Vessel enhancement in digital X-ray angiographic sequences by temporal statistical learning

András Lassó, Emanuele Trucco

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In this paper, we present a vessel enhancement method, SVM temporal filtering (STF), for X-ray angiographic (XA) images using Support Vector Machine (SVM). We show that the linear SVM applied to vessel enhancement can be regarded as a matched linear filter optimizing the contrast-to-noise ratio in XA images. We propose a non-linear kernel function for the SVM leading to good enhancement with noisy, varying grey-level dynamics at vessel pixels. One key advantage over the matched filters is that an optimal filter is learnt from images, not estimated at design stage. Results on clinical XA images show that learning-based enhancement achieves better results compared to simple subtraction and other image stacking methods. © 2005 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)343-355
Number of pages13
JournalComputerized Medical Imaging and Graphics
Volume29
Issue number5
DOIs
Publication statusPublished - Jun 2005

Keywords

  • Image enhancement
  • Learning systems
  • Matched filters
  • Support vector machines
  • X-ray angiography

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