Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Segmentation of Dynamic N-D Data Sets via Graph Cuts Using Markov Models
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Registration of Time-Series Contrast Enhanced Magnetic Resonance Images for Renography
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
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Dynamic contrast-enhanced 3D images of the kidneys, or 3D MR renography, has the potential for broad clinical applications, but suffers from respiratory motion that limits analysis and interpretation. Manual registration is prohibitively labor-intensive. In this paper, a fully automated technique, Wavelet Representation and the Fourier Transform (WRFT) method, that corrects for translation and rotation motion in 3D MR renography is presented. The method was composed by anisotropic denoising, wavelet-based feature extraction, and Fourier-based registration. This was first evaluated on a set of simulated MR renography images with defined degrees of kidney motion. The method was then tested on 24 clinical patient MR renography data sets. Results of clinical testing were compared with the results obtained using a mutual information registration method. Based on intrarenal time-intensity curves, our method showed robust and consistent agreement with the results of manually coregistered data sets.