Abstract
The confocal fluorescence endomicroscopy is an emerging technology for imaging the living subjects inside the animals and human bodies. However, the acquired images vary, due to two degrees of freedom–tissue movement and tissue expansion/contraction. This makes the 3D reconstruction of them difficult and thus limits the clinic applications. In this chapter, we propose a feature-based registration algorithm to correct the distortions between these fluorescence images. The good alignment enables us to reconstruct and visualize the 3D structure of the living cells and tissues in real time, which provides the opportunity for the clinicians to diagnose various diseases, including the early-stage cancers. Experimental results on a collection of more than 300 confocal fluorescence images of the gerbil brain microvasculature clearly demonstrate the effectiveness and accuracy of our method.
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Acknowledgments
This work was supported by two grants: SBIC RP C-010/2006 from A-Star Biomedical Research Council, Singapore, and AcRF/RGM 35/06 from Ministry of Education, Singapore. The authors would like to thank M. Goetz, C. Schneider, et al. (University of Mainz, Germany), who provided the fluorescence images of mouse microvasculature for this study. We are also thankful to S. Thomas (Optiscan Pty. Ltd., Australia) and our clinic partners, Prof. Soo Khee Chee, A/P Malini Olivo and Dr Patricia Thong (National Cancer Centre Singapore).
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Zhao, F., Cheong, L.S., Lin, F., Qian, K., Seah, H.S., Kung, SY. (2010). Registration of In Vivo Fluorescence Endomicroscopy Images Based on Feature Detection. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_59
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_59
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