No-reference Fundus Image Quality Assessment using Convolutional Neural Network
Bhargav J Bhatkalkar1, Dheeraj Rajaram Reddy2, Vishnu Asutosh Dasu3, Advait Raykar4, Srikanth Prabhu5, Sulatha Bhandary6, Govardhan Hegde7
1Bhargav J Bhatkalkar, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
2Dheeraj Rajaram Reddy, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
3Vishnu Asutosh Dasu, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
4Advait Raykar, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
5Srikanth Prabhu, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
6Sulatha Bhandary, Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
7Govardhan Hegde, Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Udupi (Karnataka), India.
Manuscript received on 29 April 2019 | Revised Manuscript received on 11 May 2019 | Manuscript Published on 17 May 2019 | PP: 663-667 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F11370476S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Computerized fundus image analysis is a well-established 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 0- 10, 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.
Keywords: Fundus Image Analysis, Image Quality Assessment, Convolutional Neural Network.
Scope of the Article: Neural Information Processing