Three-dimensional reconstruction and fusion for multi-modality spinal images.

Medical diagnosis can benefit from the complementary information in different modality images. Multi-modal image registration and fusion is an essential task in numerous three-dimensional (3D) medical image-processing applications. Registered images are not only providing more correlative information to aid in diagnosis, but also assisting with the planning and monitoring of both surgery and radiotherapy. This research is directed at registering different images captured from Computed Tomography (CT) and Magnetic Resonance (MR) imaging devices, respectively, to acquire more thorough information for disease diagnosis. Because MR bone model segmentation is difficult, this research used a 3D model obtained from CT images. This model accomplishes image registration by optimizing the gradient information accumulated around the bony boundary areas with respect to the 3D model. This system involves pre-processing, 2D segmentation, 3D registration, fusion and sub-system rendering. This method provides desired image operation, robustness verification, and multi-modality spinal image registration accuracy. The proposed system is useful in observing the foramen and nerve root. Because the registration can be performed without external markers, a better choice for clinical usage is provided for lumbar spine diagnosis.

[1]  Jayaram K. Udupa,et al.  Fast visualization, manipulation, and analysis of binary volumetric objects , 1991, IEEE Computer Graphics and Applications.

[2]  S Napel,et al.  Quantifying MRI geometric distortion in tissue , 1994, Magnetic resonance in medicine.

[3]  Yaorong Ge,et al.  Surface-based 3D image registration using the iterative closest-point algorithm with a closest-point transform , 1996, Medical Imaging.

[4]  Benoit M. Dawant,et al.  Registration of CT and MR brain images using a combination of points and surfaces , 1995, Medical Imaging.

[5]  Colin Studholme,et al.  Automated 3-D registration of MR and CT images of the head , 1996, Medical Image Anal..

[6]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[7]  C R Ramsey,et al.  Magnetic resonance imaging based digitally reconstructed radiographs, virtual simulation, and three-dimensional treatment planning for brain neoplasms. , 1998, Medical physics.

[8]  Max A. Viergever,et al.  Automatic registration of CT and MR brain images using correlation of geometrical features , 1995, IEEE Trans. Medical Imaging.

[9]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[10]  Paul F. Hemler,et al.  Grey value correlation techniques used for automatic matching of CT and MR brain and spine images , 1994, Other Conferences.

[11]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[12]  Calvin R. Maurer,et al.  Design of fiducials for accurate registration of CT and MR volume images , 1995, Medical Imaging.

[13]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..

[14]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[15]  B. Dawant,et al.  Effect of geometrical distortion correction in MR on image registration accuracy. , 1996, Journal of computer assisted tomography.

[16]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[17]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[18]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[19]  William H. Press,et al.  Numerical recipes in C , 2002 .