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

TL;DR

This paper introduces the Multiscale Top-Hat Tensor (MTHT) method for enhancing curvilinear structures in 2D and 3D biomedical images, effectively addressing contrast and noise issues through a multiscale morphological filtering approach.

Contribution

The paper presents a novel multiscale tensor-based method that improves curvilinear structure enhancement in noisy and low-contrast biomedical images.

Findings

Achieves superior enhancement quality compared to existing methods.

Effective in both synthetic and real biomedical image data.

Demonstrates robustness across various imaging conditions.

Abstract

A wide range of biomedical applications requires 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.