Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

  • E. Gerardo Mendizabal-Ruiz
  • Ioannis A. Kakadiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)


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.


Support Vector Machine Model Wavelet Packet Intravascular Ultrasound Manual Segmentation Dice Similarity Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • E. Gerardo Mendizabal-Ruiz
    • 1
  • Ioannis A. Kakadiaris
    • 1
  1. 1.Computational Biomedicine Lab, Departments of Computer Science, Electrical and Computer Engineering, and Biomedical EngineeringUniversity of HoustonHoustonUSA

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