The objective of radiologic image compression is to reduce the data volume of and to achieve a low bit rate in the digital representation of radiologic images without perceived loss of image quality. However, the demand for transmission bandwidth and storage space in the digital radiology environment, especially picture archiving and communication systems (PACS) and teleradiology, and the proliferating use of various imaging modalities, such as magnetic resonance imaging, computed tomography, ultrasonography, nuclear medicine, computed radiography, and digital subtraction angiography, continue to outstrip the capabilities of existing technologies. The availability of lossy coding techniques for clinical diagnoses further implicates many complex legal and regulatory issues. This paper reviews the recent progress of lossless and lossy radiologic image compression and presents the legal challenges of using lossy compression of medical records. To do so, we first describe the fundamental concepts of radiologic imaging and digitization. Then, we examine current compression technology in the field of medical imaging and discuss important regulatory policies and legal questions facing the use of compression in this field. We conclude with a summary of future challenges and research directions. >

Radiologic image compression-a review

The paper proposes a scheme to perform volume rendering from compressed scalar data. Instead of decompressing the entire data set before rendering, blocks of data are decompressed as needed. Discrete cosine transform based compression technique is used to illustrate the method. The data is partitioned into overlapping blocks to permit local rendering and allow easy parallelization. Compression by factor of 20 to 30 produces rendering virtually indistinguishable from rendering using the original uncompressed data. Speedup is obtained by making use of spatial homogeneity detected in the transform domain. Rendering time using the proposed approach is less than that of direct rendering from the entire uncompressed data. The proposed method thus offers an attractive option to reduce storage, computation, and transmission overhead of otherwise huge data sets. >

Volume rendering of DCT-based compressed 3D scalar data

This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB, In general, the smaller the slice distance, the better the 3-D compression performance.

Medical image compression by using three-dimensional wavelet transformation

Displacement estimated interframe (DEI) coding, a coding scheme for 3-D medical image data sets such as X-ray computed tomography (CT) or magnetic resonance (MR) images, is presented. To take advantage of the correlation between contiguous slices, a displacement-compensated difference image based on the previous image is encoded. The best fitting distribution functions for the discrete cosine transform (DCT) coefficients obtained from displacement compensated difference images are determined and used in allocating bits and optimizing quantizers for the coefficients. The DEI scheme is compared with 2-D block discrete cosine transform (DCT) as well as a full-frame DCT using the bit allocation technique of S. Lo and H.K. Huang (1985). For X-ray CT head images, the present bit allocation and quantizer design, using an appropriate distribution model, resulted in a 13-dB improvement in the SNR compared to the full-frame DCT using the bit allocation technique. For an image set with 5-mm slice thickness, the DEI method gave about 5% improvement in the compression ratio on average and less blockiness at the same distortion. The performance gain increases to about 10% when the slice thickness decreases to 3 mm. >

Statistical distributions of DCT coefficients and their application to an interframe compression algorithm for 3-D medical images

We propose a fully three-dimensional (3-D) wavelet-based coding system featuring 3-D encoding/two-dimensional (2-D) decoding functionalities. A fully 3-D transform is combined with context adaptive arithmetic coding; 2-D decoding is enabled by encoding every 2-D subband image independently. The system allows a finely graded up to lossless quality scalability on any 2-D image of the dataset. Fast access to 2-D images is obtained by decoding only the corresponding information thus avoiding the reconstruction of the entire volume. The performance has been evaluated on a set of volumetric data and compared to that provided by other 3-D as well as 2-D coding systems. Results show a substantial improvement in coding efficiency (up to 33%) on volumes featuring good correlation properties along the z axis. Even though we did not address the complexity issue, we expect a decoding time of the order of one second/image after optimization. In summary, the proposed 3-D/2-D multidimensional layered zero coding system provides the improvement in compression efficiency attainable with 3-D systems without sacrificing the effectiveness in accessing the single images characteristic of 2-D ones.

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Three-dimensional encoding/two-dimensional decoding of medical data

We performed volume compression on CT and MR data sets, each consisting of 256 X 256 X 64 or 32 images, using three-dimensional (3D) DCT followed by quantization, adaptive bit-allocation, and Huffman encoding. Cuberille based surface rendering and oblique angle slicing was performed on the reconstructed compression data using a multi-stream vector processor. For CT images 3D-DCT was found to be successful in exploiting the additional degree of voxel correlations between image frames, resulting in compression efficiency greater than 2D-DCT of individual images. During rendering operations, a substantial amount of thresholding, resampling, and filtering operations are performed on the data. At compression ratios in the range 6 - 15:1, 3D compression was not found to have any adverse visual impact on rendered output. Of these two methods, oblique angle slicing, which involves the fewest operations was found to be the most demanding of small compression errors. We conclude that 3D-DCT compression is a viable technique for efficiently reducing the size of data volumes which must be analyzed with various rendering methods.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Visualization and volumetric compression

Transform based compression methods achieve their effect by taking advantage of the correlations between adjacent pLtels in an image. The increasing use of three-dimensional imaging studies in radiology requires new techniques for image compression. For time-sequenced studies such as digital subtraction angiography, pixels are correlated between images, as well as within an image. By using three-dimensional cosine transforms, correlations in time as well as space can be exploited for image compression. Sequences of up to eight 512 x 512 x 8-bit images were compressed using a single full volume three-dimensional cosine transform, followed by quantization and bit-allocation. The quantization process is a uniform thresholding type and an adaptive three-dimensional bit-allocation table is used. The resultant image fidelity vs. compression ratio was shown to be superior to that achieved by compressing each image individually.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Christina C. Lau

Papers

Visualization and volumetric compression

Three-dimensional transform compression of images from dynamic studies