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Erschienen in:
2017 | Supplement | Buchkapitel
Fast Background Removal Method for 3D Multi-channel Deep Tissue Fluorescence Imaging
verfasst von : Chenchen Li, Xiaowei Li, Hongji Cao, He Jiang, Xiaotie Deng, Danny Z. Chen, Lin Yang, Zhifeng Shao
Erschienen in: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
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Abstract
The recent advances in tissue clearing and optical imaging have enabled us to obtain three-dimensional high-resolution images of various tissues. However, the severe background noise remains a major obstacle. In addition, there is an urgent need for fast background ground correction methods. In this paper, we present a fast background removal method for 3D multi-channel deep tissue fluorescence images, in which the objectives of different channels are well separated. We first conduct a window-based normalization to distinguish foreground signals from background noises in all channels. Then, we identify the pure background regions by conducting subtraction of images in different channels, which allow us to estimate the background noises of the whole images by interpolation. Experiments on real 3D datasets of mouse stomach show our method has superior performance and efficiency comparing with the current state-of-the-art background correction methods.