Automated Fovea Detection Based on Unsupervised Retinal Vessel Segmentation Method
| Autor: | Meysam Tavakoli, Patrick Kelley, Faraz Kalantari, Mahdieh Nazar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
genetic structures
Computer science Feature extraction FOS: Physical sciences 02 engineering and technology Fundus (eye) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering medicine Segmentation Computer vision Retina Radon transform Radon space business.industry Image and Video Processing (eess.IV) Image segmentation Electrical Engineering and Systems Science - Image and Video Processing Physics - Medical Physics eye diseases medicine.anatomical_structure 020201 artificial intelligence & image processing Medical Physics (physics.med-ph) Artificial intelligence sense organs Focus (optics) business |
| Popis: | The Computer Assisted Diagnosis systems could save workloads and give objective diagnostic to ophthalmologists. At first level of automated screening of systems feature extraction is the fundamental step. One of these retinal features is the fovea. The fovea is a small fossa on the fundus, which is represented by a deep-red or red-brown color in color retinal images. By observing retinal images, it appears that the main vessels diverge from the optic nerve head and follow a specific course that can be geometrically modeled as a parabola, with a common vertex inside the optic nerve head and the fovea located along the apex of this parabola curve. Therefore, based on this assumption, the main retinal blood vessels are segmented and fitted to a parabolic model. With respect to the core vascular structure, we can thus detect fovea in the fundus images. For the vessel segmentation, our algorithm addresses the image locally where homogeneity of features is more likely to occur. The algorithm is composed of 4 steps: multi-overlapping windows, local Radon transform, vessel validation, and parabolic fitting. In order to extract blood vessels, sub-vessels should be extracted in local windows. The high contrast between blood vessels and image background in the images cause the vessels to be associated with peaks in the Radon space. The largest vessels, using a high threshold of the Radon transform, determines the main course or overall configuration of the blood vessels which when fitted to a parabola, leads to the future localization of the fovea. In effect, with an accurate fit, the fovea normally lies along the slope joining the vertex and the focus. The darkest region along this line is the indicative of the fovea. To evaluate our method, we used 220 fundus images from a rural databse (MUMS-DB) and one public one (DRIVE). The results show that, among 20 images of the first public database (DRIVE) we detected fovea in 85% of them. Also for the MUMS-DB database among 200 images we detect fovea correctly in 83% on them. |
| Databáze: | OpenAIRE |
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