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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

The efficiency of image enhancement algorithms. The efficiency of image enhancement algorithms using the outputs from the retina model by calculating the average percentage of extracted edges in the unprocessed and processed images over 6 different images.
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Figure 7: The efficiency of image enhancement algorithms. The efficiency of image enhancement algorithms using the outputs from the retina model by calculating the average percentage of extracted edges in the unprocessed and processed images over 6 different images.

Mentions: Figure 6, shows the output of two images and enhanced versions thereof which have been passed through the degenerate retina model. The percentages of extracted edges are described under each image to show the efficacy of each algorithm. The percentage of extracted edges is higher in the images which have undergone prior visual enhancement processing. We do not present the output from the edge on cartoon algorithm in Figure 6 as it looks very similar to the edge on original processing function in this case. The average percentage of extracted edges over the 15 images for the unprocessed and processed images is shown in Figure 7.


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)

The efficiency of image enhancement algorithms. The efficiency of image enhancement algorithms using the outputs from the retina model by calculating the average percentage of extracted edges in the unprocessed and processed images over 6 different images.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: The efficiency of image enhancement algorithms. The efficiency of image enhancement algorithms using the outputs from the retina model by calculating the average percentage of extracted edges in the unprocessed and processed images over 6 different images.
Mentions: Figure 6, shows the output of two images and enhanced versions thereof which have been passed through the degenerate retina model. The percentages of extracted edges are described under each image to show the efficacy of each algorithm. The percentage of extracted edges is higher in the images which have undergone prior visual enhancement processing. We do not present the output from the edge on cartoon algorithm in Figure 6 as it looks very similar to the edge on original processing function in this case. The average percentage of extracted edges over the 15 images for the unprocessed and processed images is shown in Figure 7.

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