Spatio-temporal Image Tracking Based on Optical Flow and Clustering: An Endoneurosonographic Application

  • Andrés F. Serna-Morales
  • Flavio Prieto
  • Eduardo Bayro-Corrochano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6437)


On the process of render brain tumors from endoneurosonography, one of the most important steps consists in track the axis line of an ultrasound probe throughout successive endoscopic images. Recognizing of this line is important because it allows computing its 3D coordinates using the projection matrix of the endoscopic cameras. In this paper we present a method to track an ultrasound probe in successive endoscopic images without relying on any external tracking system. The probe is tracked using a spatio-temporal technique based on optical flow and clustering algorithm. Firstly, we compute the optical flow using the Horn-Schunck algorithm. Secondly, a feature space using the optical flow magnitude and luminance is defined. Thirdly, feature space is partitioned in two regions using the k-means clustering algorithm. After this, we calculate the axis line of the ultrasound probe using Principal Component Analysis (PCA) over segmented region. Finally, a motion restriction is defined over consecutive frames in order to avoid tracking errors. We have used endoscopic images from brain phantoms to evaluate the performance of the proposed method, we compare our methodology against ground truth and a based–color particle filter, and our results show that it is robust and accurate.


Endoneurosonography (ENS) endoscopic images tracking ultrasound probe optical flow clustering Principal Component Analysis (PCA) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Galic, S., Loncaric, S.: Spatio-temporal image segmentation using optical flow and clustering algorithm. In: Proceedings of the First International Workshop on Image and Signal Processing and Analysis, IWISPA 2000, pp. 63–68 (2000)Google Scholar
  2. 2.
    Gillams, A.: 3d imaging-a clinical perspective. In: IEE Colloquium on 3D Imaging Techniques for Medicine, pp. 111–112 (18, 1991)Google Scholar
  3. 3.
    Gobbi, D.G., Comeau, R.M., Lee, B.K.H., Peters, T.M.: Integration of intra-operative 3d ultrasound with pre-operative mri for neurosurgical guidance. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 1738–1740 (2000)Google Scholar
  4. 4.
    Gonzales, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using MATLAB, 2nd edn. Gatesmark Publishing (2009)Google Scholar
  5. 5.
    Haibo, G., Wenxue, H., Jianxin, C., Yonghong, X.: Optimization of principal component analysis in feature extraction. In: International Conference on Mechatronics and Automation, ICMA 2007, pp. 3128–3132 (5-8, 2007)Google Scholar
  6. 6.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  7. 7.
    Jähne, B.: Digital Image Processing, 5th edn. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Machucho-Cadena, R., de la Cruz-Rodriguez, S., Bayro-Corrochano, E.: Rendering of brain tumors using endoneurosonography. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (8-11, 2008)Google Scholar
  9. 9.
    Na, S., Xumin, L., Yong, G.: Research on k-means clustering algorithm: An improved k-means clustering algorithm. In: Third International Symposium on Intelligent Information Technology and Security Informatics, IITSI 2010, pp. 63–67 (2-4, 2010)Google Scholar
  10. 10.
    Nummiaro, K., Koller-Meier, E., Van Gool, L.: An adaptive color-based particle filter. Image Vision Computing 21(1), 99–110 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Ortegon-Aguilar, J., Bayro-Corrochano, E.: Omnidirectional vision tracking with particle filter. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1115–1118 (2006)Google Scholar
  12. 12.
    Roberts, D.W., Hartov, A., Kennedy, F.E., Hartov, E., Miga, M.I., Paulsen, K.D.: Intraoperative brain shift and deformation: A quantitative analysis of cortical displacement in 28 cases (1998)Google Scholar
  13. 13.
    Seber, G.A.F.: Multivariate Observations. John Wiley & Sons, Inc., Hoboken (1984)CrossRefzbMATHGoogle Scholar
  14. 14.
    Tatar, F., Mollinger, J.R., Den Dulk, R.C., van Duyl, W.A., Goosen, J.F.L., Bossche, A.: Ultrasonic sensor system for measuring position and orientation of laproscopic instruments in minimal invasive surgery. In: 2nd Annual International IEEE-EMB Special Topic Conference on Microtechnologies in Medicine Biology, pp. 301–304 (2002)Google Scholar
  15. 15.
    Varshney, S.S., Rajpal, N., Purwar, R.: Comparative study of image segmentation techniques and object matching using segmentation. In: International Conference on Methods and Models in Computer Science, ICM2CS 2009, pp. 1–6 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrés F. Serna-Morales
    • 1
  • Flavio Prieto
    • 2
  • Eduardo Bayro-Corrochano
    • 3
  1. 1.Department of Electrical, Electronic and Computer EngineeringUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Department of Mechanical and Mechatronics EngineeringUniversidad Nacional de ColombiaBogotáColombia
  3. 3.CINVESTAVUnidad GuadalajaraZapopanMéxico

Personalised recommendations