Over the last decade, different micro-tool navigation and localisation algorithms have been developed. They can be divided into three groups: the eigen-decomposition methods such as principal component analysis (PCA), the transform methods such as Hough transform (HT) and the space random iteration methods such as the random sample consensus (RANSAC) algorithm. To suppress the speckle noise of the ultrasound image, different noise suppression methods are also proposed. In this article, different combinations of preprocessing methods and localisation methods are compared. A tracking system is also developed to adapt these localisation methods to a dynamic situation. So far, the line-filter method has achieved the best contrast ratio, and the threshold method requires the shortest time. These two preprocessing methods are proposed in the localisation algorithm and the tracking system. Simulations and experiments were conducted to verify the combinations of the localisation and tracking results. In static localisation, the line-filter+RANSAC method achieves the highest localisation accuracy. In the dynamic situation, the PCA method achieves the highest tracking accuracy. In the real-time evaluation, the calculation time of the RANSAC algorithm is the shortest. To satisfy the demand for localisation accuracy and calculation time, the line-filter+RANSAC algorithm is the best choice.

References

1. 1)
  - 1. Bridal, S., Correas, J.: ‘Milestones on the road to higher resolution, quantitative, and functional ultrasonic imaging’, Proc. IEEE, 2003, 91, (10), pp. 1543–1561 (doi: 10.1109/JPROC.2003.817879).
2. 2)
  - 14. Building, E.: ‘Speckle tracking for multi-dimensional flow estimation’, Ultrasonics, 2000, 38, pp. 369–375 (doi: 10.1016/S0041-624X(99)00182-1).
3. 3)
  - L.M.J. Florack , B.M. ter Haar Romeny , J.J. Koenderink , M.A. Viergever . Scale and the differential structure of images. Image Vis. Comput. , 376 - 388
4. 4)
  - M.A. Fischler , R.C. Bolles . Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM , 381 - 395
5. 5)
  - 9. Jolliffe, I.T.: ‘Principal component analysis’ (Springer, New York, 2002, 2nd ed.), pp. 518.
6. 6)
  - 3. Draper, K.J., Blake, C.C., Gowman, L., Downey, D.B., Fenster, A.: ‘An algorithm for automatic needle localization in ultrasound-guided breast biopsies’, Med. Phys., 2000, 27, (8), pp. 1971–1979 (doi: 10.1118/1.1287437).
7. 7)
  - 10. Yin, S., Li, X., Gao, H., Kaynak, O.: ‘Data-based techniques focused on modern industry: an overview’, IEEE Trans. Ind. Electron., 2014, 0046, (c), pp. 1–11.
8. 8)
  - 19. Ricci, S., Bassi, L., Boni, E., Dallai, A., Tortoli, P.: ‘Multichannel FPGA-based arbitrary waveform generator for medical ultrasound’, Electron. Lett., 2007, 43, (24), pp. 1335–1336 (doi: 10.1049/el:20072859).
9. 9)
  - 6. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: ‘Multiscale vessel enhancement filtering’. Medical Image Computing and Computer-Assisted Interventation—MICCAI’98, 1998, vol. 1496, p. 130–137.
10. 10)
  - 18. Tortoli, P., Bassi, L., Boni, E., Dallai, A., Guidi, F., Ricci, S.: ‘ULA-OP: an advanced open platform for ULtrasound research’, Ultrason. Ferroelectr. Freq. Control. IEEE Trans., 2009, 56, (10), pp. 2207–2216 (doi: 10.1109/TUFFC.2009.1303).
11. 11)
  - 13. Zhao, Y., Cachard, C., Liebgott, H.: ‘Automatic needle detection and tracking in 3D ultrasound using an ROI-based RANSAC and Kalman method’. Ultrasound Imaging, 2013.
12. 12)
  - 15. Lewis, J.P.: ‘Fast normalized cross-correlation’, Vis. Interface, 1995, 10, (1), pp. 120–123.
13. 13)
  - 12. Barva, M., Kybic, J., Mari, J.-M., Cachard, C., Hlavac, V.: ‘Automatic localization of curvilinear object in 3D ultrasound images’, Proc. SPIE, 2005, 5750, pp. 455–462 (doi: 10.1117/12.594763).
14. 14)
  - 5. Uherèík, M., Kybic, J., Liebgott, H., Cachard, C.: ‘Model fitting using RANSAC for surgical tool localization in 3-D ultrasound images’, Biomed. Eng. IEEE Trans., 2010, 57, (8), pp. 1907–1916 (doi: 10.1109/TBME.2010.2046416).
15. 15)
  - 11. Abdi, H., Williams, L.J.: ‘Principal component analysis’, Wiley Interdiscip. Rev. Comput. Stat., 2010, 2, (4), pp. 433–459 (doi: 10.1002/wics.101).
16. 16)
  - A. Noble , D.L. Boukerroui . Ultrasound image segmentation: a survey. IEEE Trans. Med. Imag. , 8 , 987 - 1010
17. 17)
  - 4. Ding, M., Fenster, A.: ‘A real-time biopsy needle segmentation technique using Hough Transform’, Med. Phys., 2003, 30, (8), p. 2222 (doi: 10.1118/1.1591192).
18. 18)
  - 16. Jensen, J.: ‘Field: A program for simulating ultrasound systems’. Conf. Biomed. IMAGING, 1996, vol. 4, no. 34, pp. 351–353.
19. 19)
  - 17. Jensen, J., Svendsen, N.B.: ‘Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers’, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 1992, 39, (2), pp. 262–267 (doi: 10.1109/58.139123).

Errata

An Erratum has been published for this content:

Erratum: Comparison of the existing tool localisation methods on two-dimensional ultrasound images and their tracking results

Comparison of the existing tool localisation methods on two-dimensional ultrasound images and their tracking results

References

Related content