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Enhancing In-Vitro IVUS Data for Tissue Characterization

  • Francesco Ciompi
  • Oriol Pujol
  • Oriol Rodriguez Leor
  • Carlo Gatta
  • Angel Serrano Vida
  • Petia Radeva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5524)

Abstract

Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%.

Keywords

Intravascular Ultrasounds Plaque characterization Semi-supervised learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francesco Ciompi
    • 1
  • Oriol Pujol
    • 1
  • Oriol Rodriguez Leor
    • 3
  • Carlo Gatta
    • 2
  • Angel Serrano Vida
    • 4
  • Petia Radeva
    • 1
  1. 1.Dep. of Applied Mathematics and AnalysisUniversity of BarcelonaSpain
  2. 2.Computer Vision Center, Campus UABBellaterraSpain
  3. 3.Hospital Universitari “Germans Trias i Pujol”BadalonaSpain
  4. 4.Hospital General de GranollersGranollersSpain

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