Matching DSIFT Descriptors Extracted from CSLM Images

Stefan G. Stanciu, Dinu Coltuc, Denis E. Tranca, George A. Stanciu

Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania.
Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucure?ti, Romania Light Microscopy and Screening Center, Swiss Federal Institute of Technology, Zurich, Switzerland.
Electric Engineering Department, Valahia University of Targovi?te, Targovi?te, Romania.

DOI: 10.4236/eng.2013.510B042                

Abstract

The matching of local descriptors represents at this moment a key tool in computer vision, with a wide variety of methods designed for tasks such as image classification, object recognition and tracking, image stitching, or data mining relying on it. Local feature description techniques are usually developed so as to provide invariance to photometric variations specific to the acquisition of natural images, but are nonetheless used in association with biomedical imaging as well. It has been previously shown that the matching of gradient based descriptors is affected by image modifications specific to Confocal Scanning Laser Microscopy (CSLM). In this paper we extend our previous work in this direction and show how specific acquisition or post-processing methods alleviate or accentuate this problem.

Keywords

Local Features; Local Descriptors; Feature Matching; SIFT; CSLM

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	L. J. Zhi, S. M. Zhang, D. Z. Zhao, H. Zhao, S. K. Lin, D. Z. Zhao and H. Zhao, “Medical Image Retrieval Using SIFT Feature,” Proceedings of the 2009 2nd International Congress on Image and Signal Processing, Vol. 1-9, 2009, pp. 2252-2255. http://dx.doi.org/10.1109/CISP.2009.5304112
[2]	G. Kordelas and P. Daras, “Viewpoint Independent Object Recognition in Cluttered Scenes Exploiting Ray-Triangle Intersection and SIFT Algorithms,” Pattern Recognition, Vol. 43, 2010, pp. 3833-3845. http://dx.doi.org/10.1016/j.patcog.2010.05.030
[3]	M. Brown and S. Susstrunk, “Multi-Spectral SIFT for Scene Category Recognition,” 2011 IEEE Conference on Computer Vision and Pattern Recognition (Cvpr), 2011, pp. 177-184.
[4]	J. Matas, O. Chum, M. Urban and T. Pajdla, “Robust Wide-Baseline Stereo from Maximally Stable Extremal Regions,” Image and Vision Computing, Vol. 22, 2004, pp. 761-767. http://dx.doi.org/10.1016/j.imavis.2004.02.006
[5]	M. Brown, and D. G. Lowe, “Automatic Panoramic Image Stitching Using Invariant Features,” International Journal of Computer Vision, Vol. 74, 2007, pp. 59-73. http://dx.doi.org/10.1007/s11263-006-0002-3
[6]	S. G. Stanciu, R. Hristu and G. A. Stanciu, “Influence of Confocal Scanning Laser Microscopy Specific Acquisition Parameters on the Detection and Matching of Speeded-Up Robust Features,” Ultramicroscopy, Vol. 111, 2011, pp. 364-374. http://dx.doi.org/10.1016/j.ultramic.2011.01.014
[7]	P. Piccinini, A. Prati and R. Cucchiara, “Real-Time Object Detection and Localization with SIFT-Based Clustering,” Image and Vision Computing, Vol. 30, 2012, pp. 573-587. http://dx.doi.org/10.1016/j.imavis.2012.06.004
[8]	M. Dawood, C. Cappelle, M. E. El Najjar, M. Khalil and D. Pomorski, “Harris, SIFT and SURF Features Comparison for Vehicle Localization Based on Virtual 3D Model And Camera,” 2012 3rd International Conference on Image Processing Theory, Tools and Applications, 2012, pp. 307-312.
[9]	J. C. Caicedo, A. Cruz and F. A. Gonzalez, “Histopathology Image Classification Using Bag of Features and Kernel Functions,” Artificial Intelligence in Medicine, Proceedings, Vol. 5651, 2009, pp. 126-135.
[10]	T. Tamaki, J. Yoshimuta, M. Kawakami, B. Raytchev, K. Kaneda, S. Yoshida, Y. Takemura, K. Onji, R. Miyaki and S. Tanaka, “Computer-Aided Colorectal Tumor Classification in NBI Endoscopy Using Local Features,” Medical Image Analysis, Vol. 17, 2013, pp. 78-100. http://dx.doi.org/10.1016/j.media.2012.08.003
[11]	D. G. Lowe, “Distinctive Image Features from Scale- Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, 2004, pp. 91-110. http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94
[12]	J. B. Pawley, “Handbook of Biological Confocal Microscopy,” Springer, New York, 2006. http://dx.doi.org/10.1007/978-0-387-45524-2
[13]	S. G. Stanciu, R. Hristu, R. Boriga and G. A. Stanciu, “On the Suitability of SIFT Technique to Deal with Image Modifications Specific to Confocal Scanning Laser Microscopy,” Microscopy and Microanalysis, Vol. 16, 2010, pp. 515-530. http://dx.doi.org/10.1017/S1431927610000371
[14]	S. G. Stanciu, G. A. Stanciu and D. Coltuc, “Automated Compensation of Light Attenuation in Confocal Micro-scopy by Exact Histogram Specification,” Microscopy Research and Technique, Vol. 73, 2010, pp. 165-175. http://dx.doi.org/10.1002/jemt.20767
[15]	A. Vedaldi and B. Fulkerson, “VLFeat: An open and Portable Library of Computer Vision Algorithms,” 2008.
[16]	R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Addison-Wesley Longman Publishing Co., Inc., Boston, 2001.
[17]	W. Wallace, L. H. Schaefer and J. R. Swedlow, “A Workingperson’s Guide to Deconvolution in Light Microscopy,” Biotechniques, Vol. 31, 2001, p. 1076.