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Professor of Ophthalmology and Visual SciencesProfessor of
Electrical & Computer Engineering,
Primary Office: 11205 PFPIowa City, IA 52242
Primary Office Phone: 319-384-5833
Email: email@example.comWeb: More About Dr. Abramoff - RELATED WEB SITES & RESOURCESWeb: NARCIS Netherlands Academic Research NetworkWeb: Researcher ID
MS, Medicine, University of AmsterdamMS, Biomedical Informatics, University of AmsterdamMD, Medicine, University of AmsterdamPhD, Biomedical Imaging, University of Utrecht
Post Doctoral, Research Fellowship, RIKEN Neural Networks Research Laboratory, Wako-shi, Saitama Prefecture, JapanResidency, Surgery, University Hospital of Maastricht, NetherlandsResidency, Ophthalmology, University Hospital of UtrechtFellowship, Medical Retina, Vrije Universiteit University Hospital, AmsterdamClinical Epidemiology Postgraduate Degree, Vrije Universiteit, Amsterdam, NetherlandsFellowship, Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics
State of Iowa Medical License, Iowa Board of MedicineNetherlands Ophthalmology Certification, Netherlands Ophthalmologic Society (Netherlands Board of Ophthalmology)USMLE I and II, ECFMG certificationMedical License, Netherlands
Biosciences Graduate ProgramInterdisciplinary Graduate Program in Translational BiomedicineMedical Scientist Training Program
The long range goal of Dr. Abramoff's research is to make computer aided diagnosis and digital retinal imaging for the screening, diagnosis and measurement of diabetic retinopathy, age related macular degeneration and glaucoma, patient friendly, low-cost and effective. These diseases are the big three causes of blindness in the US and most of the developing world, and screening and timely treatment is known to be effective or even cost-effective. Dr. Abramoff and his students and coworkers are developing novel methods for computer aided diagnosis and image analysis, retinal imaging at ultra wide field and ultra high resolution, image guided laser therapy of the retina. His research combines clinical ophthalmology, visual neuroscience and bioinformatics to study the phenotypes and genotypes of these diseases. Dr. Abramoff and coworkers have established large retinal imaging networks in the Midwest of the US and the Netherlands, with wide spread networks of retinal cameras connected through the internet to the University of Iowa, for screening of diabetic retinopathy. The tens of thousands of patients that are being image this way form the 'laboratory' for their research. Previously they have shown that computer aided diagnosis of diabetic retinopathy and glaucoma compares to retinal experts in small studies of hundreds of patients. They have developed image analysis algorithms that simulate the visual processing by the human brain to improve existing image analysis techniques, using for example computer simulations of simple and complex cell phase shifted gabor wavelet receptive fields. They are now starting to test the image processing algorithms on larger groups of patients collected through the network. Because computer aided diagnosis allows quantification of a retinal phenotype, preliminary studies are being performed to study the association of quantitative phenotype and genotype (SNP analysis) in age related macular degeneration and other retinal diseases in collaboration with the Carver Family Center for Macular Degeneration at the UI. An essential part of Dr. Abramoff's research is thus formed by an effective large scale network of high quality retinal imaging. Dr. Abramoff and coworkers have recently developed a patient-friendly, low cost camera, supported by an R01 from the NIH to build the network even more rapidly.
Carver Family Center for Macular DegenerationFraternal Order of Eagles Diabetes Research CenterIowa Institute for Biomedical Imaging
Automated analysis of retinal images for detection of referable diabetic retinopathy.
2013 March 1. 131(3):351-7.
Extending the XNAT archive tool for image and analysis management in ophthalmology research.
Proc SPIE Med Imag.
2013 February 14. 8674:86740M.
Changes in quantitative 3D shape features of the optic nerve head associated with age.
Proceedinsg SPIE Medical Imaging 2013: Computer-Aided Diagnosis .
2013 February 13. 8670:86700O.
Splat feature classification with application to retinal hemorrhage detection in fundus images.
IEEE Trans Med Imaging.
2013 February. 32(2):364-75.
Automated artery-venous classification of retinal blood vessels based on structural mapping method.
Proc SPIE Progr Biomed Optics Imaging.
Curvature correction of retinal OCTs using graph-based geometry detection.
Phys Med Biol.
Optimizing the information yield of 3-D OCT in glaucoma.
Invest Ophthalmol Vis Sci.
2012 December 13. 53(13):8162-71.
Quantification of external limiting membrane disruption caused by diabetic macular edema from SD-OCT.
Invest Ophthalmol Vis Sci.
2012 December 7. 53(13):8042-8.
Retinal vessel width measurement at branchings using an improved electric field theory-based graph approach.
2012 November 27. 7(11):e49668.
Automated segmentation of the choroid from clinical SD-OCT.
Invest Ophthalmol Vis Sci.
2012 November 1. 53(12):7510-9.
Date Last Modified: 06/07/2014 -
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