Name
Title | ||
|---|---|---|
Luminosity and contrast normalization in color retinal images based on standard reference image. |
Abstract | ||
|---|---|---|
Color retinal images are used manually or automatically for diagnosis and monitoring progression of a retinal diseases. Color retinal images have large luminosity and contrast variability within and across images due to the large natural variations in retinal pigmentation and complex imaging setups. The quality of retinal images may affect the performance of automatic screening tools therefore different normalization methods are developed to uniform data before applying any further analysis or processing. In this paper we propose a new reliable method to remove nonuniform illumination in retinal images and improve their contrast based on contrast of the reference image. The nonuniform illumination is removed by normalizing luminance image using local mean and standard deviation. Then the contrast is enhanced by shifting histograms of uniform illuminated retinal image toward histograms of the reference image to have similar histogram peaks. This process improve the contrast without changing inter correlation of pixels in different color channels. In compliance with the way humans perceive color, the uniform color space of LUV is used for normalization. The proposed method is widely tested on large dataset of retinal images with present of different pathologies such as Exudate, Lesion, Hemorrhages and Cotton-Wool and in different illumination conditions and imaging setups. Results shows that proposed method successfully equalize illumination and enhances contrast of retinal images without adding any extra artifacts. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/12.2217131 | Proceedings of SPIE |
Keywords | Field | DocType |
Retinal image normalization,contrast enhancement,Uniform illumination,Deep Learning,Deep Convolutional Neural network | Normalization (image processing),Computer vision,Normalization (statistics),Color space,Color histogram,Optics,Image processing,Artificial intelligence,Pixel,Retinal,Channel (digital image),Physics | Conference |
Volume | ISSN | Citations |
9784 | 0277-786X | 4 |
PageRank | References | Authors |
0.42 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ehsan Shahrian Varnousfaderani | 1 | 23 | 2.81 |
| Siamak Yousefi | 2 | 86 | 13.41 |
| Akram Belghith | 3 | 22 | 4.99 |
| Michael H Goldbaum | 4 | 824 | 77.08 |