A fast and accurate feature-matching algorithm for minimally-invasive Endoscopic images

IEEE Transactions on Medical Imaging

Volumn 32, Issue 7, 2013, Pages 1201-1214

  Puerto Souza, Gustavo A ^a   Mariottini, Gian Luca ^a  

^a The University of Texas at Arlington   (United States)

Author keywords

Abdomen; endoscopic image analysis; endoscopy; feature matching; robust estimation

Indexed keywords

ABDOMEN; ENDOSCOPIC IMAGE; FEATURE MATCHING; GEOMETRIC CONSTRAINT; ILLUMINATION CHANGES; MINIMALLY INVASIVE SURGERY; ROBUST ESTIMATION; STATE-OF-THE-ART METHODS;

ALGORITHMS; ENDOSCOPY; IMAGE ANALYSIS;

IMAGE MATCHING;

ACCURACY; ALGORITHM; ARTICLE; DATA BASE; ENDOSCOPY; GEOMETRY; HIERARCHICAL MULTI AFFINE ALGORITHM; IMAGE ANALYSIS; MINIMALLY INVASIVE SURGERY; QUANTITATIVE ANALYSIS; VELOCITY;

ALGORITHMS; AREA UNDER CURVE; CLUSTER ANALYSIS; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LAPAROSCOPY; NEPHRECTOMY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; ROC CURVE; VIDEO-ASSISTED SURGERY;

EID: 84880224888     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2013.2239306     Document Type: Article

References (48)

1. B. P. Lo, M. Visentini-Scarzanella, D. Stoyanov, and G.-Z Yang, "Belief propagation for depth cue fusion in minimally invasive surgery," in Proc. 11th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2008, pp. 104-112.
2. M. Visentini-Scarzanella, G. P.Mylonas, D. Stoyanov, and G.-Z Yang, " -brush: A gaze-contingent virtual paintbrush for dense 3-D reconstruction in robotic assisted surgery," in Proc. 12th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2009, pp. 353-360.
3. J. Totz, P.Mountney, D. Stoyanov, and G.-Z Yang, "Dense surface reconstruction for enhanced navigation in MIS," in Proc. 14th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2011, pp. 89-96.
4. D. Stoyanov, G. Mylonas, F. Deligianni, A. Darzi, and G.-Z Yang, "Soft-tissue motion tracking and structure estimation for robotic assisted MIS procedures," in Proc. 8th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2005, pp. 139-146. (Pubitemid 43828358)
5. M. Hu, G. P. Penney, D. Rueckert, P. J. Edwards, F. Bello, R. Casula, M. Figl, and D. J. Hawkes, "Non-rigid reconstruction of the beating heart surface for minimally invasive cardiac surgery," in Proc. 12th Int. Conf. Med. Image Comp. Comp-Asst. Intervent., 2009, pp. 34-42.
6. P. Mountney and G. Z. Yang, "Motion compensated slam for image guided surgery," in Proc. 13th Int. Conf. Med. Image Comp. Comp- Asst. Intervent., 2010, pp. 496-504.
7. L. M. Su, B. P. Vagvolgyi, R. Agarwal, C. E. Reiley, R. H. Taylor, and G. D. Hager, "Augmented reality during robot-assisted laparoscopic partial nephrectomy: Toward real-time 3-D-CT to stereoscopic video registration," Urology, vol. 73, no. 4, pp. 896-900, 2009.
8. O. Ukimura and I. S. Gill, "Image-fusion, augmented reality, and predictive surgical navigation," Urol. Clin. N. Am., vol. 36, no. 2, pp. 115-123, 2009.
9. I. S. Gill et al., "Complications of laparoscopic nephrectomy in 185 patients: A multi-institutional review," J. Urol., vol. 154, pp. 479-483, 1995.
10. D. J. Mirota, J. Ishii, and G. D. Hager, "Vision-based navigation in image-guided interventions," Annu. Rev. Biomed. Eng., vol. 13, no. 1, pp. 297-319, 2011.
11. P. Mountney, D. Stoyanov, and G. Z. Yang, "Three-dimensional tissue deformation recovery and tracking," IEEE Signal Process. Mag., vol. 27, no. 4, pp. 14-24, 2010.
12. V. Lepetit and P. Fua, "Monocular model-based 3-d tracking of rigid objects: A survey," Found. Trends Comput. Graph. Vis., vol. 1, pp. 1-89, 2005.
13. R. Richa, A. P. L. Bo, and P. Poignet, "Towards robust 3-D visual tracking for motion compensation in beating heart surgery," Med. Image Anal., vol. 15, no. 3, pp. 3012-315, 2010.
14. S. Giannarou, M. Visentini-Scarzanella, and G. Yang, "Probabilistic tracking of affine-invariant anisotropic regions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 130-143, Jan. 2013.
15. M.Yip,D. Lowe, S. Salcudean, R. Rohling, and C. Nguan, "Real-time methods for long-term tissue feature tracking in endoscopic scenes," Inf. Process. Comput.-Assist. Intervent., pp. 33-43, 2012.
16. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comp. Vis., vol. 60, no. 2, pp. 91-110, 2004.
17. D. Stoyanov, "Surgical vision," Ann. Biomed. Eng., vol. 40, no. 2, pp. 332-345, 2012.
18. D. Cohen, E. Mayer, D. Chen, A. Anstee, J. Vale, G. Z. Yang, A. Darzi, and P. Edwards, "Augmented reality image guidance in minimally invasive prostatectomy," Prostate Cancer Imag. Computer-Aided Diagnosis, Prognosis, Intervent., pp. 101-110, 2010.
19. B. Vagvolgyi, L.-M. Su, R. Taylor, and G. D. Hager, "Video to CT registration for image overlay on solid organs," in 4th Workshop Augment. Environ. Med. Imag. Comput.-Aid. Surg., 2008, pp. 78-86.
20. T. Simpfendorfer, M. Baumhauer, M. Muller, C. N. Gutt, H. P. Meinzer, J. J. Rassweiler, S. Guven, and D. Teber, "Augmented reality visualization during laparoscopic radical prostatectomy," J. Endourol., vol. 25, no. 12, pp. 1841-1845, 2011.
21. E. H. William, P. H. James, L. Kongkuo, A. M. Scott, R. Lav, and Y. Kun-Chang, "3-D CT-video fusion for image-guided bronchoscopy," Comput. Med. Imag. Graph., vol. 32, no. 3, pp. 159-173, 2008.
22. G. A. Puerto-Souza, M. Adibi, J. A. Cadeddu, and G. L. Mariottini, "Adaptive multi-affine (AMA) feature-matching algorithm and its application to minimally-invasive surgery images," in Proc. IEEE/RSJ Int. Conf. Intel. Robots Syst., Sep. 2011, pp. 2371-2376.
23. M. Cho, J. Lee, and K. M. Lee, "Feature correspondence and deformable object matching via agglomerative correspondence clustering," in Proc. 9th Int. Conf. Comput. Vis., 2009, pp. 1280-1287.
24. J. M. Morel and G. Yu, "Asift: A new framework for fully affine invariant image comparison," J. Imag. Sci., vol. 2, no. 2, pp. 438-469, 2009.
25. H. Bay, T. Tuytelaars, and L. Van Gool, "SURF: Speeded up robust features," in Proc. Eur. Comput. Vis. Conf., 2006, pp. 404-417. (Pubitemid 44029795)
26. E. Tola, V. Lepetit, and P. Fua, "Daisy: An efficient dense descriptor applied to wide-baseline stereo," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 5, pp. 815-830, May 2010.
27. E.Rosten, R. Porter, and T. Drummond, "Faster and better:Amachine learning approach to corner detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 105-119, Jan. 2010.
28. C. Liu, J. Yuen, and A. Torralba, "Sift flow: Dense correspondence across scenes and its applications," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 978-994, May 2011.
29. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615-1630, Oct. 2005. (Pubitemid 41555406)
30. K.Mikolajczyk and C. Schmid, "Scale & affine invariant interest point detectors," Int. J. Comput. Vis., vol. 60, no. 1, pp. 63-86, 2004.
31. F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, "3-D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints," in Proc. IEEE/RSJ Int. Conf. Intel. Robots Syst., 2011, vol. 66, pp. 231-259.
32. A. C. Berg, T. L. Berg, and J.Malik, "Shapematching and object recognition using low distortion correspondences," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2005, vol. 1, pp. 26-33.
33. H. Li, E. Kim, X. Huang, and L. He, "Object matching with a locally affine-invariant constraint," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1641-1648.
34. L. Torresani, V. Kolmogorov, and C. Rotherm, "Feature correspondence via graph matching: Models and global optimization," in Proc. Eur. Comput. Vis. Conf., 2008, vol. 2, pp. 596-609.
35. A. Del Bimbo, F. Franco, and F. Pernici, "Local shape estimation from a single keypoint," in Proc. Comput. Vis. Pattern Rec. Workshops, 2010, pp. 23-28.
36. R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge, U.K.: Cambridge Univ. Press, 2000.
37. G. A. Puerto-Souza and G. L. Mariottini, "A comparative study of correspondence-search algorithms in MIS images," in Proc. 15th Int. Conf. Med. Image Comp. Comp-Assit. Intervent., 2012, pp. 625-633.
38. G. A. Puerto-Souza and G. L. Mariottini, "Hierarchical multi-affine (HMA) algorithm for fast and accurate feature matching in minimallyinvasive surgical images," in Proc. IEEE/RSJ Int. Conf. Intel. Robots Syst., 2012.
39. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
40. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol. 60, pp. 1615-1630, 2005. (Pubitemid 41555406)
41. P. Mountney, B. Lo, S. Thiemjarus, D. Stoyanov, and G.-Z. Yang, "A probabilistic framework for tracking deformable soft tissue in minimally invasive surgery," Med. Image Comput. Comput.-Assist. Intervent., pp. 34-41, 2007.
42. M. A. Fischler and R. C. Bolles, "Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography," ACM Commun., vol. 24, pp. 381-395, Jun. 1981.
43. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. NewYork: Springer-Verlag, 2001.
44. J. W. Tukey, "Exploratory data analysis," in Reading. MA:, 1977.
45. Hamlyn Centre laparoscopic/endoscopic video datasets [Online]. Available: http://hamlyn.doc.ic.ac.uk/vision
46. Feature detectors and descriptors: The state of the art and beyond [Online]. Available: http://www.featurespace.org
47. E. Herbst, X. Ren, and D. Fox, "Object segmentation from motion with dense feature matching," presented at the IEEE ICRA Workshop Semantic Percept. Mapp. Explorat., St. Paul, MN, 2012.
48. C. Strecha, T. Tuytelaars, and L. Van Gool, "Dense matching of multiple wide-baseline views," in Proc. 9th IEEE Int. Conf. Comput. Vis., 2003, pp. 1194-1201.