Curvilinear Structure Enhancement by Multiscale Top-Hat Tensor in 2D/3D Images
IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, İspanya, 3 - 06 Aralık 2018, ss.814-822, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası:
- Basıldığı Şehir: Madrid
- Basıldığı Ülke: İspanya
- Sayfa Sayıları: ss.814-822
- Anahtar Kelimeler: 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 Üniversitesi Adresli: Hayır
Özet
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.