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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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The performance of face detection when image enhancement algorithms are used. The number of detected faces over all the 14 images with different levels of macular degeneration for the original and the four image enhancement algorithms using the Viola-Jones face detection method (a) and Kienzle method (b).
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Figure 12: The performance of face detection when image enhancement algorithms are used. The number of detected faces over all the 14 images with different levels of macular degeneration for the original and the four image enhancement algorithms using the Viola-Jones face detection method (a) and Kienzle method (b).

Mentions: Figure 12, shows the performance of each algorithm in enhancing the process of detecting faces compared to the original image using both the Viola-Jones and Kienzle algorithms, respectively. We can see that Cartoonization has the highest efficiency in detecting faces which was expected as Cartoonization enhances the contrast between boundaries while keeping the color information in the scene intact. Edge overlaying on cartoon images is less effective compared to Cartoonization in detecting faces when using the Viola-Jones algorithm and there is not much difference between it and the Cartoonization alone when using the Kienzle method. The original images were ranked as third and edge overlay on original was ranked last. We find that the TRON algorithm is not efficient in detecting faces. This is because the Viola-Jones used rectangular features (templates) that compare relative intensities of adjacent regions, and the Kienzle method works on the intensity level of the image pixels. In contrast, the TRON algorithm focuses mainly on enhancing the edges over the salient information in the scene. This suppresses most of the intensity information in the image and keeps only the boundaries between contrast regions.


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 performance of face detection when image enhancement algorithms are used. The number of detected faces over all the 14 images with different levels of macular degeneration for the original and the four image enhancement algorithms using the Viola-Jones face detection method (a) and Kienzle method (b).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 12: The performance of face detection when image enhancement algorithms are used. The number of detected faces over all the 14 images with different levels of macular degeneration for the original and the four image enhancement algorithms using the Viola-Jones face detection method (a) and Kienzle method (b).
Mentions: Figure 12, shows the performance of each algorithm in enhancing the process of detecting faces compared to the original image using both the Viola-Jones and Kienzle algorithms, respectively. We can see that Cartoonization has the highest efficiency in detecting faces which was expected as Cartoonization enhances the contrast between boundaries while keeping the color information in the scene intact. Edge overlaying on cartoon images is less effective compared to Cartoonization in detecting faces when using the Viola-Jones algorithm and there is not much difference between it and the Cartoonization alone when using the Kienzle method. The original images were ranked as third and edge overlay on original was ranked last. We find that the TRON algorithm is not efficient in detecting faces. This is because the Viola-Jones used rectangular features (templates) that compare relative intensities of adjacent regions, and the Kienzle method works on the intensity level of the image pixels. In contrast, the TRON algorithm focuses mainly on enhancing the edges over the salient information in the scene. This suppresses most of the intensity information in the image and keeps only the boundaries between contrast regions.

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