and lossless image coding Methods for either lossless or lossy compression A number of compression methods may be used for either lossy or lossless compression. These methods are generally based on decorrelation techniques which reduce each image or image sequence to its absolutely essential ingredients, by removing any excess information. In all of these methods, the error image is always sent along with the compressed image. Thus in theory at least, if the error is transmitted losslessly, the overall image transmission is lossless as well. In practice, the performance of the lossless version of these methods is poor. However, as mentioned earlier, such methods may be useful for the transmission of these portions of the medical image which show no diagnostically interesting information. Decorrelation methods include: (A) Decorrelation of still 2D or 3D images [7]. These may involve: A1. Transform decorrelation (Karhunen- Loeve (KL), Discrete Fourier Transform (DFT), Discrete Hartley Transform (DHT), Discrete Cosine Transform (DCT). A2. Multiresolution decorrelation using the Laplacian or more efficient [5,32] pyramidal and wavelet structures [30,31]. The well known Joint Photographic Experts Group (JPEG) international compression standard prescribes the use of variable-neighbourhood DPCM for lossless coding and DCT followed by Huffman coding for lossy coding of still images. B. Interframe decorrelation of 2D or 3D image sequences Motion estimation and compensation may be used to effect reduction of the temporal as well as the spatial correlation of a sequence of 2D or 3D images. This is achieved by B1. Classical block-matching or pixelbased motion compensation [7,23,26]. In these techniques the compensation is based on a block-by-block or pixel-by-pixel identification of the moving parts of a scene, and coding of motion vectors along with the reference image. B2. Object based coding techniques [28,39]. In this rapidly evolving coding methodology, objects rather than pixels are identified and matched in the image sequence. The resulting compression is far higher than in the preceding category of techniques using only block or pixel matching.

5 Table 1 Medical image sizes [22] M.G. Strintzis / International Journal of Medical Informatics 52 (1998) Modality (acronym) Image dimension (pixels) Gray level (bits) Aug. size/exame (Mb) Positron emission tomography (PCT) Single photon emission Computed tomography (SPECT) Magnetic resonance imaging (MRI) Ultrasound (US) Computed tomography Spiral or helical CT Digitised colour microscopy (DCM) Varies Digital subtraction or Angiography (DSA) (per run) Digitised X-rays Computed radiography Digitised mammography (four images) B3. Model-based coding techniques [37,38]. These techniques have been used with success in videophone applications. They use 2D or 3D graphical models of the scene. In videophone applications, these models represent mainly the head of the correspondent. In medical applications, models should be made of the organ images to be transmitted. Very high compression has been claimed with this methodology as well. B4. Subband coding techniques (3D or 4D form) [30,31]. In these, the whole of the image sequence is transmitted in one package using frequency band-splitting to effect compression. The error images which are also transmitted may be coded losslessly as noted. For lossy transmission, discrete cosine transform (DCT) techniques are commonly used for the coding of error images. Alternatives include principal component analysis (PCA), neural network techniques and wavelet coding techniques [30]. The well known Motion Picture Expert Group (MPEG) group of compression standards has prescribed at present the techniques discussed mainly in B1 above (MPEG1, MPEG2). It is expected to also include methods B2 and B3 in the near future (MPEG4). 3. Conclusions A review was made of the methods currently in use and those proposed in the current literature for the compression and coding of medical images and image sequences in PACS. As noted, the search for efficient lossless compression algorithms has recently intensified. However, as noted in Table 1, the amounts of medical image information that must be processed are quite huge and the task of transmitting them through the PACS system at an acceptably fast rate remains formidable. Acknowledgements This work was based on projects supported by the Greek General Secretariat of Research and Technology projects NIKA and IHIS.

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