TY  - JOUR
T1  - Precise montaging and metric quantification of retinal surface area from ultra-widefield fundus photography and fluorescein angiography
JF  - Ophthalmic Surg Lasers Imaging Retina
Y1  - 2014
A1  - Croft, D.E.
A1  - van Hemert, J.
A1  - Wykoff, C.C.
A1  - Clifton, D.
A1  - Verhoek, M.
A1  - Fleming, A.
A1  - Brown, D.M.
KW  - medical
KW  - retinal imaging
AB  - BACKGROUND AND OBJECTIVE: Accurate quantification of retinal surface area from ultra-widefield (UWF) images is challenging due to warping produced when the retina is projected onto a two-dimensional plane for analysis. By accounting for this, the authors sought to precisely montage and accurately quantify retinal surface area in square millimeters. PATIENTS AND METHODS: Montages were created using Optos 200Tx (Optos, Dunfermline, U.K.) images taken at different gaze angles. A transformation projected the images to their correct location on a three-dimensional model. Area was quantified with spherical trigonometry. Warping, precision, and accuracy were assessed. RESULTS: Uncorrected, posterior pixels represented up to 79% greater surface area than peripheral pixels. Assessing precision, a standard region was quantified across 10 montages of the same eye (RSD: 0.7%; mean: 408.97 mm(2); range: 405.34-413.87 mm(2)). Assessing accuracy, 50 patients' disc areas were quantified (mean: 2.21 mm(2); SE: 0.06 mm(2)), and the results fell within the normative range. CONCLUSION: By accounting for warping inherent in UWF images, precise montaging and accurate quantification of retinal surface area in square millimeters were achieved. [Ophthalmic Surg Lasers Imaging Retina. 2014;45:312-317.].
VL  - 45
ER  - 

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  - JOUR
T1  - Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review
JF  - Computerized Medical Imaging and Graphics
Y1  - 2013
A1  - Haleem, M.S.
A1  - Han, L.
A1  - van Hemert, J.
A1  - Li, B.
KW  - retinal imaging
AB  - Glaucoma is a group of eye diseases that have common traits such as, high eye pressure, damage to the Optic Nerve Head and gradual vision loss. It affects peripheral vision and eventually leads to blindness if left untreated. The current common methods of pre-diagnosis of Glaucoma include measurement of Intra-Ocular Pressure (IOP) using Tonometer, Pachymetry, Gonioscopy; which are performed manually by the clinicians. These tests are usually followed by Optic Nerve Head (ONH) Appearance examination for the confirmed diagnosis of Glaucoma. The diagnoses require regular monitoring, which is costly and time consuming. The accuracy and reliability of diagnosis is limited by the domain knowledge of different ophthalmologists. Therefore automatic diagnosis of Glaucoma attracts a lot of attention. This paper surveys the state-of-the-art of automatic extraction of anatomical features from retinal images to assist early diagnosis of the Glaucoma. We have conducted critical evaluation of the existing automatic extraction methods based on features including Optic Cup to Disc Ratio (CDR), Retinal Nerve Fibre Layer (RNFL), Peripapillary Atrophy (PPA), Neuroretinal Rim Notching, Vasculature Shift, etc., which adds value on efficient feature extraction related to Glaucoma diagnosis.
VL  - 37
SN  - 0895-6111
UR  - http://linkinghub.elsevier.com/retrieve/pii/S0895611113001468?showall=true
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  - 

TY  - CONF
T1  - Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams
T2  - Conf Proc IEEE Eng Med Biol Soc
Y1  - 2013
A1  - Zutis, K.
A1  - Trucco, E.
A1  - Hubschman, J. P.
A1  - Reed, D.
A1  - Shah, S.
A1  - van Hemert, J.
KW  - retinal imaging
AB  - Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822.
JF  - Conf Proc IEEE Eng Med Biol Soc
ER  -