TY  - JOUR
T1  - Quantification of Ultra-Widefield Retinal Images
JF  - Retina Today
Y1  - 2014
A1  - D.E. Croft
A1  - C.C. Wykoff
A1  - D.M. Brown
A1  - van Hemert, J.
A1  - M. Verhoek
KW  - medical
KW  - retinal imaging
AB  - Advances in imaging periodically lead to dramatic changes in the diagnosis, management, and study of retinal disease. For example, the innovation and wide-spread application of fluorescein angiography and optical coherence tomography (OCT) have had tremendous impact on the management of retinal disorders.1,2 Recently, ultra-widefield (UWF) imaging has opened a new window into the retina, allowing the capture of greater than 80% of the fundus with a single shot.3 With montaging, much of the remaining retinal surface area can be captured.4,5 However, to maximize the potential of these new modalities, accurate quantification of the pathology they capture is critical.
UR  - http://www.bmctoday.net/retinatoday/pdfs/0514RT_imaging_Croft.pdf
ER  - 

TY  - CONF
T1  - Automatic Extraction of the Optic Disc Boundary for Detecting Retinal Diseases
T2  - 14th {IASTED} International Conference on Computer Graphics and Imaging (CGIM)
Y1  - 2013
A1  - M.S. Haleem
A1  - L. Han
A1  - B. Li
A1  - A. Nisbet
A1  - van Hemert, J.
A1  - M. Verhoek
ED  - L. Linsen
ED  - M. Kampel
KW  - retinal imaging
AB  - In this paper, we propose an algorithm based on active shape model for the extraction of Optic Disc boundary. The determination of Optic Disc boundary is fundamental to the automation of retinal eye disease diagnosis because the Optic Disc Center is typically used as a reference point to locate other retinal structures, and any structural change in Optic Disc, whether textural or geometrical, can be used to determine the occurrence of retinal diseases such as Glaucoma. The algorithm is based on determining a model for the Optic Disc boundary by learning patterns of variability from a training set of annotated Optic Discs. The model can be deformed so as to reflect the boundary of Optic Disc in any feasible shape. The algorithm provides some initial steps towards automation of the diagnostic process for retinal eye disease in order that more patients can be screened with consistent diagnoses. The overall accuracy of the algorithm was 92% on a set of 110 images.
JF  - 14th {IASTED} International Conference on Computer Graphics and Imaging (CGIM)
PB  - {ACTA} Press
ER  -