CASIA OpenIR  > 中国科学院分子影像重点实验室
A fast image registration approach of neural activities in light-sheet fluorescence microcopy images
Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie
2017-02-13
Conference NameSPIE Medical Imaging
Source PublicationSPIE Digital Library
Conference Date11 - 16 February 2017
Conference PlaceOrlando, Florida United States
AbstractThe ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. In this work, we address the problem of real-time registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve real-time registration of neural activities, we present a fast rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stack was preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. The registration can be accelerated using more GPUs in the architecture. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.
KeywordImage Registration Gpu Implementation Light-sheet Microscopy
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12264
Collection中国科学院分子影像重点实验室
Corresponding AuthorHui, Hui
AffiliationKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Meng, Hui,Hui, Hui,Hu, Chaoen,et al. A fast image registration approach of neural activities in light-sheet fluorescence microcopy images[C],2017.
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