Fundus Blood Vessels Extraction Using Digital Image Processing Techniques

Gaikwad, Sonali S.; Manza, Ramesh R.

doi:10.17577/ijertv6is100017

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…Sasirekha N [5] used splat feature segmentation to detect retinal hemorrhages in color fundus images based on the pixel distribution. Sonali S et al [6] used channel extraction methods, with equalization of the histogram. Then Morphological operations were used to Identify Occlusion.…”

Section: Fig 1 Central Retinal Vein Occlusion Imagementioning

confidence: 99%

A CNN Approach to Central Retinal Vein Occlusion Detection

Bala,

Sindha,

Sahayam

et al. 2023

International Journal of Electronics and Telecommunications

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In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.

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Section: Fig 1 Central Retinal Vein Occlusion Imagementioning

confidence: 99%