Authors :
Muluneh Hailu Heyi; Daniel Moges Tadesse
Volume/Issue :
Volume 6 - 2021, Issue 10 - October
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3bTOpOA
Abstract :
Diabetic Retinopathy (DR) is a prominent
cause of blindness and visual problem that affects the eyes
of humans who are affected by diabetics. Most of the time
it does not show symptoms at an early stage and it is hard
for the patient to identify the symptoms until a visual
ability degrade and the treatment becomes is less
effective. It becomes tough for medical experts
(ophthalmologists) to identify DR at an early stage
manually by observing the retinal image taken by a
fundus camera. Thus, computer-aided image processing
of retinal images taken by fundus camera has tremendous
advantages to detect retinal lesions associated with
Diabetic Retinopathy at an early stage. With less time and
effort, the computer aid image processing examines a
large number of images more accurately than the manual
observer-driven techniques. It becomes important
diagnostic aid to reduce the workload of
ophthalmologists. However, the presence of various
artifacts like the similarity of anatomical structures,
movement of the patient eye during image capturing,
device noise, and illumination makes the segmentation
and processing of images of major pathological structures
a difficult task.
In this study, we have developed a retinal image
segmentation algorithm and user-friendly software that
can ease the task of the medical experts by automatically
identifying Hard Exudates (HEs), which are the most
prevalent characteristic features of Diabetic Retinopathy
in its earliest stage. The algorithm first is written and
tested using MATLAB then user-friendly software is
developed using C# programming language in the
Microsoft .Net framework. To classify and segment the
retinal image taken by the fundus camera a general
representation of images color in the three spaces
(trinion) has been used and to extract image features a
trinion based Fourier Transforms has also been applied.
Neural Network (NN) based segmentation of Hard
Exudates are included in the method for color space
transformation and to extract features.
The efficiency of the developed image processing has
been tested in classifying and identifying hard exudated
and it shows better results.
Keywords :
Retinal Imaging, Image Processing, Image Segmentation, Neural Network, Diabetic Retinopathy.
Diabetic Retinopathy (DR) is a prominent
cause of blindness and visual problem that affects the eyes
of humans who are affected by diabetics. Most of the time
it does not show symptoms at an early stage and it is hard
for the patient to identify the symptoms until a visual
ability degrade and the treatment becomes is less
effective. It becomes tough for medical experts
(ophthalmologists) to identify DR at an early stage
manually by observing the retinal image taken by a
fundus camera. Thus, computer-aided image processing
of retinal images taken by fundus camera has tremendous
advantages to detect retinal lesions associated with
Diabetic Retinopathy at an early stage. With less time and
effort, the computer aid image processing examines a
large number of images more accurately than the manual
observer-driven techniques. It becomes important
diagnostic aid to reduce the workload of
ophthalmologists. However, the presence of various
artifacts like the similarity of anatomical structures,
movement of the patient eye during image capturing,
device noise, and illumination makes the segmentation
and processing of images of major pathological structures
a difficult task.
In this study, we have developed a retinal image
segmentation algorithm and user-friendly software that
can ease the task of the medical experts by automatically
identifying Hard Exudates (HEs), which are the most
prevalent characteristic features of Diabetic Retinopathy
in its earliest stage. The algorithm first is written and
tested using MATLAB then user-friendly software is
developed using C# programming language in the
Microsoft .Net framework. To classify and segment the
retinal image taken by the fundus camera a general
representation of images color in the three spaces
(trinion) has been used and to extract image features a
trinion based Fourier Transforms has also been applied.
Neural Network (NN) based segmentation of Hard
Exudates are included in the method for color space
transformation and to extract features.
The efficiency of the developed image processing has
been tested in classifying and identifying hard exudated
and it shows better results.
Keywords :
Retinal Imaging, Image Processing, Image Segmentation, Neural Network, Diabetic Retinopathy.