Automatic Recognition System for Maculopathy

Medical Image Processing: Automatic Recognition of Maculopathy

Diabetic-related eye diseases are the most common cause of blindness in the world. The screening of diabetic patients for the development of diabetic retinopathy potentially reduces the risk of blindness in these patients by 50%. Diabetic retinopathy can take two forms; background retinopathy, consisting of microaneurysms, haemorrhage, hard exudate,retinal edema, and sometimes microinfarcts of the retina (cotton wool spots),or proliferative retinopathy where new vessels develop in the retina and may bleed into the vitreous cavity.When background changes occur in the central retina, the condition is termed diabetic maculopathy, and visual acuity is at risk. This is the commonest sight threatening complication caused by diabetes.Much of this blindness can be prevented if the maculopathy is detected early enough for treatment with laser. Unfortunately, because visual loss is often a late symptom of advanced diabetic retinopathy, many patients remain undiagnosed even as their disease is causing severe retinal damage. Hence, there is an urgent need for mass-screening retinal examination for the early detection and treatment of diabetic retinopathy.

Current methods of detection and assessment of diabetic retinopathy are manual, expensive, potentially inconsistent, and require highly trained personnel to facilitate the process by searching large numbers of fundus images. Many of these images from screening programmes will be normal, but some will require grading of abnormalities (microaneurysms, haemorrhages, hard exudates, cotton wool spots) by severity and distance from the fovea (area of highest acuity), so that a judgement can be made on whether treatment is required. When abnormalities not requiring immediate treatment are found, the frequency of fundus imaging may be increased to every 4 to 6 months, and series of images compared to look for sight threatening trends requiring treatment. In contrast to this, a good, automatic method based on modern digital image processing technique will be faster, will need less may be no human intervention, and will yield consistent results.

The objectives of the proposed research project are to investigate methods for automated analysis of colour retinal images for the purpose of detecting and classifying early lesions related to diabetic maculopathy. The retinal image is analyzed automatically and an assessment of the level of maculopathy can be derived after analysis. The system will then be able to pick diabetic patients who need further examination. In addition to the detection and classification of lesions in diabetic retinopathy we will be quantifying the areas of diseased tissue. This will allow numerous measurements to be made of the deterioration in the condition over time, for example size, shape, edge sharpness, density etc. In this way the ophthalmologists will be able to objectively chart the progress of the disease. However to monitor the progression of maculopathy, we are going to do a serial study of each patient eye, with the images being taken, typically, months apart.

Staff and Students

Barry Thomas.
Majid Mirmehdi.
Alireza Osareh.

Publications

Alireza Osareh. Automated Identification of Diabetic Retinal Exudates and the Optic Disc. PhD Thesis, Department of Computer Sciene, University of Bristol, January 2004.
A Osareh, M Mirmehdi, B Thomas and R Markham. Automated identification of diabetic retinal exudates in digital colour images. British Journal of Ophthalmology,volume 87(10): 1220--1223, October 2003. Acrobat:1879487 bytes.
Richard Markham, Alireza Osareh, Majid Mirmehdi, Barry Thomas and Maria Macipe. Automated Identification of Diabetic Retinal Exudates using Support Vector Machines and Neural Networks. In: The Association for Research in Vision and Ophthalmology Conference, May 2003.
A. Osareh, M. Mirmehdi, B. Thomas and R. Markham. Comparative Exudate Classification using Support Vector Machines and Neural Networks. In: 5th International Conference on Medical Image Computing and Computer-Assisted Intervention, T. Dohi and R. Kikinis , editors, pages 413--420. Springer LNCS 2489, September 2002. Acrobat:426256 bytes.
Alireza Osareh, Majid Mirmehdi, Barry Thomas and Richard Markham. Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images. In: Proceedings of the 16th International Conference on Pattern Recognition , R. Kasturi, D. Laurendeau and C. Suen, editors, pages 743--746. IEEE Computer Society, August 2002. Acrobat:555867 bytes.
Alireza Osareh, Majid Mirmehdi, Barry Thomas and Richard Markham. Colour Morphology and Snakes for Optic Disc Localisation. In: The 6th Medical Image Understanding and Analysis Conference, A Houston and R Zwiggelaar, editors, pages 21--24. BMVA Press, July 2002. Acrobat:208159 bytes.
Alireza Osareh, Majid Mirmehdi, Barry Thomas and Richard Markham . Classification and Localisation of Diabetic-Related Eye Disease. In: 7th European Conference on Computer Vision, A. Heyden, G. Sparr, M. Nielsen and P. Johansen, editors, pages 502--516. Springer LNCS 2353, May 2002. Acrobat:677791 bytes.
A. Osareh, M. Mirmehdi, B. Thomas and R. Markham. Identifying Exudates in Diabetic Maculopathy. In: 2nd International Workshop on Computer Assisted Fundus Image Analysis, Bjarne Ersboll, editor, pages 17--17. TU Denmark, October 2001.
A. Osareh, M. Mirmehdi, B. Thomas and R. Markham. Locating the Optic Disk in Retinal Images. In: 2nd International Workshop on Computer Assisted Fundus Image Analysis, Bjarne Ersboll, editor, pages 35--35. TU Denmark, October 2001.
A. Osareh, M. Mirmehdi, B. Thomas and Richard Markham. Automatic Recognition of Exudative Maculopathy using Fuzzy C-Means Clustering and Neural Networks. In: Medical Image Understanding and Analysis, E Claridge and J Bamber, editors, pages 49--52. BMVA Press, July 2001. Acrobat:80027 bytes.

Collaborators

Bristol Eye Hospital,Bristol, BS1 2LX, U.K.