Artery/Vein Vessel Tree Identification in Near-Infrared Reflectance Retinographies
| Autor: | Marcos Ortega, Jorge Novo, Joaquim de Moura, José Rouco, Pablo Charlón |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Retinography 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Optical coherence tomography Retinal Diseases medicine Near infrared reflectance Humans Radiology Nuclear Medicine and imaging Vascular Diseases Cluster analysis Original Paper Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Retinal Vessels Pattern recognition Reflectivity Computer Science Applications Tree structure medicine.anatomical_structure Computer-aided diagnosis Artificial intelligence business 030217 neurology & neurosurgery Tomography Optical Coherence Artery |
| Zdroj: | J Digit Imaging |
| Popis: | An accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corresponding histological sections. The method, firstly, segments the vessel tree and identifies its characteristic points. Then, Global Intensity-Based Features (GIBS) are used to measure the differences in the intensity profiles between arteries and veins. A k-means clustering classifier employs these features to evaluate the potential of artery/vein identification of the proposed method. Finally, a post-processing stage is applied to correct misclassifications using context information and maximize the performance of the classification process. The methodology was validated using an OCT image dataset retrieved from 46 different patients, where 2,392 vessel segments and 97,294 vessel points were manually labeled by an expert clinician. The method achieved satisfactory results, reaching a best accuracy of 93.35% in the identification of arteries and veins, being the first proposal that faces this issue in this image modality. |
| Databáze: | OpenAIRE |
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