病理組織學是一門使用顯微技術或電子顯微鏡去觀察細胞組織形態的研究。在過去幾十年來，醫院病理部通常是使用傳統的光學病理顯微鏡，去觀察分析生物組織切片，這些切片都是以玻璃的形式存放，所以在運送過程容易毀損，也因為時間的關係造成變質，而且一次一人分析的情況下也會有人工誤判的問題。近年來隨著數位病理影像的推動，高解析影像對於準確的診斷越來越被重視，並預期未來在組織病理學中扮演一個革命性的角色。以往生醫影像資料庫只能儲存低解析影像或是字串資料，如:DNA序列；而大多數現有的大型影像技術有頻寬和儲存空間上的限制，所以在傳輸及存取之前，必須將巨量影像進行有損壓縮來解決這些問題。綜合上述，我們發現在雲端上即時互動式顯示巨量高解析顯微影像是目前市場高度需求的技術，也是未來的研究趨勢。本文提出了一超高速巨量超高解析醫學影像雲端互動顯示技術，此技術可以讓研究人員即時放大高解析影像，並清楚看到細胞質、細胞核和細胞組織型態，另外由於此技術不受頻寬限制，使用者可在雲端上進行有效率的判斷、分析。為了驗證此技術之可行性，我們成功地將之運用在高解析生物組織切片影像、幹細胞及超音波影像等醫學影像，並可快速的平移、放大、縮小巨量高解析影像。而在效能評估的兩項實驗中，其結果證明了方法一在雲端上觀看高解析影像的平均時間只要0.5~0.67秒，比方法二快了三到四倍，而且方法一進行編碼後所需的儲存空間幾乎是方法二的一半，然而，由於方法一的編碼架構較為複雜，所以編碼時間比方法二慢1.4倍，但是方法二進行編碼後小於原始影像的檔案其實是不需要的。綜合上述，使用方法一觀看高解析影像及儲存空間上有較佳的表現。

Histopathology is the study of the microscopic anatomy of cells and tissues using microscopy techniques or electron microscope. During the past many decades, the pathologist sliced the cells or tissue into very thin layers that are mounted on a glass slide and examined under a microscope. With the recent advent of digital imaging, high-resolution digital images are necessary for accurate diagnosis, and it is expected to play a revolutionary role in future histopathology. In the past, biomedical image database can be only stored string data such as DNA sequences and low-resolution images. Most existing large-scale image techniques have bandwidth and storage limitations, so images must be lossy compressed to solve this problems before transmission and storage [1]. It is difficult to display high resolution microscopic images over internet in real time. This thesis presents two ultra fast super high resolution image cloud systems, which enables users to interactively view large, high resolution images within existing web browsers in real time. In evaluation, the method1 is demonstrated to render large high resolution image over internet in real time (0.5~0.67 second on average), and the results show that the method1 performs 3~4 times faster than method2 in rendering large high resolution images while costs approximately half of storage space method1 requires. However, the encoding time of method2 are 1.4 times faster than method1 due to following reasons, the regular encoding of the method2, and the complicated structure of the method1, but it is unnecessary to encode image dimension to 1*1 of the method2.

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