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
Towards extra-luminal blood detection from intravascular ultrasound radio frequency data
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images
IEEE Transactions on Information Technology in Biomedicine
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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.