An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches

Title

An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches

Creator

Saju B.; Rajesh R.

Description

Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed schemes accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively. 2023 Baqiyatallah University of Medical Sciences. All rights reserved.

Source

International Journal on Recent and Innovation Trends in Computing and Communication, Vol-11, No. 5, pp. 170-180.

Date

2023-01-01

Publisher

Auricle Global Society of Education and Research

Subject

Green Channel; Image Quality; Noise Filtering; Pre-Processing; Quality Score; Retinal Image

Coverage

Saju B., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Rajesh R., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 23218169

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.17762/ijritcc.v11i5.6603

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