• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.268-272
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• 2003

Large medical images in PACS are compressed for saving storage space and improving network speed. The integrated compression environment was designed and developed for uniting of various compression methods. Various compression algorithm-RLE compression, lossless JEPG, JPEG, was built into it, complying with DICOM. A image compression using DWT was also implemented in it. And a unified algorithm of lossless compression and lossy compression was designed to improve images quality and to make compression ratios high. And integrated compression environment was operating together with a database program for efficient and user-friendly management.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.54-56
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• 1991

Many techniques have been used for medial image compression. Its first requirement is "Errorless" or, "Completely Reversible". Under this condition, we have increased the performance of compression using Space Filling Curve. This method converts source image in direction of Hilbert's curve path in order to ate average length longer thar in traditoinal horizental normal path. After that, we have applied RLC and reduced statistical redundancy by Huffman Coding. By this method, we have obtained more efficient results of RLC.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.131-134
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• 1988

In this paper, MIIS (Medical Image Information System) has been designed and implemented using INGRES RDBMS, which is based on a client/server architecture. The implemented system allows users to register and retrieve patient information, medical images and diagnostic reports. It also provides the function to display these information on workstation windows simultaneously by using the designed menu-driven graphic user interface. The medical image compression/decompression techniques are implemented and integrated into the medical image database system for the efficient data storage and the fast access through the network.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.29-33
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• 1992

We examine the compression performance of two irreversible (lossy) compression techniques, ADCT-VQ (Adaptive Discrete Cosine Trandform - Vector Quantization) and JPEG (Joint Photographic Experts group) which are basis of medical image information systems. Under the same compression ratio, MSE(Mean Square Error) is 0.578 lower in JPEG than in ADCT-VQ while SNR(Signal to Noise Ratio) is 1.236 dB higher in JPEG than in ADCT-VQ.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.836-838
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• 1999

This paper describes stereo image compression algorithm using disparity and JPEG. because similar images are images with common features, similiar pixel distributions, and similar edge distributions. Fields such as medical imaging or satellite imaging often need to store large collections of similar images. that is, a conventional stereo system with a single left-right pair needs twice data as a monoscopic imaging system. as a result we need compression method compatible stereo image, in this paper after we use JPEG in basic compression method and stereo matching using adaptiv window, we get disparity information, we restored right image using by restored left image and disparity.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2395-2398
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• 2003

This paper focuses on lossless medical image compression methods for medical images that operate on two-dimensional(2D) reversible integer wavelet transform. We offer an application of the Set Partitioning in Hierarchical Trees(SPIHT) algorithm [1][3][9] to medical images, using a 2D wavelet decomposition and a 2D spatial dependence tree. The wavelet decomposition is accomplished with integer wavelet filters implemented with the lifting method, where careful scaling and truncations keep the integer precision small and the transform unitary. We have tested our encoder on medical images using different integer filters. Results show that our algorithm with certain filters performs as well and sometimes better in lossless coding than previous coding systems using 2D integer wavelet transforms on medical images.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.31-38
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• 1994

This paper proposed signal decomposition and multiresolution representation through wavelet transform using wavelet orthonormal basis. And it suggested most appropriate filter for scaling function in multiresoltion representation and compared two compression method, arithmetic coding and Huffman coding. Results are as follows 1. Daub18 coefficient is most appropriate in computing time, energy compaction, image quality. 2. In case of image browsing that should be small in size and good for recognition, it is reasonable to decompose to 3 scale using pyramidal algorithm. 3. For the case of progressive transmittion where requires most grateful image reconstruction from least number of sampls or reconstruction at any target rate, I embedded the data in order of significance after scaling to 5 step. 4. Medical images such as information loss is fatal have to be compressed by lossless method. As a result from compressing 5 scaled data through arithmetic coding and Huffman coding, I obtained that arithmetic coding is better than huffman coding in processing time and compression ratio. And in case of arithmetic coding I could compress to 38% to original image data.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.184-187
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• 1997

Small PACS based on a personal computer or CR has initially been designed to improve the performance of cost-effective PACS implementation. In that system, Wavelet compression scheme is newly adopted to store images hierarchically to storage unit, and retrieve and display images progressively or display workstation. In this compression method, image is decomposed into subclasses of image by wavelet transform, and then the subclasses of image are vector quantized using a multiresolution codebook.

• Korean Journal of Digital Imaging in Medicine
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• v.6 no.1
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• pp.31-40
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• 2003

Purpose : In a PACS(Picture Archiving Communications System) environment, which is a very important component in a digital medical environment, the compression of digital medical images is a necessary and effective feature. In a current system where JPEG is applied to the compression of medical images, this study is to examine effectiveness and suitability when the JPEG2000, a more advanced compression algorithm for still images, is applied to the compression of medical images. In this thesis, we attempt to address the compressibility for effective clinical usage when compressing medical images, applying the objectivization of clinical evaluation as a function of compressibility. In the experiment al method, the compression was applied at a fixed rate using JPEG2000, and the n the result was compared with compressed images by JPEG. Method : For the performance evaluation, we choose SNR(Signal to Noise Ratio) measurement of an objective evaluation of definition and analyze a subjective evaluation by the ROC(Receiver Operating Characteristic) method. The results of the experiment showed that in the case of JPEG2000 there is hardly any distortion of images, even at high compression ratio(100:1), while regarding noise, the SNR remains around about 40dB, which is also relatively high. Before reading by reference to evaluative materials concerning objective compressed images, it is impossible to apply high compression to images : however, after reading, this can be applied to images that have already existed for some time.

• Journal of Biomedical Engineering Research
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• v.14 no.1
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• pp.9-16
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• 1993

For efficient storage and transmission of medical images, the requirement of image com pression is increasing. Because differences between reconstructed images and original images are related with errors In the diagnosis, lossless compression is generally preferred in mod- ical images. in Run Length Coding which is one of the lossless compression method, we have applied modified scanning direction based on the Hilbert curve, which is a kind of space fill ins curve. We have substituted the traditional raster scanning by Hilbert curve direction scanning. Using this method, we have studied enhancement of compression efficiency for medical images.