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results in the diagnosis of ROP and plus disease.
REFERENCES
Arcelli, C. and di Baja, G. S. (1996). Skeletons of pla-
nar patterns. In TY, K. and A, R., editors, Topo-
logical Algorithms for Digital Image Processing, vol-
ume 19 of Machine Intelligence and Pattern Recogni-
tion, pages 99–143. North-Holland, Amsterdam, The
Netherlands.
Chiang, M. F., Jiang, L., Gelman, R., Du, Y. E., and Flynn,
J. T. (2007). Interexpert agreement of plus disease
diagnosis in retinopathy of prematurity. Archives of
Ophthalmology, 125(7):875–880.
Cryotherapy for Retinopathy of Prematurity Cooperative
Group (2001). Multicenter trial of cryotherapy for
retinopathy of prematurity: Ophthalmological out-
comes at 10 years. Archives of Ophthalmology,
119:1110–1118.
Duda, R. O., Hart, P. E., and Stork, D. G. (2001). Pattern
Classification. Wiley, New York, NY, 2nd edition.
Efron, B. and Tibshirani, R. J. (1994). An Introduction to
the Bootstrap - Monographs on Statistics & Applied
Probability. Chapman and Hall/CRC, 1 edition.
Ells, A. L. and MacKeen, L. D. (2008). Dynamic documen-
tation of the evolution of retinopathy of prematurity
in video format. Journal of American Association for
Pediatric Ophthalmology and Strabismus, 12(4):349–
351.
Fiorin, D. and Ruggeri, A. (2011). Computerized analysis
of narrow-field ROP images for the assessment of ves-
sel caliber and tortuosity. In Engineering in Medicine
and Biology Society, 33rd Annual International Con-
ference of the IEEE, pages 2622–2625, Boston, MA.
Fleming, A. D., Goatman, K. A., Philip, S., Olson, J. A.,
and Sharp, P. (2007). Automatic detection of reti-
nal anatomy to assist diabetic retinopathy screening.
Physics in Medicine and Biology, 52:331–345.
Foracchia, M., Grisan, E., and Ruggeri, A. (2004). Detec-
tion of optic disc in retinal images by means of a geo-
metrical model of vessel structure. IEEE Transactions
on Medical Imaging, 23(10):1189–1195.
Gelman, R., Martinez-Perez, M. E., Vanderveen, D. K.,
Moskowitz, A., and Fulton, A. B. (2005). Diagno-
sis of plus disease in retinopathy of prematurity using
retinal image multiscale analysis. Investigative Oph-
thalmology & Visual Science, 46(12):4734–4738.
Goodman, S. N. (1999). Toward evidence-based medical
statistics. 1: The p value fallacy. Annals of Internal
Medicine, 130(12):995–1004.
Heneghan, C., Flynn, J. T., O’Keefe, M., and Cahill, M.
(2002). Characterization of changes in blood ves-
sels width and tortuosity in retinopathy of prematu-
rity using image analysis. Medical Image Analysis,
6(1):407–429.
Hildebrand, P. L., Ells, A. L., and Ingram, A. D. (2009). The
impact of telemedicine integration on resource use in
the evaluation ROP ... analysis of the telemedicine for
ROP in Calgary (TROPIC) database. Investigative
Ophthalmology and Visual Sciences, 50:E–Abstract
3151.
International Committee for the Classification of Retinopa-
thy of Prematurity (2005). The international clas-
sification of retinopathy of prematurity revisited.
Archives of Ophthalmology, 123:991–999.
Kay, S. M. (1993). Least squares. In Fundamentals of Sta-
tistical Signal Processing, Volume I: Estimation The-
ory, pages 219–286. Prentice Hall PTR.
Li, H. and Chutatape, O. (2004). Automated feature ex-
traction in color retinal images by a model based ap-
proach. IEEE Transactions on Biomedical Engineer-
ing, 51(2):246–254.
Liu, K. Y., Smith, M. R., Fear, E. C., and Rangayyan, R. M.
(2012). Evaluation and amelioration of computer-
aided diagnosis with artificial neural networks utiliz-
ing small-sized sample sets. Biomedical Signal Pro-
cessing and Control, Online:8. In press.
Metz, C. E. (1978). Basic principles of ROC analysis. Sem-
inars in Nuclear Medicine, VIII(4):283–298.
Oloumi, F., Rangayyan, R. M., and Ells, A. L. (2012a). A
graphical user interface for measurement of temporal
arcade angles in fundus images of the retina. In Cana-
dian Conference on Electrical and Computer Engi-
neering (CCECE), Proc. IEEE Canada 25th Annual,
pages 4 on CD–ROM, Montreal, QC, Canada.
Oloumi, F., Rangayyan, R. M., and Ells, A. L. (2012b). A
graphical user interface for measurement of the open-
ness of the retinal temporal arcade. In Medical Mea-
surements and Applications (MeMeA), Proc. IEEE In-
ternational Symposium on, pages 238–241, Budapest,
Hungary.
Oloumi, F., Rangayyan, R. M., and Ells, A. L. (2012c).
Parabolic modeling of the major temporal arcade in
retinal fundus images. IEEE Transactions on In-
strumentation and Measurement (TIM), 61(7):1825–
1838.
Oloumi, F., Rangayyan, R. M., and Ells, A. L. (2012d).
Quantitative analysis of the openness of the major
temporal arcade in retinal fundus images of retinopa-
thy of prematurity. In The First International Confer-
ence on Emerging Research in Electronics, Computer
Science and Technology, Leture Notes in Electrical
Engineering, pages 829–842, Mandya, Karnataka, In-
dia. Springer, LNEE.
Oloumi, F., Rangayyan, R. M., and Ells, A. L. (2013).
Quantitative analysis of the major temporal arcade in
retinal fundus images of preterm infants for detection
of plus disease. In The 15th International Association
of Science and Technology for Development (IASTED)
Conference on Signal and Image Processing, Banff,
Alberta, Canada.
Otsu, N. (1979). A threshold selection method from gray-
level histograms. IEEE Transactions on Systems, Man
and Cybernetics, SMC-9:62–66.
Peitgen, H. O., Jurgens, H., and Saupe, D. (2004). Chaos
and Fractals: New Frontiers of Science. Springer-
Verlag, New York, NY.
Poletti, E., Grisan, E., and Ruggeri, A. (2012). Image-
level tortuosity estimation in wide-field retinal images
from infants with retinopathy of prematurity. In Engi-
neering in Medicine and Biology Society, 34th Annual
Computer-aidedDiagnosisofRetinopathyofPrematurityviaAnalysisoftheVascularArchitectureinRetinalFundus
ImagesofPretermInfants
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