Influence of confocal scanning laser microscopy specific acquisition parameters on the detection and matching of speeded-up robust features.

The robustness and distinctiveness of local features to various object or scene deformations and to modifications of the acquisition parameters play key roles in the design of many computer vision applications. In this paper we present the results of our experiments on the behavior of a recently developed technique for local feature detection and description, Speeded-Up Robust Features (SURF), regarding image modifications specific to Confocal Scanning Laser Microscopy (CSLM). We analyze the repeatability of detected SURF keypoints and the precision-recall of their matching under modifications of three important CSLM parameters: pinhole aperture, photomultiplier (PMT) gain and laser beam power. During any investigation by CSLM these three parameters have to be modified, individually or together, in order to optimize the contrast and the Signal Noise Ratio (SNR), being also inherently modified when changing the microscope objective. Our experiments show that an important amount of SURF features can be detected at the same physical locations in images collected at different values of the pinhole aperture, PMT gain and laser beam power, and further on can be successfully matched based on their descriptors. In the final part, we exemplify the potential of SURF in CSLM imaging by presenting a SURF-based computer vision application that deals with the mosaicing of images collected by this technique.

[1]  J. Ellenberg,et al.  Automatic real‐time three‐dimensional cell tracking by fluorescence microscopy , 2004, Journal of microscopy.

[2]  Zhenjiang Miao,et al.  An Improved Algorithm on Image Stitching Based on SIFT features , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[3]  Hermann Ney,et al.  Features for image retrieval: an experimental comparison , 2008, Information Retrieval.

[4]  Andrew P. Bradley,et al.  Towards Optimal Image Stitching for Virtual Microscopy , 2005, Digital Image Computing: Techniques and Applications (DICTA'05).

[5]  Radu Hristu,et al.  On the suitability of SIFT technique to deal with image modifications specific to confocal scanning laser microscopy. , 2010, Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada.

[6]  Jing Li,et al.  A comprehensive review of current local features for computer vision , 2008, Neurocomputing.

[7]  Patrik Langehanenberg,et al.  Automated three-dimensional tracking of living cells by digital holographic microscopy. , 2009, Journal of biomedical optics.

[8]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[9]  Luc Van Gool,et al.  Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views , 2006, International Journal of Computer Vision.

[10]  Kebin Jia,et al.  An Effective Web Content-Based Image Retrieval Algorithm by Using SIFT Feature , 2009, 2009 WRI World Congress on Software Engineering.

[11]  Michael Unser,et al.  User‐friendly semiautomated assembly of accurate image mosaics in microscopy , 2007, Microscopy research and technique.

[12]  Stamatia Giannarou,et al.  Object recognition in the ovary: Quantification of oocytes from microscopic images , 2009, 2009 16th International Conference on Digital Signal Processing.

[13]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Andrew Zisserman,et al.  Video data mining using configurations of viewpoint invariant regions , 2004, CVPR 2004.

[15]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[16]  A. Diaspro.  Confocal and two-photon microscopy : foundations, applications, and advances , 2001 .

[17]  S. Paddock,et al.  Confocal laser scanning microscopy. , 1999, BioTechniques.

[18]  Richard Szeliski,et al.  Systems and Experiment Paper: Construction of Panoramic Image Mosaics with Global and Local Alignment , 2000, International Journal of Computer Vision.

[19]  Xiaobo Zhou,et al.  Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy , 2006, IEEE Transactions on Biomedical Engineering.

[20]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[21]  Niko Sünderhauf,et al.  COMPARING SEVERAL IMPLEMENTATIONS OF TWO RECENTLY PUBLISHED FEATURE DETECTORS , 2007 .

[22]  Yudong Zhang,et al.  Tracking features in retinal images of adaptive optics confocal scanning laser ophthalmoscope using KLT-SIFT algorithm , 2010, Biomedical optics express.

[23]  Weixing Wang,et al.  Algorithm for automatic image registration on Harris-Laplace features , 2009 .

[24]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[25]  Matthew A. Brown,et al.  Automatic Panoramic Image Stitching using Invariant Features , 2007, International Journal of Computer Vision.

[26]  Tinne Tuytelaars,et al.  Local Image Features , 2009, Encyclopedia of Biometrics.

[27]  Huiyu Zhou,et al.  Object tracking using SIFT features and mean shift , 2009, Comput. Vis. Image Underst..

[28]  Marco Lazzarino,et al.  Integration of confocal and atomic force microscopy images , 2009, Journal of Neuroscience Methods.

[29]  Richard Szeliski,et al.  Construction of Panoramic Image Mosaics with Global and Local Alignment , 2001 .

[30]  Thomas Deneux,et al.  Sift-based sequence registration and flow-based cortical vessel segmentation applied to high resolution optical imaging data , 2008, ISBI.

[31]  H. Kestler,et al.  Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system , 2010, BMC Cell Biology.

[32]  Tom Vercauteren,et al.  Image registration and mosaicing for dynamic In vivo fibered confocal microscopy : Image Registration and Mosaicing for Dynamic In Vivo Fibered Confocal Microscopy. (Recalage et mosaïques d'images pour la microscopie confocale fibrée dynamique in vivo) , 2008 .

[33]  Gertjan J. Burghouts,et al.  Performance evaluation of local colour invariants , 2009, Comput. Vis. Image Underst..

[34]  Yan Dong,et al.  Automatic Registration Based on Improved SIFT for Medical Microscopic Sequence Images , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[35]  Xiang Fan,et al.  Feature Based Automatic Stitching of Microscopic Images , 2007, ICIC.

[36]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[37]  Wei Zhang,et al.  Image Based Localization in Urban Environments , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[38]  Pietro Perona,et al.  Automatic recognition of biological particles in microscopic images , 2007, Pattern Recognit. Lett..

[39]  Wei-bang Chen,et al.  Automatic in vivo microscopy video mining for leukocytes , 2007, SKDD.

[40]  R M Zucker,et al.  Practical confocal microscopy and the evaluation of system performance. , 1999, Methods.

[41]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[42]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Stefan G. Stanciu,et al.  Feature based recognition of photonic devices in images obtained by confocal scanning laser microscopy , 2009, 2009 11th International Conference on Transparent Optical Networks.