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Assessment of image features for vessel wall segmentation in intravascular ultrasound images

  • Lucas Lo Vercio
  • José Ignacio Orlando
  • Mariana del Fresno
  • Ignacio Larrabide
Original Article

Abstract

Background

Intravascular ultrasound (IVUS) provides axial greyscale images, allowing the assessment of the vessel wall and the surrounding tissues. Several studies have described automatic segmentation of the luminal boundary and the media–adventitia interface by means of different image features.

Purpose

The aim of the present study is to evaluate the capability of some of the most relevant state-of-the-art image features for segmenting IVUS images. The study is focused on Volcano 20 MHz frames not containing plaque or containing fibrotic plaques, and, in principle, it could not be applied to frames containing shadows, calcified plaques, bifurcations and side vessels.

Methods

Several image filters, textural descriptors, edge detectors, noise and spatial measures were taken into account. The assessment is based on classification techniques previously used for IVUS segmentation, assigning to each pixel a continuous likelihood value obtained using support vector machines (SVMs). To retrieve relevant features, sequential feature selection was performed guided by the area under the precision–recall curve (AUC-PR).

Results

Subsets of relevant image features for lumen, plaque and surrounding tissues characterization were obtained, and SVMs trained with these features were able to accurately identify those regions. The experimental results were evaluated with respect to ground truth segmentations from a publicly available dataset, reaching values of AUC-PR up to 0.97 and Jaccard index close to 0.85.

Conclusion

Noise-reduction filters and Haralick’s textural features denoted their relevance to identify lumen and background. Laws’ textural features, local binary patterns, Gabor filters and edge detectors had less relevance in the selection process.

Keywords

IVUS Vessel wall Segmentation Feature selection SVM 

Notes

Acknowledgments

The present work has been partially funded by the National Agency for Science and Technology Promotion (ANPCyT, Argentina) within the projects PICT 2010-1287 and PICT 2014-1730.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest.

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Copyright information

© CARS 2016

Authors and Affiliations

  • Lucas Lo Vercio
    • 1
    • 2
  • José Ignacio Orlando
    • 1
    • 2
  • Mariana del Fresno
    • 1
    • 3
  • Ignacio Larrabide
    • 1
    • 2
  1. 1.Pladema, UNICENTandilArgentina
  2. 2.CONICETTandilArgentina
  3. 3.CIC-PBATandilArgentina

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