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Designing and testing scene enhancement algorithms for patients with retina degenerative disorders.

Al-Atabany WI, Memon MA, Downes SM, Degenaar PA - Biomed Eng Online (2010)

Bottom Line: To test our techniques, we have therefore built a retinal image processing model and compared the results to data from patient testing.Results from our model are compared to actual data obtained from testing the performance of these algorithms on 27 patients with an average visual acuity of 0.63 and an average contrast sensitivity of 1.22.Image Cartoonization was most beneficial for spatial feature detection such as face detection.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biomedical Engineering, Imperial College, South Kensington, London, UK. walid.atbany06@imperial.ac.uk

ABSTRACT

Background: Retina degenerative disorders represent the primary cause of blindness in UK and in the developed world. In particular, Age Related Macular Degeneration (AMD) and Retina Pigmentosa (RP) diseases are of interest to this study. We have therefore created new image processing algorithms for enhancing the visual scenes for them.

Methods: In this paper we present three novel image enhancement techniques aimed at enhancing the remaining visual information for patients suffering from retina dystrophies. Currently, the only effective way to test novel technology for visual enhancement is to undergo testing on large numbers of patients. To test our techniques, we have therefore built a retinal image processing model and compared the results to data from patient testing. In particular we focus on the ability of our image processing techniques to achieve improved face detection and enhanced edge perception.

Results: Results from our model are compared to actual data obtained from testing the performance of these algorithms on 27 patients with an average visual acuity of 0.63 and an average contrast sensitivity of 1.22. Results show that Tinted Reduced Outlined Nature (TRON) and Edge Overlaying algorithms are most beneficial for dynamic scenes such as motion detection. Image Cartoonization was most beneficial for spatial feature detection such as face detection. Patient's stated that they would most like to see Cartoonized images for use in daily life.

Conclusions: Results obtained from our retinal model and from patients show that there is potential for these image processing techniques to improve visual function amongst the visually impaired community. In addition our methodology using face detection and efficiency of perceived edges in determining potential benefit derived from different image enhancement algorithms could also prove to be useful in quantitatively assessing algorithms in future studies.

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Related in: MedlinePlus

Retinal model structure. Structure of the normal retina model (simulating the foveal vision and the functions of the OPL) firstly by simulating the foveated vision using a multi-scale resolution sampling approach. Pixels in the fovea region are set to 1:1 from the input image while peripheral pixels are blurred by a Gaussian function with exponentially growing kernel size with radial distance from the fovea. The macular degeneration block is used here to simulate the degenerated photoreceptors. Then color is separated into four channels; Luminance, Red, Green, Blue and Yellow to be used in simulating the color opponent channels. The processed image is then reconstructed in the reconstruction module (three channels are reconstructed here; the Luminance, R/G and B/Y channels). Images are shown beside each stage for illustration.
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Figure 4: Retinal model structure. Structure of the normal retina model (simulating the foveal vision and the functions of the OPL) firstly by simulating the foveated vision using a multi-scale resolution sampling approach. Pixels in the fovea region are set to 1:1 from the input image while peripheral pixels are blurred by a Gaussian function with exponentially growing kernel size with radial distance from the fovea. The macular degeneration block is used here to simulate the degenerated photoreceptors. Then color is separated into four channels; Luminance, Red, Green, Blue and Yellow to be used in simulating the color opponent channels. The processed image is then reconstructed in the reconstruction module (three channels are reconstructed here; the Luminance, R/G and B/Y channels). Images are shown beside each stage for illustration.

Mentions: In this work we are interested in the effect of spatial feature enhancement. Thus we model the P pathway and the non-temporal processing aspects of the M pathway. Our model is constructed from a linear combination of a set of spatial filters applied to the chromatic and achromatic color channels of the image matrix. Figure 4, shows the structure of our model. Our model represents the main processing layers of the retina, but we do not account for spike coding effects.


Designing and testing scene enhancement algorithms for patients with retina degenerative disorders.

Al-Atabany WI, Memon MA, Downes SM, Degenaar PA - Biomed Eng Online (2010)

Retinal model structure. Structure of the normal retina model (simulating the foveal vision and the functions of the OPL) firstly by simulating the foveated vision using a multi-scale resolution sampling approach. Pixels in the fovea region are set to 1:1 from the input image while peripheral pixels are blurred by a Gaussian function with exponentially growing kernel size with radial distance from the fovea. The macular degeneration block is used here to simulate the degenerated photoreceptors. Then color is separated into four channels; Luminance, Red, Green, Blue and Yellow to be used in simulating the color opponent channels. The processed image is then reconstructed in the reconstruction module (three channels are reconstructed here; the Luminance, R/G and B/Y channels). Images are shown beside each stage for illustration.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2914026&req=5

Figure 4: Retinal model structure. Structure of the normal retina model (simulating the foveal vision and the functions of the OPL) firstly by simulating the foveated vision using a multi-scale resolution sampling approach. Pixels in the fovea region are set to 1:1 from the input image while peripheral pixels are blurred by a Gaussian function with exponentially growing kernel size with radial distance from the fovea. The macular degeneration block is used here to simulate the degenerated photoreceptors. Then color is separated into four channels; Luminance, Red, Green, Blue and Yellow to be used in simulating the color opponent channels. The processed image is then reconstructed in the reconstruction module (three channels are reconstructed here; the Luminance, R/G and B/Y channels). Images are shown beside each stage for illustration.
Mentions: In this work we are interested in the effect of spatial feature enhancement. Thus we model the P pathway and the non-temporal processing aspects of the M pathway. Our model is constructed from a linear combination of a set of spatial filters applied to the chromatic and achromatic color channels of the image matrix. Figure 4, shows the structure of our model. Our model represents the main processing layers of the retina, but we do not account for spike coding effects.

Bottom Line: To test our techniques, we have therefore built a retinal image processing model and compared the results to data from patient testing.Results from our model are compared to actual data obtained from testing the performance of these algorithms on 27 patients with an average visual acuity of 0.63 and an average contrast sensitivity of 1.22.Image Cartoonization was most beneficial for spatial feature detection such as face detection.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Biomedical Engineering, Imperial College, South Kensington, London, UK. walid.atbany06@imperial.ac.uk

ABSTRACT

Background: Retina degenerative disorders represent the primary cause of blindness in UK and in the developed world. In particular, Age Related Macular Degeneration (AMD) and Retina Pigmentosa (RP) diseases are of interest to this study. We have therefore created new image processing algorithms for enhancing the visual scenes for them.

Methods: In this paper we present three novel image enhancement techniques aimed at enhancing the remaining visual information for patients suffering from retina dystrophies. Currently, the only effective way to test novel technology for visual enhancement is to undergo testing on large numbers of patients. To test our techniques, we have therefore built a retinal image processing model and compared the results to data from patient testing. In particular we focus on the ability of our image processing techniques to achieve improved face detection and enhanced edge perception.

Results: Results from our model are compared to actual data obtained from testing the performance of these algorithms on 27 patients with an average visual acuity of 0.63 and an average contrast sensitivity of 1.22. Results show that Tinted Reduced Outlined Nature (TRON) and Edge Overlaying algorithms are most beneficial for dynamic scenes such as motion detection. Image Cartoonization was most beneficial for spatial feature detection such as face detection. Patient's stated that they would most like to see Cartoonized images for use in daily life.

Conclusions: Results obtained from our retinal model and from patients show that there is potential for these image processing techniques to improve visual function amongst the visually impaired community. In addition our methodology using face detection and efficiency of perceived edges in determining potential benefit derived from different image enhancement algorithms could also prove to be useful in quantitatively assessing algorithms in future studies.

Show MeSH
Related in: MedlinePlus