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Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images

Lookup NU author(s): Professor Boguslaw ObaraORCiD


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© 2018 IEEE. A wide range of biomedical applications require enhancement, detection, quantification and modelling of curvilinear structures in 2D and 3D images. Curvilinear structure enhancement is a crucial step for further analysis, but many of the enhancement approaches still suffer from contrast variations and noise. This can be addressed using a multiscale approach that produces a better quality enhancement for low contrast and noisy images compared with a single-scale approach in a wide range of biomedical images. Here, we propose the Multiscale Top-Hat Tensor (MTHT) approach, which combines multiscale morphological filtering with a local tensor representation of curvilinear structures in 2D and 3D images. The proposed approach is validated on synthetic and real data, and is also compared to the state-of-the-art approaches. Our results show that the proposed approach achieves high-quality curvilinear structure enhancement in synthetic examples and in a wide range of 2D and 3D images.

Publication metadata

Author(s): Alharbi SS, Sazak C, Nelson CJ, Obara B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Bioinformatics and Biomedicine (BIBM 2018)

Year of Conference: 2019

Pages: 814-822

Online publication date: 24/01/2019

Acceptance date: 02/04/2018

Publisher: IEEE


DOI: 10.1109/BIBM.2018.8621329

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538654880