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On the Suitability of SIFT Technique to Deal with Image Modifications Specific to Confocal Scanning Laser Microscopy

Published online by Cambridge University Press:  05 August 2010

Stefan G. Stanciu ,
Radu Hristu ,
Radu Boriga  and
George A. Stanciu
Stefan G. Stanciu*
Affiliation:
Center for Microscopy - Microanalysis and Information Processing, University Politehnica Bucharest, Splaiul Independentei 313, Sector 6, Bucharest, Romania
Radu Hristu
Affiliation:
Center for Microscopy - Microanalysis and Information Processing, University Politehnica Bucharest, Splaiul Independentei 313, Sector 6, Bucharest, Romania
Radu Boriga
Affiliation:
Faculty of Computer Science, “Titu Maiorescu” University, Bucharest, 22, Dâmbovnicului Street, Sector 4, Bucharest, Romania
George A. Stanciu
Affiliation:
Center for Microscopy - Microanalysis and Information Processing, University Politehnica Bucharest, Splaiul Independentei 313, Sector 6, Bucharest, Romania
*
Corresponding author. E-mail: [email protected]
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Abstract

Computer vision tasks such as recognition and classification of objects and structures or image registration and retrieval can provide significant information when applied to microscopy images. Recently developed techniques for the detection and description of local features make the extraction and description of local image features that are invariant to various changes possible. The invariance and robustness of feature detection and description techniques play a key role in the design and implementation of object recognition, image registration, or image mosaicing applications. The scale-invariant feature transform (SIFT) technique is a widely used method for the detection, description, and matching of image features. In this article we present the results of our experiments regarding the repeatability of SIFT features, and to the precision of the SIFT feature matching, under image modifications specific to confocal scanning laser microscopy (CSLM). We have analyzed the behavior of SIFT while changing the pinhole aperture, photomultiplier gain, laser beam power, and electronic zoom. Our experiments, conducted on CSLM images, show that the SIFT technique is able to match detected key points between images acquired at different values of the acquisition parameters with good precision and represents a consistent tool for computer vision applications in CSLM.

Keywords

confocal scanning laser microscopy (CSLM)SIFTinvariant featuresaffine detectorslocal feature descriptors
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 16 , Issue 5 , October 2010 , pp. 515 - 530
Copyright
Copyright © Microscopy Society of America 2010

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References

REFERENCES

Bay, H., Ess, A., Tuytelaars, T. & Gool, L.V. (2008). SURF: Speeded up robust features. Comput Vis Image Und 110(3), 346–359.CrossRefGoogle Scholar
Bay, H., Fasel, B. & Gool, L.V. (2006). Interactive museum guide: Fast and robust recognition of museum objects. Proceedings of the First International Workshop on Mobile Vision.Google Scholar
Brown, M. & Lowe, D. (2007). Automatic panoramic image stitching using invariant features. Int J Comp Vision 74(1), 59–73.CrossRefGoogle Scholar
Burghouts, G. & Geusebroek, J. (2009). Performance evaluation of local colour invariants. Comput Vision Image Und 113(1), 48–62.CrossRefGoogle Scholar
Cheng, P.C. & Kriete, A. (1995). Image contrast in confocal light microscopy. In Handbook of Biological Confocal Microscopy, Pawley, J. (Ed.), pp. 327–347. New York: Plenum Press.Google Scholar
Fan, X. & Xi, S. (2007). Feature-based automatic stitching of microscopic images. Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, 2. Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
Kaynig, V., Fischer, B., Wepf, R. & Buhmann, J.M. (2007). Fully automatic registration of electron microscopy images with high and low resolution. Microsc Microanal 13(S2), 198–199.CrossRefGoogle Scholar
Ke, Y. & Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. Proc. CVPR, 2, pp. 506–513. Los Alamitos, CA: IEEE Computer Society.Google Scholar
Kondra, S., Laishram, J., Ban, J., Migliorini, E., Difoggia, V., Lazzarino, M., Torre, V. & Ruaro, M. (2009). Integration of confocal and atomic force microscopy images. J Neurosci Methods 177(1), 94–107.CrossRefGoogle ScholarPubMed
Li, Y., Yang, J., Wu, R. & Gong, F. (2006). Efficient object tracking based on local invariant features. Proceedings of the International Symposium on Communications and Information Technologies (ISCIT '06), pp. 697–700. Los Alamitos, CA: IEEE Press.Google Scholar
Lowe, D.G. (1999). Object recognition from local scale-invariant features. Proceedings of the IEEE International Conference on Computer Vision, 2, pp. 1150–1157. Washington, DC: IEEE Computer Society.Google Scholar
Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2), 91–110.CrossRefGoogle Scholar
Lu, Y. & Shaham, S. (2008). Automated 3D reconstruction of serial electron microscopy image sequences using object recognition. Microsc Microanal 14(S2), 812CD–813CD.CrossRefGoogle Scholar
Ma, B., Zimmermann, T., Rohde, M., Winkelbach, S., He, F., Lindenmaier, W. & Dittmar, K. (2007). Use of autostitch for automatic stitching of microscope images. Micron 38, 492–499.CrossRefGoogle ScholarPubMed
Mikolajczyk, K. & Schmid, C. (2004). Scale and affine invariant interest point detectors. Int J Comput Vision 1(60), 63–86.CrossRefGoogle Scholar
Mikolajczyk, K. & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE T Pattern Anal 10(27), 1615–1630.CrossRefGoogle Scholar
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T. & Van Gool, L. (2005). A comparison of affine region detectors. Int J Comput Vision 65(1–2), 43–72.CrossRefGoogle Scholar
Pechaud, M., Vanzetta, I., Deneux, T. & Keriven, R. (2008). SIFT-based sequence registration and flow-based cortical vessel segmentation applied to high resolution optical imaging data. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, May 14–17, 2008, pp. 720–723. New York: IEEE.Google Scholar
Rothganger, F., Lazebnik, S., Schmid, C. & Ponce, J. (2006). 3D object modelling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. Int J Comput Vision 66, 231–259.CrossRefGoogle Scholar
Stanciu, S.G., Hristu, R., Boriga, R. & Stanciu, G. (2009). Feature based recognition of photonic devices in images obtained by confocal scanning laser microscopy. 11th International Conference on Transparent Optical Networks (ICTON '09). Piscataway, NJ: IEEE.Google Scholar
Tang, C., Dong, Y. & Su, X. (2008). Automatic registration based on improved SIFT for medical microscopic sequence images. Proceedings of the 2008 Second International Symposium on Intelligent Information Technology, pp. 580–583. Washington, DC: IEEE Computer Society.Google Scholar
Terasaki, M. & Dailey, M.E. (1995). Confocal microscopy of living cells. In Handbook of Biological Confocal Microscopy, Pawley, J. (Ed.), pp. 327–347. New York: Plenum Press.CrossRefGoogle Scholar
Thevenaz, P. & Unser, M. (2007). User-friendly semiautomated assembly of accurate image mosaics in microscopy. Microsc Res Tech 70, 135–146.CrossRefGoogle ScholarPubMed
Vercauteren, T. (2008). Image registration and mosaicing for dynamic in vivo fibered confocal microscopy. PhD Thesis, École National Supérieure des Mines de Paris.Google Scholar
Zhou, H., Yuan, Y. & Shi, C. (2009). Object tracking using SIFT features and mean shift. Comput Vis Image Und 113(3), 345–352.CrossRefGoogle Scholar
Zitova, B. (2003). Image registration methods: A survey. Image Vision Comput 21(11), 977–1000.CrossRefGoogle Scholar