Extraction and Tracking of MRI Tagging Sheets Using a 3D Gabor Filter Bank

In this paper, we present a novel method for automatically extracting the tagging sheets in tagged cardiac MR images, and tracking their displacement during the heart cycle, using a tunable 3D Gabor filter bank. Tagged MRI is a non-invasive technique for the study of myocardial deformation. We design the 3D Gabor filter bank based on the geometric characteristics of the tagging sheets. The tunable parameters of the Gabor filter bank are used to adapt to the myocardium deformation. The whole 3D image dataset is convolved with each Gabor filter in the filter bank, in the Fourier domain. Then we impose a set of deformable meshes onto the extracted tagging sheets and track them over time. Dynamic estimation of the filter parameters and the mesh internal smoothness are used to help the tracking. Some very encouraging results are shown

[1]  William E. Higgins,et al.  An algorithm for designing multiple Gabor filters for segmenting multi-textured images , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[2]  M. Fernandez,et al.  Texture segmentation of a 3D seismic section with wavelet transform and Gabor filters , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[3]  Jerry L. Prince,et al.  On the design of the bandpass filters in harmonic phase MRI , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[4]  Leon Axel,et al.  Extracting tissue deformation using Gabor filter banks , 2004, SPIE Medical Imaging.

[5]  Dimitris N. Metaxas,et al.  Segmenting cardiac MRI tagging lines using Gabor filter banks , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[6]  Jerry L. Prince,et al.  Angle images for measuring heart motion from tagged MRI , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[7]  Dennis Gabor,et al.  Theory of communication , 1946 .

[8]  J. Bigun.  Speed, frequency, and orientation tuned 3-D Gabor filter banks and their design , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 2 - Conference B: Computer Vision & Image Processing. (Cat. No.94CH3440-5).

[9]  Helge J. Ritter,et al.  A neural 3-D object recognition architecture using optimized Gabor filters , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[10]  Dimitris N. Metaxas,et al.  A Novel Stochastic Combination of 3D Texture Features for Automated Segmentation of Prostatic Adenocarcinoma from High Resolution MRI , 2003, MICCAI.