Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Real-time gating of IVUS sequences based on motion blur analysis: method and quantitative validation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images
IEEE Transactions on Information Technology in Biomedicine
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Automatic non-rigid temporal alignment of IVUS sequences
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The assessment of arterial luminal area, performed by IVUS analysis, is a clinical index used to evaluate the degree of coronary artery disease. In this paper we propose a novel approach to automatically segment the vessel lumen, which combines model-based temporal information extracted from successive frames of the sequence, with spatial classification using the Growcut algorithm. The performance of the method is evaluated by an in vivo experiment on 300 IVUS frames. The automatic and manual segmentation performances in general vessel and stent frames are comparable. The average segmentation error in vessel, stent and bifurcation frames are 0.17 ± 0.08 mm, 0.18 ± 0.07 mm and 0.31 ± 0.12 mm respectively.