Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters
| Autor: | Nicolai Petkov, Mario Vento, George Azzopardi, Nicola Strisciuglio |
|---|---|
| Přispěvatelé: | Intelligent Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
B-COSFIRE 02 engineering and technology Fundus (eye) Information theory 030218 nuclear medicine & medical imaging Set (abstract data type) 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Image processing Retina -- Imaging 0202 electrical engineering electronic engineering information engineering Computer vision Filters selection Retinal vessels segmentation Trainable filters Vessel delineation Hardware and Architecture 1707 Software Computer Science Applications1707 Computer Vision and Pattern Recognition Selection (genetic algorithm) business.industry Process (computing) Retinal Pattern recognition systems Computer Science Applications Tree (data structure) chemistry 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Benchmark data business |
| Zdroj: | Machine Vision and Applications, 27(8). SPRINGER |
| ISSN: | 0932-8092 |
| DOI: | 10.1007/s00138-016-0781-7 |
| Popis: | The inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background.We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method we employ a set of B-COSFIRE filters selective for vessels and vesselendings. Such a set is determined in an automatic selection process and can adapt to different applications.We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE demonstrate the effectiveness of the proposed approach. peer-reviewed |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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