@inproceedings{04e039a4845b4eb98cad21a8073b64d7,
title = "Robust guidewire segmentation through boosting, clustering and linear programming",
abstract = "Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional edgebased methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming. In particular, numerous invariant to position/rotation classifiers are combined to detect candidate pixels of curvilinear structure. These candidates are grouped into consistent geometric segments through the use of a state-of-the art unsupervised clustering algorithm. The complete curvilinear structure is obtained through an ordering of these segments using the elastica model in a linear programming framework. Very promising results were obtained on guide wire segmentation in fluoroscopic images.",
keywords = "Boosting, Clustering, Fluoroscopic images, Guide wire, Linear programming, Linear structures, Steerable filters",
author = "Nicolas Honnorat and {\'R}egis Vaillant and Nikos Paragios",
year = "2010",
doi = "10.1109/ISBI.2010.5490138",
language = "English (US)",
isbn = "9781424441266",
series = "2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings",
pages = "924--927",
booktitle = "2010 7th IEEE International Symposium on Biomedical Imaging",
note = "7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 ; Conference date: 14-04-2010 Through 17-04-2010",
}