2018/09/23 by Shuaa S. Alharbi, Çiğdem Sazak, Cigdem Sazak +8
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Glaucoma and retinal disorders #Medical Image Segmentation Techniques #Retinal Diseases and Treatments #Retinal Imaging and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1809.08678
openalex publication_date 2018/09/23 · arxiv created 2019/01/04 · arxiv updated 2019/01/08 · openalex created_date 2022/08/03 · openalex updated_date 2026/07/28
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.