Segmentation-Based Registration of Organs in Intraoperative Video Sequences
Intraoperative optical imaging of exposed organs in visible, near-infrared, and infrared (IR) wavelengths in the body has the potential to be useful for real-time assessment of organ viability and image guidance during surgical intervention. However, the motion of the internal organs presents significant challenges for fast analysis of recorded 2D video sequences. The movement observed during surgery, due to respiration, cardiac motion, blood flow, and mechanical shift accompanying the surgical intervention, causes organ reflection in the image sequence, making optical measurements for further analysis challenging. Correcting alignment is difficult in that the motion is not uniform over the image. This paper describes a Canny edge-based method for segmentation of the specific organ or region under study, along with a moment-based registration method for the segmented region. Experimental results are provided for a set of intraoperative IR image sequences.
KeywordsVideo Sequence Image Sequence Discrete Fourier Transform Image Registration Canny Edge Detection
Unable to display preview. Download preview PDF.
- 5.Bardera, M., Feixas, I.B.: Normalized similarity measures for medical image registration. In: SPIE Medical Imaging, Proceedings of SPIE, vol. 5370 (2004)Google Scholar
- 6.Pluim, J., Maintz, J., Viergever, M.: Mutual information based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22(8) (2003)Google Scholar
- 8.Maintz, J., van den Elsen, P., Viergever, M.: 3D multimodality image registration using morphological tools. In: Image and Vision Computing, vol. 19. Elsevier, Amsterdam (2001)Google Scholar
- 10.Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: DARPA Image Understanding Workshop, DARPA, pp. 121–130 (1981)Google Scholar
- 12.Davatzikos, C., Prince, J.L., Bryan, R.N.: Image Registration Based on Boundary Mapping. IEEE Transactions on Medical Imaging 15(1) (February 1996)Google Scholar
- 14.Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. I. Addison-Wesley, Reading (1992)Google Scholar