Abstract
Abstract Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image. This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. Deep learning is one of the important computational application techniques used for a medical imaging application. The main aim of this article is to find the best enhancement techniques for the identification of diabetic retinopathy (DR) and are tested with the commonly used deep learning techniques, and the performances are measured. In this article, the input image is taken from the Indian-based database named as Indian Diabetic Retinopathy Image Dataset, and 13 filters are used including smoothing and sharpening filters for enhancing the images. Then, the quality of the enhancement techniques is compared using performance metrics and better results are obtained for Median, Gaussian, Bilateral, Wiener, and partial differential equation filters and are combined for improving the enhancement of images. The output images from all the enhanced filters are given as the convolutional neural network input and the results are compared to find the better enhancement method.
Highlights
Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina
The input retinal fundus image is taken from the Indianbased database named as Indian Diabetic Retinopathy Image Dataset given by ISBI 2018 challenge
The combinational filter enhancement graph clearly shows that the peak signal-to-noise ratio (PSNR) value is high for Gaussian filter (GF) + partial differential equation (PDE), the E value is higher for GWMF + PDE, the mean square error (MSE) and root mean square error (RMSE) error range is smaller for GF + PDE, the structural similarity index measure (SSIM) value is close to 1 for GF + PDE, the contrast improvement index (CII) value is highest for GWMF + PDE, the absolute mean brightness error (AMBE) value is lower for Wiener filter (WF) + PDE, the linear index of fuzziness (LIF) value is lower for GWMF + PDE, the relative contrast enhancement factor (RCEF) value is better for median filter (MF) + PDE, and the universal image quality index (UQI) value is highest for GWMF + PDE
Summary
Abstract: Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. The contrast variation, low contrast improvement, and irregular illumination suppression lead to the pre-processing stage [7] This method mainly helps the CAD system and visual assessment for better segmentation and classification in retinal fundus images [8]. The morphological operation has different parameters like size, shape, and structuring elements for measuring the performance of the method by varying the values This helps in improving the retinal image enhancement and further process [13]. It is proved that for better segmentation and classification methods, image enhancement techniques should be properly selected by finding the noise present in the available retinal fundus images. The structure of the article is as follows: Section 2 gives the retinal image enhancement block diagram as well as various image enhancement techniques and their performance metrics comparison; Section 3 gives the combinational filters and their performance metrics; and Section 4 gives the convolution neural network architecture and the accuracy obtained from all of the enhanced filters
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