R Geetha Ramani1, J Jeslin Shanthamalar2. 1. Department of Information Science and Technology, Anna University, Chennai, India. 2. Sathyabama Institute of Science and Technology, Sathyabama University, Chennai, India. jeslin.cse@sathyabama.ac.in.
Abstract
PURPOSE: Automatic retinal fundus image quality analysis is one of the most essential preliminary stages in automatic computer-aided retinal disease diagnosis system, which allows good-quality fundus images for accurate disease prediction through localization and segmentation of retinal regions. This paper presents new feature extraction methods using full-reference and no-reference image quality metrics for image quality classification. METHODS: Basic image features, reference and no-reference features are extracted from the fundus image and applied through different classification techniques to determine the image quality for further diagnosis. In this paper, human-made categorization including good and non-good-quality fundus image classification is constructed by considering major features of retinal fundus images are illumination, clarity, image intensity, contrast and region visibility. The proposed system presented fundus image quality classification by automatic extraction of features from fundus images through image processing techniques and automatic classification of image quality through different classification algorithm. RESULTS: This system was thoroughly investigated on 2674 retinal fundus images from publically available datasets, namely MESSIDOR, Drishti-GS1, DRIVE, HRF, DRIONS-DB, DIARETDB0, DIARETDB1, IDRiD, INSPIRE-AVR, CHASE-DB1, ONHSD, DRIMDB and e-ophtha-EX with better performance results in terms of sensitivity, accuracy, precision and F1 score of 99.36%, 96.79%, 96.29% and 97.79%, respectively. CONCLUSION: The proposed system results were compared to the existing state-of-the-art approaches and outperform existing methods for image quality assessment representing the efficiency and robustness of our system is most suitable for automatic image analysis during retinal disease diagnosis.
PURPOSE: Automatic retinal fundus image quality analysis is one of the most essential preliminary stages in automatic computer-aided retinal disease diagnosis system, which allows good-quality fundus images for accurate disease prediction through localization and segmentation of retinal regions. This paper presents new feature extraction methods using full-reference and no-reference image quality metrics for image quality classification. METHODS: Basic image features, reference and no-reference features are extracted from the fundus image and applied through different classification techniques to determine the image quality for further diagnosis. In this paper, human-made categorization including good and non-good-quality fundus image classification is constructed by considering major features of retinal fundus images are illumination, clarity, image intensity, contrast and region visibility. The proposed system presented fundus image quality classification by automatic extraction of features from fundus images through image processing techniques and automatic classification of image quality through different classification algorithm. RESULTS: This system was thoroughly investigated on 2674 retinal fundus images from publically available datasets, namely MESSIDOR, Drishti-GS1, DRIVE, HRF, DRIONS-DB, DIARETDB0, DIARETDB1, IDRiD, INSPIRE-AVR, CHASE-DB1, ONHSD, DRIMDB and e-ophtha-EX with better performance results in terms of sensitivity, accuracy, precision and F1 score of 99.36%, 96.79%, 96.29% and 97.79%, respectively. CONCLUSION: The proposed system results were compared to the existing state-of-the-art approaches and outperform existing methods for image quality assessment representing the efficiency and robustness of our system is most suitable for automatic image analysis during retinal disease diagnosis.
Authors: James Lowell; Andrew Hunter; David Steel; Ansu Basu; Robert Ryder; Eric Fletcher; Lee Kennedy Journal: IEEE Trans Med Imaging Date: 2004-02 Impact factor: 10.048
Authors: Alan D Fleming; Sam Philip; Keith A Goatman; John A Olson; Peter F Sharp Journal: Invest Ophthalmol Vis Sci Date: 2006-03 Impact factor: 4.799
Authors: Meindert Niemeijer; Xiayu Xu; Alina V Dumitrescu; Priya Gupta; Bram van Ginneken; James C Folk; Michael D Abramoff Journal: IEEE Trans Med Imaging Date: 2011-06-16 Impact factor: 10.048