臺灣博碩士論文加值系統

影像儲存與傳輸系統(Picture Archiving and Communication Systems, PACS)被發展出來處理數位醫學影像，包含硬體與軟體兩個層面的設計，最終目標是希望有效解決儲存空間和傳輸問題。因為壓縮的原理是去除影像中冗餘性(Redundancy)來減少資料容量，因此一種壓縮方式的優劣與被壓縮資料本身的特性相當有關係，必須根據待壓縮資料的特性結構選擇合宜的方式才可以有效壓縮資料。所以隨著醫學影像的種類不同，造影方式的改變，直接或間接都會改變影像儲存與傳輸系統中壓縮方式的改變，故必須由影學影像的特性和壓縮的本質探討，來滿足不同影像的需求，找尋一個合適壓縮方式的策略。
本論文著重在探討存在於動態立體影像中各方相間的冗餘性，並利用不同於傳統小波分解(Dyadic Decomposition)的方式─Directional Dyadic Decomposition來去除而達到更有效的影像壓縮。而此Directional Dyadic Decomposition分解的策略亦可用於多維度影像。其次探討影像經由小波分解之後所形成的階層式結構( Pyramidal Structure )，以及零樹狀編碼(Zerotree Coding)如何應用此結構來做有效壓縮編碼與漸進式傳輸(Progressive Transmission)，達到網路化的需求，以期對PACS的傳輸與儲存有所助益。

Medical services today rely heavily on imaging technology, including X-ray computed tomography, magnetic resonance imaging, nuclear medicine examination, and etc. As these examinations become more and more common and more medical equipment, picture archiving and communication systems (PACS) have been proposed as tools for converting the image data in digital form and for handling the resulting amount of information. In order to make PACS work well, we need to compress the image data for efficient storage and transmission. The goal of image compression is to reduce the redundancy in the original image, since the adjacent pixel values are usually correlated. For medical image, lossless compression is preferred, because it does not degrade the image and can facilitate accurate diagnosis. So we use the integer wavelet for lossless compression. Compressing an image set with multi-dimension is very important in medical. By removing redundancy in the different dimension of images, we can acquire the smaller image data size than by conventional 2D method.
Our study is to know the difference of redundancy between each dimension using correlative map. The larger redundancy is, the higher priority is. According to the priority, we can build a decomposition order from higher priority to lower priority. Conventional wavelet dyadic decomposition method is to remove the average redundancy existing in all dimensions of images and could not reveal the different redundancy in different dimension. So we have improved the wavelet decomposition method, which is called directional dyadic decomposition, to decrease more redundancy of images. And we use the 2D set partitioning in hierarchical trees (SPIHT) to code the pyramidal structure after directional dyadic decomposition. Compared with the dyadic decomposition, our directional dyadic decomposition with 2DSPIHT coding produced 10% increases in compression ratio. Besides, the compression ratio of our method is about two times higher than JPEG-LS.

第一章 緒論 (Introduction) 1
1.1 醫學動態立體影像( Medical Dynamic-Volume images)..........1
2.2 影像儲存與傳輸系統 (Picture archiving and communication system, PACS).................................................3
1.3 醫學影像壓縮(Medical Image Compression)...................4
第二章 影像壓縮原理 (The Principle of Image Compression)
2.1 前言(Preface).............................................7
2.2 醫學影像壓縮(Medical Image Compression)...................7
2.3 資訊理論(Information Theory)..............................8
2.4 熵值編碼(Entropy Coding)..................................8
2.5 時域編碼(Time-domain Coding).............................10
2.6 頻域編碼(Frequency-domain Coding)........................11
第三章 整數小波的基礎原理 (Fundamentals of Integer Wavelet Compression)
3.1 小波轉換(Wavelet Transform)..............................12
3.2 Lifting Scheme ...........................................17
3.3 整數小波轉換(Integer Wavelet Transform)..................19
第四章 多解析度零樹狀編碼 ( Multi-resolution Zerotree Coding)
4.1 階層結構( Property of the Pyramidal Structure)...........20
4.2 嵌入式零樹枝小波編碼 (Embedded Zerotree Wavelet Coding, EZW).........................................................21
4.3 階層式樹枝集合分割編碼 (Set Partitioning in Hierarchical Trees , SPIHT)...............................................26
第五章 影像分解與冗餘性移除策略 ( Image Decomposition and Redundancy Removal Strategy)
5.1 相關圖(Correlative Map) .................................29
5.2 多維度影像小波分解方式與分解順序的影響 (The Wavelet Decomposition of Multi-dimensional Image and the Influence of Decomposition Order )........................................32
5.3 小波分解階層(The Decomposition Level of Wavelet).........36
5.4 零樹狀編碼(Zerotree Coding)..............................36
第六章 實驗和討論 ( Experiments and Discussions).............39
6.1 影像冗餘性的探討─相關圖的觀察 (Analysis of Image Redundancy─Correlative Map )
6.2多維度影像小波分解方式與分解順序的影響 (The Influence of Wavelet Decomposition Method and Order for Multi-dimensional Images)......................................................48
6.3 多維度小波分解(Multi-dimension Decomposition of Wavelet).....................................................54
6.4 小波分解階層對熵值的影響 (Influence of Wavelet Decomposition Level).........................................56
6.4 零樹狀編碼(Zerotree Coding)..............................60
第七章 結論和未來展望 ( Conclusions and Future Work).........72
參考資料(Reference)..........................................73

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