No-reference Fundus Image Quality Assessment using Convolutional Neural Network

Bhatkalkar, Bhargav J and Reddy, Dheeraj Rajaram and Dasu, Vishnu Asutosh and Raykar, Advait and Prabhu, Srikanth and Bhandary, Sulatha V and Hegde, Govardhan (2019) No-reference Fundus Image Quality Assessment using Convolutional Neural Network. International Journal of Recent Technology and Engineering, 7 (6S4). pp. 663-667. ISSN 2277-3878

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Abstract

Computerized fundus image analysis is a wellestablished research area in the field of medical imaging. The cause of noise in fundus images is due to many factors like the low lighting conditions, adverse illumination effects, camera malfunctioning, etc. The presence of noise may lead to data loss and sometimes to the wrong data interpretation. Classifying the fundus images into either good quality or bad quality is very important as the good quality fundus images can be directly sent for processing without any preprocessing, hence reducing the computational time and the bad quality images can be forwarded for the required preprocessing stages. In this paper, we are using a convolutional neural network (CNN) to assess the quality of fundus images automatically. We use No-reference image quality assessment technique (IQA) classify the fundus images into good quality or bad quality based on their quality. A Mean Opinion Square (MOS) of 12 image quality assessment participants is taken for labeling the 300 fundus images based on their quality. The participants have rated the fundus images on the scale of 010, where the 0-rating is given for very bad quality fundus images, and 10-rating is given for the very good quality fundus images. The experimental study has proven that the classification result of the proposed CNN outperforms the best-known blind image quality assessment algorithms, namely, DIVINE, BLIINDS-II, and BRISQUE when trained on the public databases LIVE, TID2013 and on our fundus image dataset.

Item Type: Article
Uncontrolled Keywords: —Fundus image analysis, image quality assessment, convolutional neural network
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Medicine > KMC Manipal > Ophthalmology
Depositing User: MIT Library
Date Deposited: 29 Jun 2019 05:44
Last Modified: 29 Jun 2019 05:44
URI: http://eprints.manipal.edu/id/eprint/154057

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