Enhancing In-Vitro IVUS Data for Tissue Characterization

Authors:
Francesco Ciompi;Oriol Pujol;Oriol Rodriguez Leor;Carlo Gatta;Angel Serrano Vida;Petia Radeva
Affiliations:
Dep. of Applied Mathematics and Analysis, University of Barcelona, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona, Spain;Hospital Universitari "Germans Trias i Pujol", Badalona, Spain;Computer Vision Center, Campus UAB, Bellaterra, Spain;Hospital General de Granollers, Granollers, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona, Spain
Venue:
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Year:
2009

Citing 5
Cited 1

Floating search methods in feature selection

Pattern Recognition Letters
Advanced Topics in Digital Signal Processing

Advanced Topics in Digital Signal Processing
Reducing multiclass to binary: a unifying approach for margin classifiers

The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
In-Vivo IVUS tissue classification: a comparison between RF signal analysis and reconstructed images

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications

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

Quantified Score

Hi-index	0.01

Visualization

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%.