PURPOSE: To determine the usefulness of compression standard JPEG2000 for compression of mammographic images. MATERIALS AND METHODS: Image of a mammographic phantom was compressed using JPEG2000 at ratios of 10:1, 20:1, 30:1, 40:1, 50:1 and 60:1. The sizes of the images were compared, and scores were recorded by counting the numbers of fibers, groups of specks and masses seen in each phantom image. More than four fibers, three groups of specks and three masses and a total score of 10 were considered acceptable. RESULTS: The size of a DICOM image was 17,042 KB, a TIFF image was 8,324 KB, the original JPEG image was 1,506 KB and the most compressed image (50:1) above an acceptable total score of 10 was 43 KB. In each category, the compression image of fiber was acceptable up to compression ratio of 50:1 (score of 5), groups of specks was acceptable up to 60:1 (score of 3) and mass was acceptable up to 50:1 (score of 3.5). The total score, which was acquired by adding up the individual scores of all three categories, for a compression ratio of 50:1 was 12 and was acceptable, but the total score for 60:1 was 8 and was not acceptable. CONCLUSION: The compression standard JPEG2000 is an efficient means for compressing mammographic images at high ratios without compromising diagnostic value.
Data Compression*

PURPOSE: We wanted to evaluate an acceptable compression rate of JPEG2000 for long term archiving of CT and MR images in PACS. MATERIALS AND METHODS: Nine CT images and 9 MR images that had small or minimal lesions were randomly selected from the PACS at our institute. All the images are compressed with rates of 5:1, 10:1, 20:1, 40:1 and 80:1 by the JPEG2000 compression protocol. Pairs of original and compressed images were compared by 9 radiologists who were working independently. We designed a JPEG2000 viewing program for comparing two images on one monitor system for performing easy and quick evaluation. All the observers performed the comparison study twice on 5 mega pixel grey scale LCD monitors and 2 mega pixel color LCD monitors, rspectively. The PSNR (Peak Signal to Noise Ratio) values were calculated for making quantitative comparisons. RESULTS: On MR and CT, all the images with 5:1 compression images showed no difference from the original images by all 9 observers and only one observer could detect a image difference on one CT image for 10:1 compression on only the 5 mega pixel monitor. For the 20:1 compression rate, clinically significant image deterioration was found in 50% of the images on the 5M pixel monitor study, and in 30% of the images on the 2M pixel monitor. PSNR values larger than 44 dB were calculated for all the compressed images. CONCLUSION: The clinically acceptable image compression rate for long term archiving by the JPEG2000 compression protocol is 10:1 for MR and CT, and if this is applied to PACS, it would reduce the cost and responsibility of the system.
Data Compression ; Noise

PURPOSE: To evaluate the effect of lossy image compression on skeletal images and to determine the compression ratio which does not lead to difficulties when images are interpreted for diagnostic purposes. MATERIALS AND METHODS: Thirty-two computed radiographs (CR) of osteolytic bone tumors were obtained from Picture Archiving and Communication System. They were compressed to three different levels (Q factor 30, 70, 120) using the JPEG (Joint Photographic Expert Group) technique. Ninety-six pairs of uncompressed and compressed images were randomly ordered and then serially displayed on two high-resolution monitors. During a side-by-side review, three radiologists independently compared each pair of uncompressed and compressed images, and these were rated once using a five-category ordinal scale for tumor-related findings, linear structures, and soft tissues. The reviewers were then obliged to decide which image in each pair was of better quality, and finally, they were asked to evaluate the influence of image compression on diagnostic accuracy. RESULTS: The reviewers found no significant difference in image quality between uncompressed and compressed images with a Q factor 30. Compressed images with a Q factor of 70 or 120, however, revealed clinically relevant degradation. Among 96 observations of compressed images, 15 with a Q factor of 70 and 35 with a Q factor of 120 were considered inadequate for clinical purposes. CONCLUSION: If the JPEG technique is used, compressed CR skeletal images with a Q factor of 30 are acceptable for clinical application. Compressed images with a Q factor of 70 or 120 may, however, cause diagnostic difficulty and thus cannot be used for clinical purposes.
Data Compression ; Radiographic Image Enhancement

Recently many domestic hospitals computerize and automatize the process to manage their own medical data. These medical data should be shared among the departments in the hospital and be held in common in case of patients transfers or references to the remote hospitals. However, there is no simple method to share data among the management systems to be shared due to their heterogeneous platforms. Schema sharing with systems and standard format data transfer could be a solution. In this paper, we propose HIES(Hospital Information Exchange System), which uses XML as a standard medical data transfer method. HIES uses XML, which is currently recognized as the standard of electronic documents, for integrating and managing heterogeneous and distributed medical data. Information Sharing Manager to maintain consistent schema information for heterogeneous database systems in hospital systems as well as Image Compression/Partition Manager for efficient image data transfer and reemergence among the systems are implemented.
Data Compression ; Humans ; Information Dissemination

This paper focuses on lossless medical image compression methods for medical images that operate on three-dimensional(3-D) irreversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm to medical images, using a 3-D wavelet decomposition and a 3-D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling(square root 2) and truncations keep the integer precision and the transform unitary. We have tested our encoder on volumetric medical images using different integer filters and different coding unit sizes. The coding unit sizes of 16 slices save considerable dynamic memory(RAM) and coding delay from full sequence coding units used in previous works. Results show that, even with these small coding units, our algorithm with certain filters performs as well and better in lossless coding than previous coding systems using 3-D integer wavelet transforms on volumetric medical images.
Clinical Coding ; Data Compression* ; Lifting* ; Wavelet Analysis*

The compression of medical image series is very important in telemedicine. The motion estimation plays a key role in the video sequence compression. In this paper, an improved square-diamond search (SDS) algorithm is proposed for the motion estimation of medical image series. The improved SDS algorithm reduces the number of the searched points. This improved SDS algorithm is used in wavelet transformation field to estimate the motion of medical image series. A simulation experiment for digital subtraction angiography (DSA) is made. The experiment results show that the algorithm accuracy is higher than that of other algorithms in the motion estimation of medical image series.
Algorithms ; Angiography, Digital Subtraction ; Data Compression ; methods ; Fourier Analysis

A Fast Multi-resolution Volume Rendering Method (FMVRM) based on wavelet and Shear-Warp is herein proposed. In this method, the medical volume data is compressed using wavelet transformation first. Then based on the set resolution, the medical volume data is decompressed guided by Opacity transfer function (OTF). Finally, the 3D medical image is reconstructed on the basis of Shear-Warp using Block-based run length encoded (BRLE) data structure, in which, the aliasing artifacts resulting from under-sampling in Shear-Warp is avoided by the pre-integrated volume rendering technology. Experiments demonstrate the good performance of the proposed method.
Algorithms ; Artifacts ; Data Compression ; methods ; Humans ; Imaging, Three-Dimensional ; methods

Now researches of ECG data compression mainly focus on compressing the ECG data of low sampling rate. A BW-based high sampling rate ECG data lossless compression algorithm is proposed in this paper. We apply difference operation to the original ECG data first and take part of the 16-bit binary differential value as 8-bit binary. Then the differential results are coded with the move-to-front coding method in order to make the same characters centralizing in a certain area. Last, we gain a high compression ratio by using the arithmetic coding method further. Our experimental results indicate that this is an efficient lossless compression method suitable for body surface ECG data as well as for heart ECG data. The average compression ratios come up to 3.547 and 3.608, respectively. By comparison with current ECG compression algorithms, our algorithm has gained much improvement in terms of the compression ratio, especially when applied to the high sampling rate ECG data.
Algorithms ; Data Compression ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

In this paper is proposed a new method that approximates line segment with angle to control line as a basis for improving radial fitting. Experiments on selected records from the Massachusettes Institute of Technology and Boston's Beth Isral Hospital (MIT-BIH) arrhythmia database have revealed that the improved algorithm not only increases computation quantity, but also improves approximating quality and potentiates Real-time application of the linear approximation distance thresholding (LADT).
Algorithms ; Data Compression ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted

This paper introduces the implement method of DICOM medical image compression technology, The image part of DICOM files are extracted and converted to BMP format. The non-image information in DICOM file are stored into the text. When the final image of JPEG standard and non-image information are encapsulated to DICOM format images, it realizes the compression of medical image, which is beneficial to the image storage and transmission.
Data Compression ; methods ; Image Processing, Computer-Assisted ; methods ; Software