DALL·E 2023-12-01 12.20.04 - An illustration showcasing 'Diagnostic Retinal Image Analysis'. The image should feature a high-tech medical environment focused on eye health. In the

Diagnostic Retinal Image Analysis

AI For Medical Image Analytics

Diagnostic Retinal Image Analysis

Retinal imaging has rapidly grown within ophthalmology in the past twenty years. The availability of low cost fundus cameras to take direct images of the retina, fundus photography, makes it possible to examine the eye for the presence of many different eye diseases with a simple, non-invasive method. Many important eye diseases as well as systemic diseases manifest themselves in the retina.
The most prevalent causes of blindness in the modern world includes age-related macular degeneration, diabetic retinopathy, and glaucoma. The early detection of these ocular diseases will enable prompt referral to specialist centers for immediate treatment. Automated retinal image analysis (RIA) has huge potential to make retinal assessment more precise, reliable, and quantifiable beyond clinical diagnosis from pre-clinical screening to diagnosis to support clinical decision making.

Research Areas

Collaborators

Selected Publications

  • M.M. Fraz , M. Badar, A.W. Malik, S. A. Barman , “Computational Methods for Exudates Detection and Macular Edema Estimation in Retinal Images: A Survey”, Archives of Computational Methods in Engineering, Vol. 26 , No. 4, PP. 1193–1220, Sep, 2019. IF: 7.242
  • S. A. Badawi and M. M. Fraz , “Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules”, BioMed Research International, Vol. 2019 , No. 1, PP. 17, Apr, 2019. IF: 2.197
  • Christopher G Owen, Alicja R Rudnicka, Roshan A Welikala, M. Moazam Fraz, Sarah A Barman , Robert Luben, Shabina A Hayat, Kay-Tee Khaw, David P Strachan, Peter H Whincup, Paul J Foster , “Retinal vasculometry associations with cardiometabolic risk factors in the European Prospective Investigation of Cancer Norfolk study”, Ophthalmology, Vol. 126 , No. 1, PP. 96-106, Jan, 2019. IF: 7.732
  • M. M Fraz , W. Jahangir, S. Zahid, M. M. Hamayun, S. A. Barman , “Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification”, Biomedical Signal Processing and Control, Vol. 35 , No. 1, PP. 50-62, May, 2017. IF: 2.943
  • M.M. Fraz , R.A. Welikala, A.R. Rudnicka, C.G. Owen, D.P. Strachan, S.A. Barman , “QUARTZ: Quantitative Analysis of Retinal Vessel Topology and size – An automated system for quantification of retinal vessels morphology”, Expert Systems with Applications, Vol. 42 , No. 20, PP. 7221, Nov, 2015. IF: 4.292
  • M.M. Fraz , A. R. Rudnicka, C. G. Owen, S. A. Barman , “Delineation of blood vessels in paediatric retinal images using decision trees-based ensemble classification.”, International journal of computer assisted radiology and surgery, Vol. 9 , No. 5, PP. 795-811, Sep, 2014. IF: 2.155 
  • M.M. Fraz , A. Basit, S. A. Barman , “Application of Morphological Bit Planes in Retinal Blood Vessel Extraction”, Journal of Digital Imaging, Vol. 26 , No. 2, PP. 274-286, Apr, 2013. IF: 2.572 
  • M.M. Fraz , P. Remagnino, A. Hoppe, A. Rudnicka, C.G. Owen, P. H. Whincup, S.A. Barman, “Quantification of blood vessel calibre in retinal images of multi-ethnic school children using model based approach”, Computerized Medical Imaging and Graphics, Vol. 37, No. 1, PP. 48-60, Jan, 2013. IF: 3.298(27.) 
  • M.M. Fraz , S.A. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen , “An approach to localize the retinal blood vessels using bit planes and centerline detection”, Computer Methods and Programs in Biomedicine, Vol. 108 , No. 2, PP. 600-616, Nov, 2012. IF: 3.424
  • M.M. Fraz , P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, S.A. Barman, “Blood vessel segmentation methodologies in retinal images – A survey”, Computer Methods and Programs in Biomedicine, Vol. 108 , No. 1, PP. 407-433, Oct, 2012. IF: 3.424
  • M.M. Fraz , P. Remagnino, A. Hoppe, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen, S.A. Barman, “An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation”, IEEE Transactions on Biomedical Engineering, Vol. 59 , No. 9, PP. 2538-2548, Sep, 2012. IF: 4.491

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