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AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES

Year 2020, Volume: 25 Issue: 1, 555 - 572, 30.04.2020

Nerma Corovıc Emel Arslan

https://doi.org/10.17482/uumfd.660759

Abstract

Human pupil abnormalities can be an indicator of many diseases. Anisocoria is a common condition that has been estimated at 20% of the average population. It is specified by inequality in the size of the pupils of the eyes. This paper proposes an anisocoria determining algorithm from a digital image by using the MATLAB computing environment that involves the usage of MATLAB Computer Vision and Image Processing Toolbox. The image used in this work as input data is an image that has been fetched from Siblings Image Database. An input image where anisocoria is present has been downloaded from the Internet. The paper gives an idea of understanding how pupil detection and measurement can be used in medical and psychology diagnostics by using a simple algorithm.

Keywords

Image Processing Anisocoria Diagnosis Pupil Detection Pupil Measurement

References

  • Basit, A., Javed, M. Y., & Masood, S. (2008). Non-circular Pupil Localization in Iris Images. 2008 4th International Conference on Emerging Technologies.
  • Dey, S., & Samanta, D. (2007). An Efficient Approach for Pupil Detection in Iris Images. 15th International Conference on Advanced Computing and Communications (ADCOM 2007).
  • Kaiser,P.K., Friedman, N. J., & Pineda, R. (2014). The Massachusetts Eye and Ear Infirmary Illustrated Manual of Ophthalmology (4th Edition) Elsevier Saunders. eBook ISBN: 9780323225274
  • Iacoviello, D. (2006). Analysis of pupil fluctuations after a light stimulus by image processing and neural network. Computers & Mathematics with Applications, 1260-1270.
  • Lin, L., Lin, P., LiFang, W., & Lun, Y. (2010). A robust and accurate detection of pupil images. 3rd International Conference on Biomedical Engineering and Informatics. Yantai, China: IEEE.
  • Mahmood, N. H., Uyop, N., Mansor, M. M., & Jumadi, A. M. (2011). Measurement of the Area and Diameter of Human Pupil Using Matlab. 5th Kuala Lumpur International Conference on Biomedical Engineering (p. vol 35. ). Berlin, Heidelberg: Springer.
  • Nowak, W., Zarowska, A., Szul-Pietrzak, E., & Misiuk-Hojło, M. (2014). System and measurement method for binocular pupillometry to study pupil size variability. BioMedical Engineering OnLine.
  • Pasarica, A., Cehan, V., Bozomitu, R. G., & Lupu, R. (2015). Pupil detection algorithms for eye tracking applications. 2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging.
  • Viola, P., & Jones, M. (2004). Robust real-time object detection. International Journal of Computer Vision, 137-154.

Görüntü İşleme Teknikleri Kullanarak Anizokori Hastalığını Teşhis Eden Uygulama

Year 2020, Volume: 25 Issue: 1, 555 - 572, 30.04.2020

Nerma Corovıc Emel Arslan

https://doi.org/10.17482/uumfd.660759

Abstract

Göz bebeğindeki anormallikler bir çok hastalığın belirtisi olabilir. Bu hastalıklardan biri olan Anizokori, iki göz bebeği büyüklüğünde bir eşitsizlik olma durumu ile karakterize edilmektedir. Anizokori normal popülasyonun %20'sinde görülen yaygın bir durumdur. Bu makalede, dijital görüntü üzerinde MATLAB hesaplama ortamı kullanılarak uygulanan görüntü işleme teknikleri ile her iki göz bebeğinin tespiti ve büyüklük karşılaştırması gerçekleştirilmekte ve elde edilen sonuçlar ışığında anizokor durumu olup olmadığının belirlenmesi için bir algoritma önerilmektedir. Söz konusu çalışmada giriş verisi ve test amaçlı kullanılmak üzere görüntüler Siblings Image Database ve internet ortamından temin edilmiştir. Bu makale, görüntü işleme algoritması kullanılarak, göz bebeğinin tespiti ve ölçümünün tıp ve psikoloji tanılarında nasıl kullanılabileceğini anlamaya yönelik fikir ortaya koymuş bir çalışmadır.

Keywords

Görüntü İşleme Anizokori Teşhis Gözbebeği Tespiti Göz Bebeği Ölçümü

References

  • Basit, A., Javed, M. Y., & Masood, S. (2008). Non-circular Pupil Localization in Iris Images. 2008 4th International Conference on Emerging Technologies.
  • Dey, S., & Samanta, D. (2007). An Efficient Approach for Pupil Detection in Iris Images. 15th International Conference on Advanced Computing and Communications (ADCOM 2007).
  • Kaiser,P.K., Friedman, N. J., & Pineda, R. (2014). The Massachusetts Eye and Ear Infirmary Illustrated Manual of Ophthalmology (4th Edition) Elsevier Saunders. eBook ISBN: 9780323225274
  • Iacoviello, D. (2006). Analysis of pupil fluctuations after a light stimulus by image processing and neural network. Computers & Mathematics with Applications, 1260-1270.
  • Lin, L., Lin, P., LiFang, W., & Lun, Y. (2010). A robust and accurate detection of pupil images. 3rd International Conference on Biomedical Engineering and Informatics. Yantai, China: IEEE.
  • Mahmood, N. H., Uyop, N., Mansor, M. M., & Jumadi, A. M. (2011). Measurement of the Area and Diameter of Human Pupil Using Matlab. 5th Kuala Lumpur International Conference on Biomedical Engineering (p. vol 35. ). Berlin, Heidelberg: Springer.
  • Nowak, W., Zarowska, A., Szul-Pietrzak, E., & Misiuk-Hojło, M. (2014). System and measurement method for binocular pupillometry to study pupil size variability. BioMedical Engineering OnLine.
  • Pasarica, A., Cehan, V., Bozomitu, R. G., & Lupu, R. (2015). Pupil detection algorithms for eye tracking applications. 2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging.
  • Viola, P., & Jones, M. (2004). Robust real-time object detection. International Journal of Computer Vision, 137-154.
There are 9 citations in total.

Details

Primary Language English
Subjects Software Testing, Verification and Validation
Journal Section Research Articles
Authors

Nerma Corovıc 0000-0002-5564-2620

Emel Arslan 0000-0003-4668-392X

Publication Date April 30, 2020
Submission Date December 18, 2019
Acceptance Date March 2, 2020
Published in Issue Year 2020 Volume: 25 Issue: 1

Cite

APA Corovıc, N., & Arslan, E. (2020). AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 25(1), 555-572. https://doi.org/10.17482/uumfd.660759
AMA Corovıc N, Arslan E. AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES. UUJFE. April 2020;25(1):555-572. doi:10.17482/uumfd.660759
Chicago Corovıc, Nerma, and Emel Arslan. “AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25, no. 1 (April 2020): 555-72. https://doi.org/10.17482/uumfd.660759.
EndNote Corovıc N, Arslan E (April 1, 2020) AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25 1 555–572.
IEEE N. Corovıc and E. Arslan, “AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES”, UUJFE, vol. 25, no. 1, pp. 555–572, 2020, doi: 10.17482/uumfd.660759.
ISNAD Corovıc, Nerma - Arslan, Emel. “AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 25/1 (April 2020), 555-572. https://doi.org/10.17482/uumfd.660759.
JAMA Corovıc N, Arslan E. AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES. UUJFE. 2020;25:555–572.
MLA Corovıc, Nerma and Emel Arslan. “AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 25, no. 1, 2020, pp. 555-72, doi:10.17482/uumfd.660759.
Vancouver Corovıc N, Arslan E. AN IMPLEMENTATION THAT DIAGNOSIS ANISOCORIA DISEASE USING IMAGE PROCESSING TECHNIQUES. UUJFE. 2020;25(1):555-72.

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