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

Alharbi, Shuaa S.; Sazak, Cigdem; Alhasson, Haifa; Nelson, Carl J; Obara, Boguslaw

Abstract

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.

Citation

Alharbi, S. S., Sazak, C., Alhasson, H., Nelson, C. J., & Obara, B. (2020). The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images. Methods, 173, 3-15. https://doi.org/10.1016/j.ymeth.2019.05.025

• Journal Article Type: Article
• Acceptance Date: May 30, 2019
• Online Publication Date: Jun 7, 2019
• Publication Date: Feb 15, 2020
• Deposit Date: May 30, 2019
• Publicly Available Date: Jun 7, 2020
• Journal: Methods
• Print ISSN: 1046-2023
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 173
• Pages: 3-15
• DOI: https://doi.org/10.1016/j.ymeth.2019.05.025
• Public URL: https://durham-repository.worktribe.com/output/1329705