Line detection in speckle images using Radon transform and ℓ1 regularization

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 (ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> 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 %.