TY - GEN
T1 - Probabilistic segmentation of the lumen from intravascular ultrasound radio frequency data
AU - Gerardomendizabal-Ruiz, E.
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.
PY - 2012
Y1 - 2012
N2 - Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. In this paper, we present a method for the segmentation of the luminal border using IVUS radio frequency (RF) data. Specifically, we parameterize the lumen contour using Fourier series. This contour is deformed by minimizing a cost function that is formulated using a probabilistic approach in which the a priori term is obtained using the prediction confidence of a Support Vector Machine classifier using features extracted from the RF signal. We evaluated the performance of our method by comparing our results with manual segmentations from two expert observers on 280 frames from eight 40 MHz IVUS sequences from rabbits and pigs. The performance was evaluated using the Dice similarity coefficient, coefficient of determination, and linear regressions of the lumen area for each frame. Our results indicate the feasibility of our method for the segmentation of the lumen from IVUS RF data.
AB - Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. In this paper, we present a method for the segmentation of the luminal border using IVUS radio frequency (RF) data. Specifically, we parameterize the lumen contour using Fourier series. This contour is deformed by minimizing a cost function that is formulated using a probabilistic approach in which the a priori term is obtained using the prediction confidence of a Support Vector Machine classifier using features extracted from the RF signal. We evaluated the performance of our method by comparing our results with manual segmentations from two expert observers on 280 frames from eight 40 MHz IVUS sequences from rabbits and pigs. The performance was evaluated using the Dice similarity coefficient, coefficient of determination, and linear regressions of the lumen area for each frame. Our results indicate the feasibility of our method for the segmentation of the lumen from IVUS RF data.
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U2 - 10.1007/978-3-642-33418-4_56
DO - 10.1007/978-3-642-33418-4_56
M3 - Conference contribution
AN - SCOPUS:84988872439
SN - 9783642334177
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 454
EP - 461
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
A2 - Lu, Le
A2 - Criminisi, Antonio
A2 - Menze, Bjoern H.
A2 - Montillo, Albert
A2 - Langs, Georg
A2 - Langs, Georg
A2 - Menze, Bjoern H.
A2 - Tu, Zhuowen
A2 - Ayache, Nicholas
A2 - Delingette, Hervé
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer-Verlag
T2 - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Y2 - 5 October 2012 through 5 October 2012
ER -