FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Combining Growcut and temporal correlation for IVUS lumen segmentation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A holistic approach for the detection of media-adventitia border in IVUS
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
IVUS-histology image registration
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Probabilistic segmentation of the lumen from intravascular ultrasound radio frequency data
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
An IVUS image-based approach for improvement of coronary plaque characterization
Computers in Biology and Medicine
Shape prior model for media-adventitia border segmentation in IVUS using graph cut
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-density-based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.