  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.
