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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

Simulating scotoma (photoreceptor loss). The macular degeneration mask simulated in the mode: a) is the original image; b) is the foveated image with fovea focusing to the upper right part of the image; c) is the mask output that simulates retinal deterioration in the macula. Black pixels represent regions of photoreceptor loss; white pixels correspond to responsive regions of the normal photoreceptor mosaic. In this example, the black pixels cover a total of 100% of the macula region; d) is the degenerated image.
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Figure 5: Simulating scotoma (photoreceptor loss). The macular degeneration mask simulated in the mode: a) is the original image; b) is the foveated image with fovea focusing to the upper right part of the image; c) is the mask output that simulates retinal deterioration in the macula. Black pixels represent regions of photoreceptor loss; white pixels correspond to responsive regions of the normal photoreceptor mosaic. In this example, the black pixels cover a total of 100% of the macula region; d) is the degenerated image.

Mentions: Where M is the mask output, Xfovea, Yfovea is the x and y position of the fovea, S is the size of the defected region and D is the degree of degeneration. The output pixel of this mask is 0 or 1; 0 (black) pixels represent regions of photoreceptor loss [where M(x, y) = 0] and 1 (white) pixels correspond to responsive regions of the normal photoreceptor [where M(x, y) = 1]. To simulate the blurring effect, the output is not simply set to zero. Rather, it has been ablated to simulate the diffusion of the photoreceptor loss by filling in the black spots with a Gaussian average of the pixels of the adjacent spots of healthy photoreceptors (pixels). Figure 5, shows the output of the mask for a fovea fixated to the top right part of an image with size of degeneration equivalent to the same size of the macula region which biologically equal to 6 mm (equivalent to 144 × 144 pixels for a 1008 × 800 image) [35].


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)

Simulating scotoma (photoreceptor loss). The macular degeneration mask simulated in the mode: a) is the original image; b) is the foveated image with fovea focusing to the upper right part of the image; c) is the mask output that simulates retinal deterioration in the macula. Black pixels represent regions of photoreceptor loss; white pixels correspond to responsive regions of the normal photoreceptor mosaic. In this example, the black pixels cover a total of 100% of the macula region; d) is the degenerated image.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Simulating scotoma (photoreceptor loss). The macular degeneration mask simulated in the mode: a) is the original image; b) is the foveated image with fovea focusing to the upper right part of the image; c) is the mask output that simulates retinal deterioration in the macula. Black pixels represent regions of photoreceptor loss; white pixels correspond to responsive regions of the normal photoreceptor mosaic. In this example, the black pixels cover a total of 100% of the macula region; d) is the degenerated image.
Mentions: Where M is the mask output, Xfovea, Yfovea is the x and y position of the fovea, S is the size of the defected region and D is the degree of degeneration. The output pixel of this mask is 0 or 1; 0 (black) pixels represent regions of photoreceptor loss [where M(x, y) = 0] and 1 (white) pixels correspond to responsive regions of the normal photoreceptor [where M(x, y) = 1]. To simulate the blurring effect, the output is not simply set to zero. Rather, it has been ablated to simulate the diffusion of the photoreceptor loss by filling in the black spots with a Gaussian average of the pixels of the adjacent spots of healthy photoreceptors (pixels). Figure 5, shows the output of the mask for a fovea fixated to the top right part of an image with size of degeneration equivalent to the same size of the macula region which biologically equal to 6 mm (equivalent to 144 × 144 pixels for a 1008 × 800 image) [35].

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