Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images
This addresses the need for better curvilinear structure enhancement in biomedical imaging, but it appears incremental as it builds on existing multiscale and morphological filtering techniques.
The paper tackles the problem of enhancing curvilinear structures in 2D and 3D biomedical images, which suffer from contrast variations and noise, by proposing the Multiscale Top-Hat Tensor (MTHT) approach, achieving high-quality enhancement validated on synthetic and real data compared to state-of-the-art methods.
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.