标题: | 超频谱生医影像系统之巨量资料处理与云端操控之研究 Research of big data process and cloud control for biomedical hyperspectral imaging system |
作者: | 龚益群 Kung, Yi-Chiun 欧阳盟 Ou-Yang, Mang 电机工程学系 |
关键字: | 超频谱影像;巨量资料;云端控制;资料压缩;hyperspectral imaging;big data;cloud control;data compression |
公开日期: | 2013 |
摘要: | 此论文提出一种新的超频谱生医影像平台,此平台以嵌入式继光显微超频影像系统 (ERL-HIS)作为代表超频谱生医影像的载具,并整合了云端控制以及即时压缩于其中。整合于ERL-HIS的云端控制能够透过连结机台的伺服器与远端用户做连结,进而操控ERL-HIS的扫描动作并同时做资料接收。 ERL-HIS内的MFC伺服器与Android 用户端彼此互相传送讯息,这些讯息依赖设计好的命令架构做仪器操控以及资料传输。除此之外,此种超频谱生医影像应用于巨量资料上的概念也将被讨论于此论文,主要讨论所提出的压缩方法与Hadoop平行运算做结合之应用细节,以及一种压缩率略差但是可以快速解压缩出想要的特定频谱资讯之方法。 另外,此论文也着重于ERL-HIS影像的压缩,分为充满杂讯的原影像之压缩与去杂讯后的影像压缩,所提出的压缩法主要利用邻近像素的整条光谱来预测尚未被预测的光谱之趋势,该种方法与着名的LUT法以及3D-CALIC的预测路径(相邻频带间的预测)是完全不同的。 对于充满杂讯的原影像,所提出的方法在资料残余之熵值上比起LUT与3D-CALIC还要少1bpp以上,因此可以达到更好的压缩率。而对于去杂讯后的影像,所提出的方法在熵值上比起LUT方法少约0.5bpp,比起3D-CALIC则少约0.3bpp,因此在压缩上仍然可以稍微得到较佳的压缩率。在执行时间上此三者的比例为: 1 : 47.58 : 4.25 (LUT:3D-CALIC:所提出的方法(去杂讯版本)) 1 : 47.58 : 13.83 (LUT:3D-CALIC:所提出的方法(杂讯版本)) 可以看出,在压缩率上此论文所提出的方法可以达到最佳的压缩率,且有着比起3D-CALIC快上数倍的执行速度。 In this thesis, a novel platform for an embedded relay lens hyperspectral imaging system (ERL-HIS) is presented. This type of platform integrates cloud control and real-time compression on ERL-HIS. Cloud control is designed to operate the ERL-HIS remotely through a cloud server and users can manipulate the ERL-HIS during scanning for image transformation. The details of how to make commands from android clients to the ERL-HIS using an MFC server are discussed. Additionally, a concept for biomedical big data is discussed. We delineate the utilization of the proposed compression method into Hadoop for parallel computing. Also, a modified compression designed for quick access decoding path is discussed. A novel compression method for noisy signals from an ERL-HIS and de-noised signals are proposed. The proposed method predicts pixels using the tendency of neighbors’ spectrums while the other two methods, the LUT method and the 3D-CALIC method, predict pixels band by band. These proposed compression methods are compared to the LUT method and the 3D-CALIC method with regard to compressing an AVIRIS hyperspectral image. The proposed method performs with a better compression ratio (CR) than LUT and 3D-CALIC with regard to noisy signals from the ERL-HIS and its residuals cost about 1bpp less other methods. When compressing de-noised signals, entropy of the residuals produced from proposed method is less than LUT’s about 0.5bpp and is less than 3D-CALIC’s about 0.1 bpp so it still has better CR than other methods. The average runtime proportion of these methods are as follows: 1 : 47.58 : 4.25 (LUT:3D-CALIC:proposed method(de-noised version)) 1 : 47.58 : 13.83 (LUT:3D-CALIC: proposed method (noisy version)) Thus, it is suggested for compressing ERL-HIS biomedical images if a high CR is required. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT070050730 http://hdl.handle.net/11536/73620 |
显示于类别: | Thesis |