title: Designing and testing scene enhancement algorithms for patients with retina degenerative disorders.
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.
authors.affiliation: Institute of Biomedical Engineering, Imperial College, South Kensington, London, UK. walid.atbany06@imperial.ac.uk
authors.forename: Walid I
authors.initials: WI
authors.lastname: Al-Atabany
authors.forename: Muhammad A
authors.initials: MA
authors.lastname: Memon
authors.forename: Susan M
authors.initials: SM
authors.lastname: Downes
authors.forename: Patrick A
authors.initials: PA
authors.lastname: Degenaar
mesh_terms: D000465:Algorithms; D003116:Color; D015350:Contrast Sensitivity; D005128:Eye Diseases / Q000503:physiopathology*; D006801:Humans; D008954:Models, Biological; D010364:Pattern Recognition, Visual; D012160:Retina / Q000473:pathology / Q000502:physiology* / Q000503:physiopathology*; D014792:Visual Acuity