Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net
Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vess...
Saved in:
Main Authors: | Surbhi Bhatia (Author), Shadab Alam (Author), Mohammed Shuaib (Author), Mohammed Hameed Alhameed (Author), Fathe Jeribi (Author), Razan Ibrahim Alsuwailem (Author) |
---|---|
Format: | Book |
Published: |
Frontiers Media S.A.,
2022-03-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation
by: Yugen Yi, et al.
Published: (2022) -
MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation
by: Zhenchao CUI, et al.
Published: (2022) -
Convolutional Neural Network Based Real Time Arabic Speech Recognition to Arabic Braille for Hearing and Visually Impaired
by: Surbhi Bhatia, et al.
Published: (2022) -
Precise in vivo adaptive optics imaging of retinal vessels
by: Oleg Zadorozhnyy, et al.
Published: (2023) -
Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images
by: Wenjing Li, et al.
Published: (2022)