Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8837
標題: 高維度無失真醫學影像壓縮系統
High-Dimension Lossless Medical Image Compression System
作者: 陳俊江
Chen, Jiun-Jiang
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
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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。
本論文將探討高效率的二維與三維醫學影像無失真壓縮系統。我們在二維影像壓縮系統中提出線性預測壓縮方法,利用最小平方差值的條件獲得較準確的預測值,此外並利用影像畫面間的相關性,將二維線性預測的方法延展到三維影像。其壓縮效率勝過其它二維與三維影像壓縮系統。
在三維影像預測壓縮中,我們利用前後張畫面與當前張畫面的關係性,採用雙向預測之方法,並參考JPEG-LS[1]和CALIC[3]的誤差回授補償架構,以提升預測之準確性。所發展出的高維度無失真醫學影像壓縮系統可適用於單張與連續畫面醫學影像的壓縮。模擬結果顯示,在二維影像平均壓縮率比JPEG-LS好0.32位元編碼率、比CALIC好0.2位元編碼率。在三維影像平均壓縮率比3D-LCL好0.4位元編碼率、比Interband CALIC好0.55位元編碼率,所提出的演算法也勝過ME及CorCoeff等演算法。除了壓縮效能外,我們也針對各種壓縮方法的運算複雜度以及記憶體需求作分析。

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.
URI: http://hdl.handle.net/11455/8837
其他識別: U0005-1603201112484300
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