Abstract
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.
Highlights
Retinal disease treatment helps in avoiding vision loss
Diagnosing processes are very time consuming to diagnose for single patients so Scanning laser ophthalmoscope avoids this difficulty
Scanning Laser Ophthalmoscope images gives the outcome of 2-D retinal scans
Summary
Retinal disease treatment helps in avoiding vision loss. Manual techniques were used to detect retinal diseases previously. In Otsu’s method [7] eyelashes are detected based on thresholding but due to variation in threshold value results are not accurate. Optic nerve head and fovea [8] structure is used for detection of eyelashes but results are not accurate. Grid analysis is another method used to generate features of particular region rather than each pixel. To select improved pixels from the image superpixel generation is introduced [10] This technique helps in grouping pixels into different regions depending upon their regional size and compactness. This helps to reduce the area to be detected and utilize less time for computation.
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