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High-Dimension Lossless Medical Image Compression System
|關鍵字:||medical image;醫學影像;medical image compression;image compression;linear predictor;醫學影壓縮;影像壓縮;線性預測||出版社:||電機工程學系所||引用:||M.J.Weinberger, G.Seroussi, G.Sapiro,“The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,”IEEE Trans. on Image Processing, vol. 9, no. 8, Aug. 2000. M.J.Weinberger, G.Seroussi, G.Sapiro,“LOCO-I: A low complexity, context-based, lossless image compression algorithm,”IEEE Conf. on Data Compression (DCC), pp. 140-149, Mar. 1996. X.Wu, N.Memon,“CALIC-A Context Based Adaptive Lossless Image Codec,” IEEE Trans. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp.1891-1894, May 1996. X.Wu, N.Memon,“Context-based, adaptive, lossless image coding,”IEEE Trans. on Communications, vol. 45, no. 4, pp. 437-444, Apr. 1997. X.Wu,”Lossless Compression of Continuous-Tone Images via Context Selection, Quantization, and Modeling,”IEEE Trans. on Image Process., vol. 6, no. 5, pp. 656-664, May 1997. 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Owens, “Architectures for wavelet transforms: A Survey,” Journal of VLSI Signal Processing, vol. 14, no. 2, pp. 171-192, 1996. D.E.Knuth,“Dynamic Huffman coding,” J. Algorithms, vol. 6, no. 2, pp.163–180, June 1985. J. Jiang, “Novel design of arithmetic coding for data compression,” IEE Proc. on Computer and Digital Techniques, vol. 142, no. 6, pp. 419–424, Nov. 1995. MRI掃描器: http://upload.wikimedia.org/wikipedia/commons/b/bd/Modern_3T_MRI.JPG MRI掃描影像: http://xn--icko3ax6j8b2g4c.jp/mri 腦部解剖影像: http://www.neurochirurgie-zwolle.nl/grap/MRI_fMRI.jpg CT文件:http://bmeimage.be.cycu.edu.tw/Lab/database/CT/CT.html 膠囊內視鏡:http://www2.mmh.org.tw/gi/patient_corner/capsule/capsule.htm 膠囊內視鏡:http://www.vcharkarn.com/uploads/82/82549.jpg MRI訊號的物理原理與fMRI簡介.pdf. SPECT掃描影像:http://en.wikipedia.org/wiki/Golomb_coding 4-D MRI影像: www.vision.ethz.ch/4dmri||摘要:||
目前醫院已大量使用影像檔案與通訊系統(picture archiving and communication system；PACS)來管理儲存醫學影像，並且能透過網路做快速的傳遞。然而醫學影像由於解析度高，資料儲存量大，受限於實際硬碟空間與傳輸頻寬，往往有諸多不便。而無失真影像壓縮的技術則可以有效提昇儲存效率與傳輸量，並能忠實的還原原始影像，以作為後續的辨識、分類等應用需求。近年來雖有許多無失真醫學影像壓縮論文研究被發表，不過大多數的研究都未能完全利用醫學影之特性來進行預測性壓縮，因而影響了壓縮的效能。
Hospitals nowadays have relied heavily on picture archiving and communication system (PACS) to manage the ever increasing amount of medical images. With the help of PACS, medical images can also be retrieved from the archiving system and distributed rapidly over the internet to the client''s terminal. However, due to the high resolution of the medical images and the tremendous amount of volumetric data, the system is often overloaded in face of the limited communication bandwidth and data storage space. Lossless image compression techniques can thus step in to help alleviate the problem. They can be applied to effectively reduce the demands of bandwidth and storage while preserving the authenticity of the medical images subject to future diagnosis, classification purposes. Although numerous research on lossless medical image compression have been reported, many of these work fail to exploit the features unique to each type of images thoroughly in predictive compression, which leads to inferior compression efficiency.
In this thesis, we investigate efficient lossless compression schemes for both 2-D and 3-D medical images. In the proposed 2-D scheme, the prediction efficiency is greatly improved by using least square formulation. This scheme is further extended to the volumetric (3D) medical images. A bi-directional prediction technique aiming at exploiting the data correlations across adjacent image frames was developed. An error feedback mechanism similar to the one used in JPEG-LS and CALIC was also employed to enhance the prediction accuracy. The developed lossless compression system is applicable to both 2D and 3D images. Simulation results indicate that the average compression ratio of the proposed scheme, when applied to 2D images, outperforms those of JPEG-LS and CALIC schemes by 0.32bpp and 0.2bpp, respectively. For the volumetric medical image data, the proposed scheme exhibit even higher compression gains over existing schemes such as 3D-LCL and interband CALIC. The numbers are 0.4bpp and 0.5bpp in each case. The performance edge retains when compared with ME and CorCoeff schemes. Besides the compression efficiency, two other critical performance factors, i.e., computing complexity and memory usage, are also evaluated in this thesis.
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