Liu Sidong, Cai Weidong, Wen Lingfeng, Eberl Stefan, Fulham Michael J, Feng Dagan
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5657-60. doi: 10.1109/IEMBS.2010.5627900.
The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric neurological images. Our preliminary results indicate that the proposed volumetric feature extraction approach could support reliable 3D neuroimaging data retrieval and management.
三维神经成像数据量的增加产生了对高效数据管理和检索的需求。我们认为，通过强大的体积特征进行图像检索有助于管理这些大型图像数据集。在本文中，我们引入了一种基于面向无序掩码的新特征提取方法，该方法利用三维生理参数神经图像中的体积空间分布模式。我们的初步结果表明，所提出的体积特征提取方法能够支持可靠的三维神经成像数据检索和管理。
