Variational Mode Decomposition Based Retinal Area Detection and Merging of Superpixels in SLO Image
Authors: Manikandan Suchetha , Deepika V. , Philip Nada 
Published: February 2020

Background:

Scanning Laser Ophthalmoscope (SLO) image can be used to detect retinal
diseases. However detecting retinal area is a major task as retina artefacts such as eyelashes
and eyelids are also captured. Huge part of retina can be viewed if it is done with the help of encroachment
of SLO.Vision loss can be avoided with the help of retinal disease treatment. In olden
days retinal diseases are recognized using manual techniques. Alteration of zooming and contrast
are imparted by Optometrists and ophthalmologists. It is done to deduce images and diagnose results
based on familiarity and domain knowledge. These diagnostic methods are always a time
consuming process. Thus execution time can be reduced using mechanical examination of retinal
images. It is better to glimpse at the images which could screen more patients and more unswerving
diagnoses can be given in a time efficient manner. Scanning Laser Ophthalmoscope images
gives the outcome of 2-D retinal scans. However it contains artefacts such as eyelids and eyelashes
along with true retinal area. So the main confront is to eliminate these artefacts from the captured
retinal image.

Objective:

Scanning Laser Ophthalmoscope (SLO) image can be used to detect retinal diseases.
However detecting retinal area is a major task as retina artefacts such as eyelashes and eyelids are
also captured. Huge part of retina can be viewed if it is done with the help of encroachment of SLO.
In this paper our novel technique helps in detecting the true retinal area based on image processing
techniques. To the SLO image two dimensional Variational Mode Decomposition (VMD) is applied.

Methods:

In this paper our novel technique helps in detecting the true retinal area based on image
processing techniques. To the SLO image two dimensional Variational Mode Decomposition
(VMD) is applied. As a result of this different modes are obtained. Mode 1 is chosed as it has high
frequency. Then mode1 is pre-processed using median filtering. After this preprocessed mode1
image is grouped into pixels based on regional size and compactness called superpixels. Superpixels
are generated to reduce complexity. Superpixel merging is done subsequent to Superpixel generation.
It is done to reduce further difficulty and to enhance the speed. From the merged superpixels
feature generation is performed using Regional, Gradient and textural features. It is done to
eliminate artefacts and to detect the retinal area. Also feature selection will reduce the processing
time and increase the speed. A classifier is constructed using Adaptive Network Fuzzy Inference
System (ANFIS) for classification of features and its performance is compared with Artificial
Neural Network (ANN).

Results:

By this novel approach we got a classification accuracy of 98.5%.

Conclusion:

Thus 2D-VMD gives six different modes. Based on high frequency mode1 is chosen.
This further makes the process easier and it helps to achieve accuracy level higher. ANFIS is able
to achieve higher accuracy when compared with ANN. Using ANFIS 98.5.