3 research outputs found
A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation
Over the last decades, various methods have been employed in
medical images analysis. Some state-of-the-arts techniques such as deep learn�ing have been recently applied to medical images analysis. This research pro�poses the application of deep learning technique in performing segmentation of
retinal blood vessels. Analyzing and segmentation of retina vessels has assisted
in diagnosis and monitoring of some diseases. Diseases such as age-related
fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arterioscle�rosis and choroidal neovascularization can be effectively managed by the
analysis of retinal vessels images. In this work, a Deep Convolutional Encoder�Decoder Architecture for the segmentation of retinal vessels images is proposed.
The proposed method is a deep learning system composed of an encoder and
decoder mechanism allows a low resolution image set of retinal vessels to be
analyzed by set of convolutional layers in the encoder unit before been sent into
a decoder unit for final segmented output. The proposed system was evaluated
using some evaluation metrics such as dice coefficient, jaccard index and mean
of intersection. The review of the existing works was also carried out. It could be
shown that the proposed system outperforms many existing methods in the
segmentation of retinal vessels images