Pretreatment of tear film video in corneal topography_Journal of Biomedical Engineering

Authors：

CHEN Yanping , LIU Mengke ,  YAN Huangping , FANG Zheng

College of Aerospace Engineering, Xiamen University, Xiamen, Fujian 361102, P.R.China;

Corresponding?author：

YAN Huangping, Email: hpyan@xmu.edu.cn

Keywords：

corneal topography; tear film stability; image pretreatment; blur detection; morphological operation

DOI：

10.7507/1001-5515.201804033

Video：

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Abstract Full text Figures/Tables Video References Cited by

In the evaluation of tear film stability based on corneal topography, a pretreatment algorithm for tear film video was proposed for eye movement, eyelash reflection and background interference. First, Sobel operator was used to detect the blur image. Next, the target image with highlighted ring pattern was obtained by the morphological open operation performed on the grayscale image. Then the ring pattern frequency of the target image was extracted through the Hough circle detection and fast Fourier transform, and a band-pass filter was applied to the target image according to the ring pattern frequency. Finally, binarization and morphological closed operation were used for the localization of the ring pattern. Ten tear film videos were randomly selected from the database and processed frame by frame through the above algorithm. The experimental results showed that the proposed algorithm was effective in removing the invalid images in the video sequence and positioning the ring pattern, which laid a foundation for the subsequent evaluation of tear film stability.

Citation： CHEN Yanping, LIU Mengke, YAN Huangping, FANG Zheng. Pretreatment of tear film video in corneal topography. Journal of Biomedical Engineering, 2019, 36(6): 1018-1023. doi: 10.7507/1001-5515.201804033 Copy

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1. Nelson J D, Craig J P, Akpek E K, et al. TFOS DEWS II introduction. Ocul Surf, 2017, 15(3): 269-275.
2. Sweeney D F, Millar T J, Raju S R. Tear film stability: a review. Exp Eye Res, 2013, 117(12): 28-38.
3. Alonso-Caneiro D, Szczesna-Iskander D H, Iskander D R, et al. Application of texture analysis in tear film surface assessment based on videokeratoscopy. J Optom, 2013, 6(4): 185.
4. Buehren T, Lee B J, Collins M J, et al. Ocular microfluctuations and videokeratoscopy. Cornea, 2002, 21(4): 346-351.
5. Alonso-Caneiro D, Iskander D R, Collins M J. Estimating corneal surface topography in videokeratoscopy in the presence of strong signal interference. IEEE Trans Biomed Eng, 2008, 55(10): 2381-2387.
6. Iskander D R, Collins M J. Applications of high-speed videokeratoscopy. Clin Exp Optom, 2005, 88(4): 223-231.
7. Espinosa J, Roig A B, Mas D, et al. Corneal topography reinterpretation through separate analysis of the projected rings//Biophotonics: Photonic Solutions for Better Health Care III, Brussels, Belgium: International Society for Optics and Photonics, 2012, 8427(2): 741-770.
8. Carpente A, Ramos L, Barreira N, et al. On the automation of the tear film non-invasive break-up test//2014 IEEE 27th International Symposium on Computer-Based Medical Systems, New York, USA: IEEE Computer Society, 2014, 41(5): 185-188.
9. Schack T, Muma M, Weaam A, et al. A procedure to locate the eyelid position in noisy videokeratoscopic images. EURASIP Journal on Advances in Signal Processing, 2016(1): 136.
10. Morelande M R, Iskander D R, Collins M J, et al. Automatic estimation of the corneal limbus in videokeratoscopy. IEEE Trans Biomed Eng, 2002, 49(12): 1617-1625.
11. Iskander D R, Collins M J, Davis B. Evaluating tear film stability in the human eye with high-speed videokeratoscopy. IEEE Trans Biomed Eng, 2005, 52(11): 1939-1949.
12. Alonso-Caneiro D, Iskander D R, Collins M J. Assessment of tear film surface quality using dynamic-area high-speed videokeratoscopy. IEEE Trans Biomed Eng, 2009, 56(5): 1473-1481.
13. 隋成華, 韓勇浩, 徐丹陽, 等. 角膜地形圖儀中實時圖像檢測. 光學學報, 2017(6): 72-78.
14. Downie L E. Automated tear film surface quality breakup time as a novel clinical marker for tear hyperosmolarity in dry eye disease. Invest Ophthalmol Vis Sci, 2015, 56(12): 7260-7268.
15. Shi Changfa, Cheng Yuanzhi, Wang Jinke, et al. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Med Image Anal, 2017, 38: 30-49.
16. Wang Jinke, Cheng Yuanzhi, Guo Changyong, et al. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J Comput Assist Radiol Surg, 2016, 11(5): 817-826.
17. Marziliano P, Dufaux F, Winkler S, et al. Perceptual blur and ringing metrics: application to JPEG2000. Signal Processing: Image Communication, 2004, 19(2): 163-172.
18. Javaran T A, Hassanpour H, Abolghasemi V. Automatic estimation and segmentation of partial blur in natural images. Visual Computer, 2017, 33(2): 151-161.
19. Vu C T, Phan T D, Chandler D M. S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans Image Process, 2012, 21(3): 934-945.
20. 吳劍, 丁輝, 王廣志, 等. 邊緣檢測微分算子的分析及在醫學圖像中的應用. 生物醫學工程學雜志, 2005, 22(1): 82-85.
21. Alkhaldi W, Iskander D R, Zoubir A M, et al. Enhancing the standard operating range of a Placido disk videokeratoscope for corneal surface estimation. IEEE Trans Biomed Eng, 2009, 56(3): 800-809.

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