An important topic in medical research is the process of improving the images
obtained from medical devices. As a consequence, there is also a need to
improve medical image resolution and analysis. Another issue in this field is
the large amount of stored medical data [16]. Human brain databases at medical
institutes, for example, can accumulate tens of Terabytes of data per year. In
this paper, we propose a novel medical image format representation based on
multiple data structures that improve the information maintained in the medical
images. The new representation keeps additional metadata information, such as
the image class or tags for the objects found in the image. We defined our own
ontology to help us classify the objects found in medical images using a
multilayer neural network. As we generally deal with large data sets, we used
the MapReduce paradigm in the Cloud environment to speed up the image
processing. To optimize the transfer between Cloud nodes and to reduce the
preprocessing time, we also propose a data compression method based on
deduplication. We test our solution for image representation and efficient data
transfer in a multisite cloud environment. Our proposed solution optimizes the
data transfer with a time improvement of 27% on average.

本文提出一种基于多重数据结构的医学图像格式表示方法，它在医学图像中保留额外的元数据信息，并使用多层神经网络定义了自己的本体论来帮助分类医学图像中的对象。此外，研究人员还在云端使用 MapReduce 范例来改善数据处理速度，同时提出了一种基于去重的数据压缩方法以优化数据传输，该方案在多站点云环境中测试得到了验证。