Content-Based Retrieval of Multimodal Medical Images

多模型医学图像的基于内容的检索医学图像是患者诊断、治疗、手术计划、结果评估和医学教育的核心。然而，它们在未来的多媒体医疗保健环境中对信息管理提出了最严格的要求。因此，本研究将探索基于内容的多模态医学图像检索新技术，包括磁共振成像（MRI）、正电子发射断层扫描（PET）、磁共振波谱（MRS）和脑磁图（MEG），以及补充文本报告和图像注释。“内容”一词是广义的，包括图像中特定物体的结构和功能上下文，物体本身及其三维解剖结构，甚至不同类型医学图像之间的语义关系。本探索性研究的成功将为构建未来的多媒体医疗信息系统提供关键的技术成分，并为数字化医学图书馆开辟新的前景。

IRI-9527982 Wong, Stephen University of California-San Francisco $49,666 - 12 mos SGER: content-Based Retrieval of Multimodel Medical Images Medical images are at the heart of patient's diagnosis, therapy treatment, surgical planning, outcome assessment, and medical education. They, however, impose the most stringent requirements of information management in the future multimedia healthcare environments. Accordingly, this research will explore new techniques of content-based retrieval of multimodal medical images, including magnetic resonance imaging (MRI), positron emission tomography (PET), magnetic resonance spectroscopy (MRS), and magnetoencephalography (MEG), as well as complemented textual reports and image annotations. The term "content" is in a general sense, including structural and functional contexts of particular objects in an image, the objects themselves and their three dimensional anatomical structure, and even their semantic relationships across different kinds of medical images. The success of this exploratory research will provide key technical ingredients to construct future multimedia healthcare information systems and open up new vistas for digital medical library.