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

Alharbi S. S., Sazak C., Nelson C. J., Obara B.

IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 3 - 06 December 2018, pp.814-822 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • City: Madrid
  • Country: Spain
  • Page Numbers: pp.814-822
  • Keywords: Curvilinear Structures, Image Enhancement, Mathematical Morphology, Top-Hat, Tensor Representation, Vesselness, Neuriteness, RETINAL VESSEL SEGMENTATION, MATHEMATICAL MORPHOLOGY, ORIENTATION RESPONSES, RANKING, FILTER, 3D
  • Recep Tayyip Erdoğan University Affiliated: No

Abstract

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