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Lookup NU author(s): Professor Boguslaw ObaraORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2019. Quantification and modelling of curvilinear structures in 2D and 3D images is a common challenge in a wide range of biomedical applications. Image enhancement is a crucial pre-processing step for curvilinear structure quantification. Many of the existing state-of-the-art enhancement approaches still suffer from contrast variations and noise. In this paper, we propose to address such problems via the use of a multiscale image processing approach, called Multiscale Top-Hat Tensor (MTHT). MTHT produces a better quality enhancement of curvilinear structures in low contrast and noisy images compared with other approaches in a range of 2D and 3D biomedical images. The proposed approach combines multiscale morphological filtering with a local tensor representation of curvilinear structure. The MTHT approach is validated on 2D and 3D synthetic and real images, and is also compared to the state-of-the-art curvilinear structure enhancement approaches. The obtained results demonstrate that the proposed approach provides high-quality curvilinear structure enhancement, allowing high accuracy segmentation and quantification in a wide range of 2D and 3D image datasets.
Author(s): Alharbi SS, Sazak C, Nelson CJ, Alhasson HF, Obara B
Publication type: Article
Publication status: Published
Journal: Methods
Year: 2020
Volume: 173
Pages: 3-15
Print publication date: 15/02/2020
Online publication date: 07/06/2019
Acceptance date: 30/05/2019
Date deposited: 09/05/2021
ISSN (print): 1046-2023
ISSN (electronic): 1095-9130
Publisher: Academic Press Inc.
URL: https://doi.org/10.1016/j.ymeth.2019.05.025
DOI: 10.1016/j.ymeth.2019.05.025
PubMed id: 31176770
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