Pruned-tree structured vectored quantization (PTSVQ) was applied to the lower five gray scale remapped bits of normal and fatty ultrasound liver images. The upper bits were compressed reversibly. This combination of techniques is termed PTSVQ with splitting. The effect of the compression on the difference in texture between normal and fatty liver images was studied at different compression rates and distortions. The changes in texture were measured by changes in the principal components of the covariance matrix of image vectors. The vectors were the same size as those used in the compression technique. There were clear differences in the components of normal and fatty liver images. These differences were largely removed by the PTSVQ with splitting technique even at average single pixel distortions several times smaller than the image noise. These results suggest that the effect of compression on second order statistics should be measured when evaluating algorithms in addition to the first order average distortion.

One of the advantages that a picture archiving and communications system (PACS) is supposed to provide over a film-based operation is improved performance in retrieving images. Although it seems self-evident that this should be so, this experiment was intended to verify this and to provide some time comparisons for the two methods. The experiment consisted of randomly selecting ultrasound and computed tomography cases and determining how long it took to retrieve files at a PACS workstation or in person from the file room. To simulate actual retrieval volumes, a total of 40 cases from current to 6 months old, 20 cases from the past year, and 10 cases more than 1 year old was selected. Results indicate that PACS retrieval can indeed be faster than file room retrieval. However, the difference is less for recent cases than for older cases. For cases 6 or fewer months old, the workstation retrieval was approximately 2.5 minutes faster per case than the film file room. This time difference increased markedly when extended to the 1-year and older-than-1-year groups. This report details the results of this study and provides information about the reliability of the two archives.

Digital techniques are used more often than ever in a variety of fields. Medical information management is one of the largest digital technology applications. It is desirable to have both a large data storage resource and extremely fast data transmission channels for communication. On the other hand, it is also essential to compress these data into an efficient form for storage and transmission. A variety of data compression techniques have been developed to tackle a diversity of situations. A digital value decomposition method using a splitting and remapping method has recently been proposed for image data compression. This method attempts to employ an error-free compression for one part of the digital value containing highly significant value and uses another method for the second part of the digital value. We have reported that the effect of this method is substantial for the vector quantization and other spatial encoding techniques. In conjunction with DCT type coding, however, the splitting method only showed a limited improvement when compared to the nonsplitting method. With the latter approach, we used a nonoptimized method for the images possessing only the top three-most-significant- bit value (3MSBV) and produced a compression ratio of approximately 10:1. Since the 3MSB images are highly correlated and the same values tend to aggregate together, the use of area or contour coding was investigated. In our experiment, we obtained an average error-free compression ratio of 30:1 and 12:1 for 3MSB and 4MSB images, respectively, with the alternate value contour coding. With this technique, we clearly verified that the splitting method is superior to the nonsplitting method for finely digitized radiographs.

A 10-bit or 12-bit gray scale is provided in commercial laser film digitizers. The true contrast resolution on the digitized image within a laser spot area of 200 μm in diameter is limited by both the quantum mottle and instrumentation noise. In this report, we investigated the mean value, standard deviation, and adjacent pixel correlation coefficient on a calibrated step wedge film with two laser digitizers. The results were disappointing, because we found that the evaluated contrast information is inferior to the manufacturers' specifications. On the output side, the brightnesses of different gray levels from a clinical monitor were measured with a narrow angle luminance probe and evaluated by a brief human perception study. In addition, the implications for teleradiology applications are discussed.

One area of PACS research at the Georgetown University Medical Center, Department of Radiology, relates to the user interface of the PACS workstations. Other research areas include network simulation, data compression, digital radiology, image processing, and teaching workstation development. The major focus of this paper is a presentation of a PACS user interface issues, including what should be considered when designing a man- machine interface for a PACS workstation. A brief discussion of PACS requirements is presented, followed by the advantages and disadvantages of various interaction techniques and devices. Next, a set of requirements for a PACS user interface prototyping environment is specified. Finally, the hardware and software components of the Georgetown PACS research environment are presented. Both those components which are applicable to user interface prototyping and those used for other PACS-related projects are discussed.

A 10-12 bit gray scale is provided in commercial laser film digitizers. The true contrast resolution on the digitized image within 200 microns square is limited by both the quantum mottle and instrumentation noise. In this paper, we investigate that mean value, standard deviation, and adjacent pixel correlation coefficient on a calibrated step wedge film with two laser digitizers. The results were disappointing. On the output side, the brightness of different grey levels from a clinical monitor was measured with a narrow angle luminance probe. In addition, the imlipcations for teleradiology applications are discussed in this paper.

A compression server in a PACS environment has to deal with images of different types and sizes. The images will flow into compression at different rates, ranging from 1-2 Mbytes/sec to 9.6 Kbytes/sec. Additionally, the pattern of the flow can vary. Some images will enter compression in one block. Other images, especially large images (i.e., digitized film images) will enter compression as a series of blocks. Interleaving of blocks can also occur. For example, the first two blocks from a digitized X-ray film may enter compression followed by a block from a CT image followed by more blocks from the X-ray film. In order to process incoming images rapidly, the compression service must compress blocks as they arrive. The compression of a CT image should not have to wait for the final arrival of a slowly transmitted large digital X-ray image. On the other hand, temporarily buffering large images or adding extra compression hardware may make the compression service too expensive. These PACS network considerations argue for compression that operates on images locally. That is, the compression algorithm should not have to know the statistics of the entire image to be effective. Any transforms should operate on local blocks within the image independent of the results on antecedent or subsequent blocks. In addition, since the network may present relatively small blocks to compression (as small as 64 Kbytes), the compression technique should not add a large amount of overhead to the compressed data in the form of tables, descriptors, and so forth. In this paper, the effects of breaking data into fragments was tested using a simulation tool dubbed PAW (Performance Analysis Workstation.) Two cases were considered. In the first case, large images were compressed in their entirety. In the second case, large images were broken into fragments and the fragments were compressed separately. The results show that processing the data in fragments is desirable.

On a PACS network, it is desirable to use a non-destructive image compression technique in order both to minimize the storage and to improve the transmission speed. However, if the effect of noise in images is not taken into account, the expected degree of compression may not be achieved. We have studied some radiological images with different levels of noise using various decomposition methods incorporated with Huffman and Lempel-Ziv coding. When more correlations exist between pixels, these techniques can be made more efficiently. On the other hand, the additional noise disrupts the correlation between adjacent pixels and leads to a less compressed result. Hence, prior to a systematic compression in a PAC system two main issues need to be addressed: a) the true information range which exists in a specific type of radiological image and b) the costs and benefits of compression for the PACS.