Abstract
Retinal fundus image is a crucial tool for ophthalmologists to diagnose eye-related diseases. These images provide visual information of the interior layer of the retina structures such as optic disc, optic cup, blood vessels and macula that can assist ophthalmologist in determining the health of an eye. Segmentation of blood vessels in fundus images is one of the most fundamental phase in detecting diseases such as diabetic retinopathy. However, the ambiguity of the retina structures in the retinal fundus images presents a challenge for researcher to segment the blood vessels. Extensive pre-processing and training of the images is necessary for precise segmentation, which is very intricate and laborious. This paper proposes the implementation of object-oriented-based metadata (OOM) structures of each pixel in the retinal fundus images. These structures comprise of additional metadata towards the conventional red, green, and blue data for each pixel within the images. The segmentation of the blood vessels in the retinal fundus images are performed by considering these additional metadata that enunciates the location, color spaces, and neighboring pixels of each individual pixel. From the results, it is shown that accurate segmentation of retinal fundus blood vessels can be achieved by purely employing straightforward thresholding method via the OOM structures without extensive pre-processing image processing technique or data training.
Highlights
Visual information in retinal fundus image provide a crucial tool for ophthalmologists to diagnose eye-related diseases
This paper proposes an implementation of object-oriented metadata (OOM) structures that provides additional description of each pixel within an image
This paper proposes the employment of object-oriented metadata (OOM) structures for describing each individual pixel within the retinal fundus images
Summary
Most researches emphasize on the either the extensive preprocessing of the images [10, 12, 15] or the massive learning datasets of the images [4,5,6,7, 19]. This resulted in the methods requires intricate and laborious processing. Instead of relying on intensive preprocessing image processing operation and massive training datasets, this approach relies on the metadata of each pixel to produce an accurate segmentation
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