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The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images

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.

Publication metadata

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.


DOI: 10.1016/j.ymeth.2019.05.025

PubMed id: 31176770


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