Variational Mode Decomposition Based Retinal Area Detection and Merging of Superpixels in SLO Image

(E-pub Ahead of Print)

Author(s): Suchetha.M*, Deepika.V

Journal Name: Current Signal Transduction Therapy

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Abstract:

Scanning Laser Ophthalmoscope (SLO) image can be used to detect retinal diseases. However identifying retinal area is a major task as retinal artefacts such as eyelashes and eyelids are also captured. Major part of retina can be viewed if detection is done with the help of images 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. As a result of this different modes are obtained. Mode-1 is choosed because 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 next 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). By this novel approach we got an classification accuracy of 98.5%.

Keywords: Scanning Laser Ophthalmoscope, Superpixel Generation, Superpixel merging, Classifier construction

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(E-pub Ahead of Print)
DOI: 10.2174/1389203721666200218105730

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