İristeki sodyum halkası genişliği ile kolesterol seviyesi arasındaki ilişkinin belirlenmesi

Yıl 2018, , 1557 - 1568, 19.12.2018

Çetin Kurnaz , Burak Kürşat Gül

https://doi.org/10.17341/gazimmfd.416452

Cited By: 2

Öz

Yüksek kolesterolün belirtilerinden olan sodyum halkası, iris çevresinde oluşan beyaza yakın parlaklıkta bir bölgedir. Bu halkanın incelenmesi ve değişiminin takip edilmesi, kolesterol seviyesinin belirlenmesi ve kontrol edilmesine alternatif bir yöntem olarak düşünülebilir. Bu çalışmada, sodyum halka genişliğinin hesaplanması, hesaplanan genişlik ile kolesterol seviyesi arasındaki ilişkinin belirlenmesi amaçlanmıştır. Bunun için 10 farklı hastadan elde edilen göz resimleri kullanılarak sodyum halka genişlikleri belirlenmiştir. Belirlenen bu değerler ile hastanın kolesterol değerleri arasındaki ilişki incenmiş ve literatürde var olmayan bir sodyum halka genişliği belirleme yöntemi önerilmiştir. Önerilen yöntem ile iriste meydana gelen sodyum halka genişliği kolesterol değerleri kullanılarak %85,3 doğrulukla belirlenebilmektedir.

Anahtar Kelimeler

İris, Sodyum halkası genişliği, Kolesterol, Biyomedikal görüntü işleme, Regresyon analizi.

Kaynakça

• Gasson M., Meints M., Warwick K., A study on PKI and Biometrics, Future of Identity in The Information Society (FIDIR) report. 2005, www.fidis. net/fileadmin/fidis/deliverables/fidis-wp3-del3.2.study_on_PKI_and_biometrics.pdf
• Szewczyk R., Jablonski P., Kulesza Z., Napieralski A., Cabestany J., Moreno M., Automatic People Identification on The Basis of Iris Pattern Extraction Features and Classification, 23rd International Conference Microelectronics, 691-694, 12-15 May, 2002.
• Bertillon A., La couleur de l'iris , Revue scientifique, France, 1985.
• Flom L., Safir A., Iris recognition system, 28 August 1986, US4641349.
• Daugman J., Biometric personal identification system based on iris analysis, 1994, US5291560.
• Boles, W. W., A security system based on human iris identification using wavelet transform, Engineering Applications of Artificial Intelligence, 11(2),77-85,1998.
• Wildes, R. P., Asmuth, J. C., Green, G. L., Hsu, S. C., Kolczynski, R. J., Matey, J. R., McBride, S. E., A system for automated iris recognition, IEEE Workshop on Applications of Computer Vision:121-128, 5-7 December, 1994.
• Daugman J., How iris recognition Works, IEEE Trans. on Circuits and Systems for Video Technology, 14 (1), 21-30, 2004.
• Sivasankar, K., Sujaritha, M., Pasupathi, P., Muthukumar, S., FCM based Iris image analysis for tissue imbalance stage identification, International Conference on Emerging Trends in Science Engineering and Technology, 13-14 December, 2012.
• Fausett L., Fundamental of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, New Jersey, 1994.
• Simon, A., Worthen, D. M., Mitas, J. A., An evaluation of iridology, The journal of the American Medical Association, 242(13), 1385-1389, 1979.
• Jensen B., Iridology Simplified-An Introduction to the Science of Iridology and its Relation to Nutrition, Iridologist International, 5th ed., Route 1 Box 52, Escondido, California, 1980.
• Lodin, A., Demea, S., Design of an iris-based medical diagnosis system, International Symposium on Signals, Circuits and Systems (ISSCS), 9-10 July, 2009.
• Amerifar, S., Targhi, A. T., Dehshibi, M. M., Iris the picture of health: Towards medical diagnosis of diseases based on iris pattern, The 10th International Conference on Digital Information Management, ICDIM 2015, 120-123, 21-23 October, 2015.
• Sitorus M. A. R., Purnomo M. H., Wibawa A. D., Iris image analysis of patient Chronic Renal Failure (CRF) using watershed algorithm, 4th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering, 54-58, 2-3 November, 2015.
• Sulistiyo M. D., Dayawati R. N., Pahirawan P. M., Iridology-based dyspepsia early detection using linear discriminant analysis and Cascade Correlation Neural Network, 2nd International Conference on Information and Communication Technology (ICoICT), 139-144, 28-30Mya, 2014.
• Jensen B. , The science and practice of iridology, Whitman Publications, 1952.
• Wibawa A. D., Purnomo M. H., Early detection on the condition of Pancreas organ as the cause of diabetes mellitus by real time iris image processing, IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings (APCCAS), 1008-1010, 4-7 December, 2006.
• Othman, Z., Satria P. A., Preliminary study on iris recognition system: Tissues of body organs in iridology, IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 115-119, 30 November- 2 December, 2010.
• Larsson M., Human Iris Characteristics as Biomarkers for Personality, Doctoral thesis, Öre-bro University, Department of Behavioral, Social and Legal Sciences, 2007.
• Larsson M., Pedersen N. L., Stattin H., Associations between iris characteristics and personality in adulthood, Biological Psychology, 75(2), 165-175, 2007.
• Rosenberg A., Kagan J., Iris pigmentation and behavioral inhibition, Developmental Psychobiology, 20(4): 377-392, 1987.
• Shen B., Xu Y., Lu G., Zhang D, Detecting iris lacunae based on Gaussian filter, 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), 233-236, 26-28 November, 2007.
• Lai C., Chiu C., Health Examination Based on Iris Images, The 9th International Conference on Machine Learning and Cybernetics, 11-14 July, 2010.
• Ramlee R. A., Ranjit S., Using iris recognition algorithm, detecting cholesterol presence, International Conference on Information Management and Engineering (ICIME 2009), 714-717, 3-5 April, 2009.
• Ramlee R. A., Aziz K. A., Ranjit S., Esro M., Automated Detecting Arcus Senilis, Symptom for Cholesterol Presence Using Iris Recognition Algorithm, Journal of Telecommunication, Electronic and Computer Engineering, 3(2), 29-39, 2011.
• Ramlee R. A, Azha K., Ranjit S., Detecting Cholesterol Presence with Iris Recognition Algorithm, Biometric Systems, Design and Applications, InTech Publishers, 2011.
• Vikas B., Cholesterol Presence Detection Using Iris Recognition, International Journal of Technology and Science, 2(1), 2014.
• Sarika G. S., Madhuri S. J., Automated Detection of Cholesterol Presence using Iris Recognition Algorithm, International Journal of Computer Applications, 133(6), 41-45, 2016.
• Gül B. K., Kurnaz Ç., İris Analizi ile Kandaki Yüksek Kolesterolün Belirlenmesi, XX. Biyomedikal Mühendisliği Ulusal Toplantısı (BİYOMUT 2016), İzmir-Türkiye, 3-5 Kasım, 2016.
• Hanukoglu I., Steroidogenic enzymes: structure, function, and role in regulation of steroid hormone biosynthesis, J Steroid Biochem Mol Biol, 43 (8), 779–804, 1992.
• Razin S., Tully J. G., Cholesterol Requirement of Mycoplasmas, Journal of Bacteriology, 102 (2), 306–310, 1970.
• https://en.wikipedia.org/wiki/ Hypercholesterolemia, Erişim tarihi Nisan 12, 2017.
• https://tr.wikipedia.org/wiki/Kolesterol, Erişim tarihi Nisan 12, 2017.
• https://tr.wikipedia.org/wiki/Düşük_yoğunluklu_lipoprotein, Erişim tarihi Nisan 12, 2017.
• Daugman J., Iris Recognition, American Scientist, 89(4), 326-333, 2001.
• http://www.iridology-swansea.co.uk/corneal-arcus/, Erişim tarihi Nisan 12, 2017.
• http://sivasanta.blogspot.com.tr/2008/08/cholesterol-ring.html, Erişim tarihi Nisan 12, 2017.
• Brown, S. H, Multiple linear regression analysis: a matrix approach with MATLAB, Alabama Journal of Mathematic, 34, 1-3, 2009.