Line detection in speckle images using Radon transform and ℓ1 regularization

Pui Anantrasirichai, Marco Allinovi, Wesley Hayes, David Bull, Alin Achim

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

Boundaries and lines in medical images are important structures as they can delineate between tissue types, organs, and membranes. Although, a number of image enhancement and segmentation methods have been proposed to detect lines, none of these have considered line artefacts, which are more difficult to visualise as they are not physical structures, yet are still meaningful for clinical interpretation. This paper presents a novel method to restore lines, including line artefacts, in speckle images. We address this as a sparse estimation problem using a convex optimisation technique based on a Radon transform and sparsity regularisation (ℓ1 norm). This problem divides into subproblems which are solved using the alternating direction method of multipliers, thereby achieving line detection and deconvolution simultaneously. The results for both simulated and in vivo ultrasound images show that the proposed method outperforms existing methods, in particular for detecting B-lines in lung ultrasound images, where the performance can be improved by up to 30 %
Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6240-6244
Number of pages5
ISBN (Electronic)9781509041176
ISBN (Print)9781509041183
DOIs
Publication statusE-pub ahead of print - 19 Jun 2017

Publication series

Name
ISSN (Print)2379-190X

Keywords

  • ultrasound
  • inverse problem
  • ADMM
  • line detection
  • sparsity regularisation

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    Cite this

    Anantrasirichai, P., Allinovi, M., Hayes, W., Bull, D., & Achim, A. (2017). Line detection in speckle images using Radon transform and ℓ1 regularization. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) (pp. 6240-6244). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICASSP.2017.7953356