The detection of vascular structures from noisy images is a fundamental process for extracting meaningful information in many applications. Most well-known vascular enhancing techniques often rely on Hessian-based filters. This paper investigates the feasibility and deficiencies of detecting curve-like structures using a Hessian matrix. The main contribution is a novel enhancement function, which overcomes the deficiencies of established methods. Our approach has been evaluated quantitatively and qualitatively using synthetic examples and a wide range of real 2D and 3D biomedical images. Compared with other existing approaches, the experimental results prove that our proposed approach achieves high-quality curvilinear structure enhancement.

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 highquality curvilinear structure enhancement in synthetic examples and in a wide range of 2D and 3D images.

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Curvilinear structure detection and quantification is a large research area with many imaging applications in fields such as biology, medicine, and engineering. Curvilinear enhancement is often used as a pre-processing stage for ridge detection, but there has been little investigation into the relationship between enhancement and ridge detection. In this paper, we thoroughly evaluate the pair-wise combinations of different curvilinear enhancement and ridge detection methods across two highly varied datasets, as well as samples of three other datasets. In particular, we present the approaches complementing one another and the gained insights, which will aid researchers in designing generic ridge detectors.