Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Engineering a freehand 3D ultrasound system
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Adaptive Stochastic Gradient Descent Optimisation for Image Registration
International Journal of Computer Vision
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Carotid artery and jugular vein tracking and differentiation using spatiotemporal analysis
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Optimization of mutual information for multiresolution image registration
IEEE Transactions on Image Processing
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We propose a methodology to register medical images of carotid arteries from tracked freehand sweep B-Mode ultrasound (US) and magnetic resonance imaging (MRI) acquisitions. Successful registration of US and MR images will allow a multimodal analysis of atherosclerotic plaque in the carotid artery. The main challenge is the difference in the positions of the patient's neck during the examinations. While in MRI the patient's neck remains in a natural position, in US the neck is slightly bent and rotated. Moreover, the image characteristics of US and MRI around the carotid artery are very different. Our technique uses the estimated centerlines of the common, internal and external carotid arteries in each modality as landmarks for registration. For US, we used an algorithm based on a rough lumen segmentation obtained by robust ellipse fitting to estimate the lumen centerline. In MRI, we extract the centerline using a minimum cost path approach in which the cost is defined by medialness and an intensity based similarity term. The two centerlines are aligned by an iterative closest point (ICP) algorithm, using rigid and thin-plate spline transformation models. The resulting point correspondences are used as a soft constraint in a subsequent intensity-based registration, optimizing a weighted sum of mutual information between the US and MRI and the Euclidean distance between corresponding points. Rigid and B-spline transformation models were used in this stage. Experiments were performed on datasets from five healthy volunteers. We compared different registration approaches, in order to evaluate the necessity of each step, and to establish the optimum algorithm configuration. For the validation, we used the Dice similarity index to measure the overlap between lumen segmentations in US and MRI.