Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Statistical 3D Vessel Segmentation Using a Rician Distribution
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Fast Extraction of Tubular and Tree 3D Surfaces with Front Propagation Methods
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Automatic cerebrovascular segmentation by accurate probabilistic modeling of TOF-MRA images
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II
IEEE Transactions on Information Technology in Biomedicine
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In this paper, we present a supervised statistical-based cerebrovascular segmentation method from Time-Of-Flight MRA. The novelty of this method is that rather than model the dataset over the entire intensity range, we at first use a low threshold to eliminate the lowest intensity region, and then use two uniform distributions to model the middle and high intensity regions, respectively. Subsequently, in order to overcome the intensity overlap between subcutaneous fat and arteries, a high order multiscale features based energy function is introduced to enhance the segmentation. Comparing with those sole intensity based segmentation method the newly proposed algorithm can solve the problem of the regional intensity variation of TOF-MRA well and improve the quality of segmentation. The experimental results also show that the proposed method can provide a better quality segmentation than sole intensity information used method.