Retrieving challenging vessel connections in retinal images by line co-occurrence statistics
| Autor: | Samaneh Abbasi-Sureshjani, Jiong Zhang, Remco Duits, Bart M. ter Haar Romeny |
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| Přispěvatelé: | Medical Image Analysis, Center for Analysis, Scientific Computing & Appl., Mathematical Image Analysis |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Similarity (geometry) Curvilinear structures General Computer Science Computer science Computer Vision and Pattern Recognition (cs.CV) Connection (vector bundle) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Retina 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Models Statistics Spectral clustering 0202 electrical engineering electronic engineering information engineering Line co-occurrences Cortical connectivity Contextual affinity matrix Visual Cortex Curvilinear coordinates Models Statistical Retina/anatomy & histology Visual Cortex/anatomy & histology Retinal Vessels Statistical model Retinal Statistical chemistry Projective line Bundle Line (geometry) 020201 artificial intelligence & image processing Original Article Biotechnology Perceptual grouping |
| Zdroj: | Biological Cybernetics Biological Cybernetics, 111(3-4). Springer |
| ISSN: | 1432-0770 0340-1200 |
| Popis: | Natural images contain often curvilinear structures, which might be disconnected, or partly occluded. Recovering the missing connection of disconnected structures is an open issue and needs appropriate geometric reasoning. We propose to find line co-occurrence statistics from the centerlines of blood vessels in retinal images and show its remarkable similarity to a well-known probabilistic model for the connectivity pattern in the primary visual cortex. Furthermore, the probabilistic model is trained from the data via statistics and used for automated grouping of interrupted vessels in a spectral clustering based approach. Several challenging image patches are investigated around junction points, where successful results indicate the perfect match of the trained model to the profiles of blood vessels in retinal images. Also, comparisons among several statistical models obtained from different datasets reveal their high similarity, i.e., they are independent of the dataset. On top of that the best approximation of the statistical model with the symmetrized extension of the probabilistic model on the projective line bundle is found with a least square error smaller than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\%$$\end{document}2%. Apparently, the direction process on the projective line bundle is a good continuation model for vessels in retinal images. |
| Databáze: | OpenAIRE |
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