Mobile Screening Framework of Anterior Segment Photographed Images

  • Authors

    • N Syahira M Zamani
    • Laily Azyan Ramlan
    • W Mimi Diyana W Zaki
    • Aini Hussain
    • Haliza Abdul Mutalib
    https://doi.org/10.14419/ijet.v7i4.11.20780

    Received date: October 2, 2018

    Accepted date: October 2, 2018

    Published date: October 2, 2018

  • ASPI, computational time, mobile screening system, Otsu multi-thresholding approach, pterygium.

  • Abstract

    This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.

  • References

    1. Z.A. Hamid, Z. Harun, S.H. Lubis, N. Mohamed, I. Ishak, H. Fathi
    2. Othman, N. Z. M. Saat, J. Rafaai, M. R. M. Noor, S. Z. Jamil,
    3. Adoption of the mobile health screening programme for farming
    4. communities: A study among pesticide-exposed farmers from
    5. North East of Peninsular Malaysia, Jurnal Sains Kesihatan Malaysia (Malaysian Journal of Health Sciences) 122 (2014) 63-69.
    6. W.M.D.W. Zaki, M.M. Daud, S.R. Abdani, A. Hussain, H. A. Mutalib, Automated pterygium detection method of anterior segment
    7. photographed images, Computer Methods and Programs in Biomedicine 154 (2017) 1-78.
    8. Q. Le, J. Xiang, X. Cui, X. Zhou, J. Xu, Prevalence and associated
    9. factors of pinguecula in a rural population in Shanghai, Eastern
    10. China, Ophthalmic Epidemiology 22 (2015) 130-138.
    11. H.C. Yu, C.L. Lin, Z.T.Y. Chen, F.R. Hu, F.C. Sung, I.J. Wang,
    12. Risk of skin cancer in patients with pterygium: A nationwide population-based cohort study in Taiwan, Ocular Surface 12 (2014) 69-
    13. R. Mumtaz, M. Hussain, S. Sarwar, K. Khan, S. Mumtaz, M. Mumtaz, Automatic detection of retinal hemorrhages by exploiting im-age processing techniques for screening retinal diseases in diabetic patients, International Journal of Diabetes in Developing Countries 38 (2018) 80-87.
    14. P.H. Scanlon, R. Malhotra, G. Thomas , C. Foy, J.N. Kirkpatrick, N.Lewis‐Barned, B. Harney, S.J. Aldington, The effectiveness of creening for diabetic retinopathy by digital imaging photography and technician ophthalmoscopy, Diabetic Medicine 20 (2003) 467-474.
    15. S.R. Abdani, W.M.D.W. Zaki, A. Hussain, A. Mustapha, An adaptive nonlinear enhancement method using sigmoid function for iris
    16. segmentation in pterygium cases, Proceedings of the IEEE International Electronics Symposium, (2015) pp. 53-57.
    17. Paramount Health Care. Medical policy-corneal topography. 017,http://www.paramounthealthcare.com/documents/MedicalPolicy/P
    18. G0055_Corneal_Topography.pdf.
    19. C.L. Ventola, Mobile devices and apps for health care professionals: uses and benefits, Pharmacy and therapeutics 39 (2014) 356.
    20. F. Martínez, C. Tobar, C. Taramasco, Implementation of asmartphone application in medical education: A randomised trial (iSTART), BMC Medical Education 17 (2017) 1-9.
    21. Statista. Mobile app usage.
    22. https://www.statista.com/topics/1002/mobile-app-usage/.
    23. B.C. Toy, D.J. Myung, L. He, C.K. Pan, R.T. Chang, A. Polkinhorne, D. Merrell, D. Foster, M.S. Blumenkranz, Smartphonebased dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye isease,Retina 36 (2016) 1000-1008.
    24. J.T. Boruff, D. Storie, Mobile devices in medicine: A survey of how medical students, residents, and faculty use smartphones and other mobile devices to find information, Journal of the Medical Library Association 102 (2014) 22-30.
    25. A. Suzuki, N. Shimamura, T. Kainuma, N. Kawazu, C. Okada, T.Oka, K. Koiso, A. Masagaki, Y. Yagasaki, S. Gonoi, T. Ichikawa, 6.1 A 1/1.7-inch 20Mpixel back-illuminated stacked CMOS image sensor for new imaging applications, Proceedings of the IEEE International Solid-State Circuits Conference, (2015) pp. 1-3.[15] S.R. Abdani, W.M.D.W. Zaki, A. Mustapha, A. Hussain, Iris segmentation method of pterygium anterior segment photographed image, Proceedings of the IEEE Symposium on Computer Applications and Industrial Electronics, (2015) pp. 69–72.
    26. M.H.J. Vala , A. Baxi, A review on Otsu image segmentation algorithm, International Journal of Advanced Research in Computer
    27. Engineering and Technology 2 (2013) 387-389.
    28. N. Otsu, A threshold selection method from gray-level histograms,IEEE Trans. Syst. Man Cybern. 9, (1979) 62-66.
    29. D. Garcia. Image segmentation using Otsu thresholding. MathWorks. https://www.mathworks.com/matlabcentral/fileexchange/26532-image-segmentation-using-otsu-thresholding?focused=5151723&tab=function.
    30. A. Aminlari, R. Singh, D. Liang, Management of pterygium,Eyenet (2010) 37-38.
    31. A. Hoyle. A close look at the Huawei P9’s dual-lens camera.CNET. https://www.cnet.com/news/a-close-look-at-the-huawei-
    32. p9s-dual-lens-camera/.

  • Downloads

  • How to Cite

    Syahira M Zamani, N., Azyan Ramlan, L., Mimi Diyana W Zaki, W., Hussain, A., & Abdul Mutalib, H. (2018). Mobile Screening Framework of Anterior Segment Photographed Images. International Journal of Engineering and Technology, 7(4.11), 85-89. https://doi.org/10.14419/ijet.v7i4.11.20780