Retinal disease diagnosis by morphological feature extraction and SVM classification of retinal blood vessels

Princye, P.Hosanna; Vijayakumari, V.

doi:10.4066/biomedicalresearch.29-16-2320

Cited by 5 publications

(1 citation statement)

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“…Numerous researches have employed machine learning of different types and methods to segment blood vessels in retinal fundus images. Machine learning methods such as artificial neural network (ANN) [3,8], support vector machine (SVM) [14], and recently convolutional neural network (CNN) [7,19] has shown to be a reliable method to provide an accurate segmentation towards the blood vessels in retinal fundus images. Nevertheless, employing machine learning requires massive amount of training datasets to allow the algorithm to learn different pathological features in the retinal fundus images.…”

Section: Machine Learningmentioning

confidence: 99%

Retinal Fundus Image Blood Vessels Segmentation via Object-Oriented Metadata Structures

Fadzil

Ahmad

Khalid³

et al. 2018

IJET

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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.

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