Advances on medical imaging and computing

Authors:
Tianzi Jiang;Xiaobo Li;Gaolong Gong;Meng Liang;Lixia Tian;Fuchun Li;Yong He;Yufeng Zang;Chaozhe Zhu;Shuyu Li;Songyuan Tang
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P. R. China
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

Citing 4
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Abstract

In this article, we present some advances on medical imaging and computing at the National Laboratory of Pattern Recognition (NLPR) in the Chinese Academy of Sciences. The first part is computational neuroanatomy. Several novel methods on segmentations of brain tissue and anatomical substructures, brain image registration, and shape analysis are presented. The second part consists of brain connectivity, which includes anatomical connectivity based on diffusion tensor imaging (DTI), functional and effective connectivity with functional magnetic resonance imaging (fMRI). It focuses on abnormal patterns of brain connectivity of patients with various brain disorders compared with matched normal controls. Finally, some prospects and future research directions in this field are also given.