Novel and adaptive contribution of the red channel in pre-processing of colour fundus images |
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| Institution: | 1. University of Texas at Dallas, United States;2. Oklahoma State University, United States;1. Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea;2. Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea;1. Department of Electrical Engineering, IIT Madras, India;2. Healthcare Technology Innovation Centre, IIT Madras, India;3. Beyond Eye Care, India;1. College of Chemistry, Chemical Engineering and Environmental Engineering, Liaoning Shihua University, Fushun 113001, PR China;2. School of New Energy, Shenyang Institute of Engineering, Shenyang 110136, PR China;3. College of Petroleum Engineering, Liaoning Shihua University, Fushun 113001, PR China;1. BICG, Departments of Pediatrics, Bioengineering, Radiology, University of Washington, Seattle, USA;2. ICube, Université de Strasbourg/CNRS, UMR 7357, Illkirch, France |
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| Abstract: | A new pre-processing method for colour fundus images with adaptive contribution of the red channel is proposed. Based on a condition that is developed in this paper, this method utilises the intensity information from both red and green channels instead of using only the green channel as in the usual practice. The histogram matching is used to modify the histogram of the green channel by using the histogram of the red channel (of the same retinal image) to obtain a new processed image having the advantages of both channels. This method can be used to correct non-uniform illumination in colour fundus images or as a pre-processing step in the automatic analysis of retinal images.Results show that the use of histogram matched (HM) image give better performance than using the green channel image when employing the two-dimensional matched filter to detect retinal blood vessels. At specificity of 90%, in case of abnormal images, sensitivity increased from 76% when using the green channel image to 82% when using the HM image compared with 81% when using the piece-wise threshold probing method. In case of normal images, at the same specificity, the sensitivity obtained when using green channel image or HM image was 87% compared with 88% for the piece-wise threshold probing method. |
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