Personal data protection with smart cards using eye-ground image recognition technique
Abstract
Diabetes-induced pathologies are among the major causes, worldwide, of poor sight and blindness and are nowadays the least identifiable and treatable diseases. The resultant severe pathological changes entail persistent loss of visuality functions in patients over 50 [1, 2, 3, 4]. In recent years, such pathologies tend to become “younger”. Actually, early manifestations of diabetes-triggered eye-ground pathological changes are ophthalmoscopied even at the age of 12 to 20 years [5]. It is noteworthy that a significant rise of morbidity rate is observed among the able-bodied categories of the population, inasmuch as the longevity of older people has increased, thereby increasing their share in the overall population [6]. In the USA, eye-ground pathologies hold the second place, after diabetes, among the causes of blindness. In Ukraine, the situation, as to the extent of diabetes-induced eye-ground pathologies, is worsening all the time [7]. For instance, for the last 20 years, the annual quantity of the first-revealed sight-disabled patients suffering from such pathology has increased 2.5 times [6].
References
Starr C. E., Guyer D. R., Yannuzzi L. A. Age-related macular degeneration. Can we stem this worldwide public health crisis? Postgrad. Med. 1998. Vol. 103, N 5. P. 153–156, 161–164.
Dougherty G.: Image analysis in medical imaging: recent advances in selected examples. Biomed. Imaging Interv. J. 2010. 6(3), e32.
Beutel J., Kundel H. L., Van Metter R. L. Handbook of Medical Imaging, vol. 1. SPIE, Bellingham, Washington, 2000.
Rangayyan R. M.: Biomedical Image Analysis. CRC, Boca Raton, FL, 2005.
Meyer-Base A. Pattern Recognition for Medical Imaging. Elsevier Academic, San Diego, CA, 2004.
Dougherty G. Digital Image Processing forMedical Applications. Cambridge University Press, Cambridge, 2009.
. Sanniti di baja G., Thiel E. Computing and comparing distancedriven skeleton. Proc. of 2nd international workshop on visual form. Italy, 1994. P. 475–486.
Identification of retinal vessels by color image analysis / V. Rakotomalala, L. Macaire, J.-G. Postaire, M. Valette // Machine graphics & vision. 1998. V. 7. № 4. P. 725–743.
Image manipulation using M-filters in a Pyramidal computer model / M. E. Montiel, A. S. Agueado, M. A. Garza-Jinich et al. // IEEE trans. on pattern analysis and machine intelligence. 1995. V. 17, 111. P. 1110–1115.
Soares J., Leandro J., Cesar Jr. R., Jelinek H., Cree M. Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification. IEEE Transactions of Medical Imaging, Vol. 25, No. 9, 2006, pp. 1214–1222.
Welk M., Breub M., Vogel O. Differential Equations for Morphological Amoebas. Lecture Notes in Computer Science, Vol. 5720/2009, 2009, Pp. 104–114.
Joshi G. D., Sivaswamy J. Colour Retinal Image Enhancement based on Domain Knowledge. Sixth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP'08), 2008, Pp. 591–598.
Nagashima S., Ito K., Aoki T., Ishii H., Kobayashi K. High Accuracy Estimation of Image Rotation using 1D Phase-Only Correlation // IEICE Trans.Fund. v. E92-A, P. 235–243, 2009.
Ritter G. X., Wilson J. N. Handbook of Computer Vision Algorithms in Image Algebra. CRC Press, Boca Raton, Florida, USA, 1996. 357 p.
Development of the electronic service system of a municipal clinic (based on the analysis of foreign webresources) / A. V. Lantsberg, Klaus G. Troitzch T.I. Buldakova. Automatic Documentationand Mathematical Linguistics. 2011. N. 2. V. 45. P. 74–80.