All Title Author
Keywords Abstract

Composite Match Index with Application of Interior Deformation Field Measurement from Magnetic Resonance Volumetric Images of Human Tissues

DOI: 10.1155/2012/135204

Full-Text   Cite this paper   Add to My Lib


Whereas a variety of different feature-point matching approaches have been reported in computer vision, few feature-point matching approaches employed in images from nonrigid, nonuniform human tissues have been reported. The present work is concerned with interior deformation field measurement of complex human tissues from three-dimensional magnetic resonance (MR) volumetric images. To improve the reliability of matching results, this paper proposes composite match index (CMI) as the foundation of multimethod fusion methods to increase the reliability of these various methods. Thereinto, we discuss the definition, components, and weight determination of CMI. To test the validity of the proposed approach, it is applied to actual MR volumetric images obtained from a volunteer’s calf. The main result is consistent with the actual condition. 1. Introduction The physical property is the base of the biological simulation, computer-assisted medical applications, such as clinical diagnosis, and surgical simulation, surgical planning. And estimation of internal deformation field or deformation motion for the biological tissues plays a very significant role in physical parameters estimation. Thus, measuring the internal deformation field of biological tissues is becoming the focus research. Magnetic resonance (MR) imaging (MRI) provides superb anatomic images with excellent spatial resolution and contrasts among soft tissues; thus, it is widely used in computer-assisted medical applications, such as clinical diagnosis, surgery simulation, operation planning, and evaluation of physical characteristics of biological tissues. Increasing number of researchers in medical simulation and medical virtual reality focus on the interior deformation field or motion measurement of biological tissues from MR volumetric images, and it has become one of the significant branches of medical image analysis. Generally, approaches for estimating the deformation of MR volumetric images can be classified into two typical types: elastic deformation model-based and feature matching-based methods. The elastic deformation model-based method can be classified into either parametric or geometric active models [1]. To obtain the deformation information of an object, the parametric active contours, also called snakes, try to minimize a defined cost function so that the function deforms a given initial contour toward the boundary of the object. This method was first introduced by Kass et al. in 1987 [2] and subsequently developed and used by Lang et al. [3], Cho and Benkeser [4], and


[1]  Y. Chenoune, E. Deléchelle, E. Petit, T. Goissen, J. Garot, and A. Rahmouni, “Segmentation of cardiac cine-MR images and myocardial deformation assessment using level set methods,” Computerized Medical Imaging and Graphics, vol. 29, no. 8, pp. 607–616, 2005.
[2]  M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, 1988.
[3]  J. Lang, D. K. Pai, and R. J. Woodham, “Robotic acquisition of deformable models,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '02), vol. 1, pp. 933–938, May 2002.
[4]  J. Cho and P. J. Benkeser, “Elastically deformable model-based motion-tracking of left ventricle,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), vol. 1, pp. 1925–1928, September 2004.
[5]  B. J. Matuszewski, J. K. Shen, L. K. Shark, and C. J. Moore, “Estimation of internal body deformations using an elastic registration technique,” in Proceedings of the International Conference on Medical Information Visualisation-BioMedical Visualisation (MediVis '06), pp. 15–20, July 2006.
[6]  C. Vicent, C. Francine, C. Tomeu, and D. Francoise, “A geometric model for active contours in image processing,” Numerische Mathematik, vol. 66, no. 1, pp. 1–31, 1993.
[7]  R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: a level set approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158–175, 1995.
[8]  C. Vicent, “Geometric models for active contours,” in Proceedings of the IEEE International Conference on Image Processing, vol. 3, pp. 9–12, October 1995.
[9]  F. Huang and J. Su, “Face contour detection using geometric active contours,” in Proceedings of the 4th World Congress on Intelligent Control and Automation, vol. 3, pp. 2090–2093, Shanghai, China, June 2002.
[10]  H. Chui and A. Rangarajan, “New algorithm for non-rigid point matching,” in proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), pp. 44–51, June 2000.
[11]  Y. Zheng and D. Doermann, “Robust point matching for nonrigid shapes by preserving local neighborhood structures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 643–649, 2006.
[12]  S. Belongie, J. Malik, and J. Puzicha, “Shape matching and object recognition using shape contexts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509–522, 2002.
[13]  A. Myronenko, X. Song, and M. A. Carreira-Perpinan, “Nonrigid point set registration: coherent point drift,” Advances in Neural Information Processing Systems, vol. 19, pp. 1009–1016, 2007.
[14]  A. Myronenko and X. Song, “Point set registration: coherent point drifts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2262–2275, 2010.
[15]  O. Choi and I. S. Kweon, “Robust feature point matching by preserving local geometric consistency,” Computer Vision and Image Understanding, vol. 113, no. 6, pp. 726–742, 2009.
[16]  X. Papademetris, A. P. Jackowski, R. T. Schultz, L. H. Staib, and J. S. Duncan, “Integrated intensity and point-feature nonrigid registration,” in Proceedings of the 7th International Conference of Medical Image Computing and Computer-Assisted Intervention (MICCAI '04), vol. 3216, pp. 763–770, September 2004.
[17]  P. Zhang, S. Hirai, and K. Endo, “A feature matching-based approach to deformation fields measurement from MR images of non-rigid object,” International Journal of Innovative Computing, Information and Control, vol. 4, no. 7, pp. 1607–1615, 2008.
[18]  P. Zhang, S. Hirai, K. Endo, and S. Morikawa, “Local deformation measurement of biological tissues based on feature tracking of 3D MR volumetric images,” in Proceedings of the IEEE/ICME International Conference on Complex Medical Engineering (CME '07), pp. 707–712, Beijin, China, May 2007.
[19]  P. L. Zhang and S. Hirai, “A local geometric preserving approach for interior deformation fields measurement from MR volumetric images of human tissues,” in Proceeding of the IEEE International Conference on Robotics and Biomimetics, pp. 437–441, 2010.
[20]  P. L. Zhang, S. Hirai, and K. Endo, “A method for non-rigid 3D deformation fields measurement: application to human calf MR volumetric images,” in Proceedings of the Workshop at IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 8–13, 2007.
[21]  C. Harris and M. J. Stephens, “A combined corner and edge detector,” in Proceedings of the 4th Alvey Vision Conference, pp. 147–151, 1988.
[22]  P. L. Zhang, S. Hirai, and K. Endo, “A feature tracking-based approach for local deformation fields measurement of biological tissue from MR volumes,” in Proceedings of the 3rd Joint Workshop on Machine Perception and Robotics, December 2007.
[23]  G. Q. Chen, “Robust point feature matching in projective space,” in Proceedings of the Robust Point Feature Matching in Projective Space, pp. 717–722, December 2001.


comments powered by Disqus