Fast Background Removal Method for 3D Multi-channel Deep Tissue Fluorescence Imaging
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.
KeywordsBackground removal Multi-channel Fluorescence microscopy image
This work are partially supported by the National Natural Science Foundation of China (Grant No. 11426026, 61632017, 61173011) and a Project 985 grant of Shanghai Jiao Tong University and the National Science Foundation of China (Grant No. 11374207, 31370750, 31670722 and 81627801) and SJTU Cross-Disciplinary Research Fund in Medicine and Engineering (Grant No. YG2012MS23). D. Chen’s research was supported in part by NSF grants CCF-1217906 and CCF-1617735. The authors are grateful to the generous support by Nikon Instruments Co., Ltd. (Shanghai, China).
- 2.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)Google Scholar
- 3.Lindblad, J., Bengtsson, E.: A comparison of methods for estimation of intensity non uniformities in 2D and 3D microscope images of fluorescence stained cells. In: Proceedings of the Scandinavian Conference on Image Analysis, pp. 264–271 (2001)Google Scholar
- 5.Peng, T., Wang, L., Bayer, C., Conjeti, S., Baust, M., Navab, N.: Shading correction for whole slide image using low rank and sparse decomposition. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 33–40. Springer, Cham (2014). doi: 10.1007/978-3-319-10404-1_5 Google Scholar
- 9.Yang, L., Zhang, Y., Guldner, I.H., Zhang, S., Chen, D.Z.: Fast background removal in 3d fluorescence microscopy images using one-class learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 292–299. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_35 CrossRefGoogle Scholar