Title: Application of SIFT and SURF Detectors for Medical X-Ray Images
Authors: Prachi. G. Bhende, Dr A. N. Cheeran
DOI: http://dx.doi.org/10.18535/jmscr/v4i3.09
Abstract
Detecting, identifying, and recognizing salient regions or feature points in images is a very important and fundamental problem in computer vision and robotics. For gray scale x-ray images, stable and repeatable salient features that are invariant to a variety of effects like rotation, scale changes, view point changes, noise, or change in illumination conditions can provide better classification results.This paper compares two different methods for scale and rotation invariant interest point/feature detector and descriptor: Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF). It also presents a way to extract distinctive invariant features from gray scale X-ray images that can be used to perform reliable matching between different views of an object/scene.
Keywords: Feature detection, Feature matching, SIFT, SURF
References
1. David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, In International Journal of Computer Vision, 60, 2, pp. 91– 110, 2004.
2. Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, “Surf: Speeded up robust features”, In proceedings of the ninth European Conference on Computer Vision, May 2006.
3. Krystian Mikolajczyk and Cordelia Schmid.A performance evaluation of local descriptors, IEEE Transaction on Pattern Analysis & Machine Intelligence, Vol.27(10),pp.1615–1630,2005. URLhttp://lear.inrialpes.fr/pubs/2005/MS05
4. T. Linderberg, “ Feature detection with automatic scale selection”, IJCV, 30(2),pp:79 - 116, 1998.
5. Seok-Wun Ha, Yong-Ho Moon, “Multiple Object Tracking Using SIFT Features and Location Matching” ,International Journal of Smart Home ,Vol. 5, No. 4,pp. 17-26, October 2011.
6. G. Carneiro and A.D. Jepson, “Multi-scale phase-based local features”, In CVPR (1), pp. 736-743, 2003.
7. C. Harris and M. Stephens, “A combined corner and edge detector”, In Proceedings of the Alvey Vision Conference 1988, pp. 147–151, 1988.
8. T. Linderberg and Bart M, Haar Romeny, “Linear scale-space” In Geometry-Driven Diffusion, Kluwer Academic Publishers ,pages 1–77, Dordrecht, Netherlands, 1994.
9. Luo Juan, and Oubong Gwun, “A Comparison of SIFT, PCA-SIFT and SURF”, International Journal of Image Processing (IJIP), Vol. 3, Issue 4, pp. 143-152.
10. [P. C. Cattin, H. Bay, L. Van Gool, and G. Szekely, “Retina mosaicing using local features”, In Medical Image Computing and Computer-Assisted Intervention (MICCAI), October 2006.
11. F. Jurie and C. Schmid, “Scale-invariant shape features for recognition of object categories”, In CVPR, volume II, pp. 90 - 96, 2004.
12. D. Lowe,“Distinctive Image Features from Scale-Invariant Key points”, International Journal of Computer Vision, pp. 1-28, 2004.
13. Peer Neubert, Niko Sunderhauf, and Peter Protzel, “Fastslam using SURF features: An efficient implementation and practical experiences”, In Proceedings of the International Conference on Intelligent and Autonomous Vehicles, IAV07, Tolouse, France, September 2007.
14. Stephen Se, David G. Lowe, and Jim Little, .Mobile Robot, “Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks”, In International Journal of Robotics Research, 21, 8, pp. 735–758, 2002.
15. Nabeel Younus Khan, Brendan McCane, and Geoff Wyvill, “SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset”, International Conference on Digital Image Computing: Techniques and Applications, pp.501-506, 2011.
16. Hongbo Li, Ming Qi And Yu Wu, “A Real-Time Registration Method Of Augmented Reality Based On Surf And Optical Flow”, Journal Of Theoretical And Applied Information Technology, Vol. 42, No.2, pp. 281-286, August 2012.
17. C. Dance, J. Willamowski, L. Fan, C. Bray, and G. Csurka, “Visual categorization with bags of keypoints”, International Workshop on Statistical Learning in Computer Vision, Prague, 2004.
18. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni, “Locality- sensitive hashing scheme based on p-stable distributions”, In Symposium on Computational Geometry, pp. 253-262, 2004.
19. R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning”, In CVPR (2), pp. 264-271, 2003
20. L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever, “General intensity transformations and differential invariants”, JMIV, Vol. 4(2), pp. 171-187, 1994.
21. W. T. Freeman and E. H. Adelson, “The design and use of steerable filters”, PAMI, Vol. 13(9), pp. 891- 906, 1991.
22. J. Goldstein, J. C. Platt, and C. J. C. Burges, “Redundant bit vectors for quickly searching high-dimensional regions”, In Deterministic and Statistical Methods in Machine Learning, pp. 137-158, 2004.
23. M. Grabner, H. Grabner, and H. Bischof, “Fast approximated sift”, In ACCV, Vol.1, pp. 918-927, 2006.
24. Vini Vidyadharan, and Subu Surendran, “Automatic Image Registration using SIFT-NCC”, Special Issue of International Journal of Computer Applications (0975 – 8887) , pp.29-32, June 2012.
25. T. Kadir and M. Brady, “Scale, saliency and image description”, IJCV, Vol. 45(2), pp. 83 - 105, 2001.
26. Y. Ke and R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors”, In CVPR, Vol. (2), pp. 506- 513, 2004