Abstract
The artificial intraocular lens (IOL) is inserted to replaced the crystalline lens of the human eye after cataract surgery or refraction clear lens extraction. When the IOL, is in an aquatic environment, glistenings, which are liquid-filled microvacuoles, can be discriminated from normal state. Automatic detection of glistenings is a new problem, they have tiny sizes, sometimes have low contrast and also similar with the lens background. In this paper, the candidate glistenings are automatically detected by mathematic morphology method and fine segmented using the classifiers, we used k-nearest neighbor (kNN) and Naïve Bayes for comparing the results. The detected glistenings are validated by object-based with ophthalmologists hand-drawn ground-truth. The result shows that classification can improve the performance of glistenings detection better than using only morphology method. The proposed software was developed for creating an effective automatic glistenings detection and quantification with a user-friendly Graphical User Interface,reliable results. Thus this computer-aid tool will help the researcher analyze the experiment results to better understand glistenings characteristic that have an effect on vary conditions.










Similar content being viewed by others
References
Abarghouei A, Ghanizadeh A, Sinaie S, Shamsuddin S (2009) A Survey of Pattern Recognition Applications in Cancer Diagnosis. Proc. International Conference of Soft Computing and Pattern Recognition, pp 448–453
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185
Bellucci R (2013) An introduction to intraocular lenses: material, optics, haptics, design and aberration
Byun J, Verardo MR, Sumengen B, Lewis GP, Manjunath BS, Fisher SK (2006) Automated tool for nuclei detection in digital microscopic images: application to retinal images. Mol Vis 12:949–960
Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10):R100
Datta NS, Sarker R, Dutta HS, De M (2012) Software based automated early detection of diabetic retinopathy on non dilated retinal image through mathematical morphological process. Int J Comput Appl 60(18):20–24
Davies E (1990) Machine vision: theory, algorithms and practicalities, Chap. 5. Academic Press, London
Deza E, Deza MM (2009) Encyclopedia of distances. Springer, p 94
Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163
Gillam P (2014) How do the muscles in the iris bring about the pupil reflex?. [image] Available at: https://pmgbiology.com/tag/pupil-reflex/. Accessed 8 Feb 2017
Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River
Gregori NZ, Spencer TS, Mamalis N, Olson RJ (2002) In vitro comparison of glistening formation among hydrophobic acrylic intraocular lenses. J Cataract Refract Surg 28(7):1262–1268
Gunenc U, Oner FH, Tongal S, Ferliel M (2001) Effects on visual function of glistenings and folding marks in AcrySof intraocular lenses. J Cataract Refract Surg 27(10):1611–1614
Hayashi K, Hirata A, Yoshida M, Yoshimura K, Hayashi H (2012) Long-term effect of surface light scattering and glistenings of intraocular lenses on visual function. Am J Ophthalmol 154(2):240–251.e2
Japunya T, Jitpakdee P, Uyyanonvara B, Aimmanee P, Philippaki E, Hull C, Barman S (2014) Software for the Quantification of Glistenings in Intra-Ocular Lenses. Proceedings of the World Congress on Engineering, vol I. WCE 2014, London, 2–4 July 2014, pp 34–38
Kato K, Nishida M, Yamane H, Nakamae K, Tagami Y, Tetsumoto K (2001) Glistening formation in an AcrySof lens initiated by spinodal decompositionof the polymer network bytemperature change. J Cataract Refract Surg 27(9):1493–1498
Malley M (1995) AcrySof `glistenings' and questions of haze. Ophthalmology Times 20(18):1
Mamalis N (2012) Intraocular lens glistenings. J Cataract Refract Surg 38(7):1119–1120
Mönestam E, Behndig A, Medicinska fakulteten, Oftalmiatrik, Institutionen för klinisk vetenskap & Umeå universitet (2011) Impact on visual function from light scattering and glistenings in intraocular lenses, a long-term study. Acta Ophthalmol 89(8):724–728
Oshika T, Shiokawa Y, Amano S, Mitomo K (2001) Influence of glistenings on the optical quality of acrylic foldable intraocular lens. Br J Ophthalmol 85(9):1034–1037 [online]. Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1724105/pdf/v085p01034.pdf
Rhodes A, Bai L (2011) Circle Detection Using a Gabor Annulus. Proceedings of the 22nd British Machine Vision Conference, Dundee
Sopharak A, Dailey M, Uyyanonvara B, Barman S, Tom W, New KT, Moe YA (2010) Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J Mod Opt 57(2):124–135
Sopharak A, Uyyanonvara B, Barman S (2012) Fine Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using a Hybrid Approach. Dr. Akara Sopharak The 2012 International Conference of Signal and Image Engineering (ICSIE), Imperial College, London, 4–6 July 2012
Sopharak A, Uyyanonvara B, Barman S (2014) Comparing SVM and Naïve Bayes Classifier for Automatic Microaneurysm Detections. ICISVC 2014: International Conference on Image, Signal and Vision Computing, in Tokyo, Japan, 29–30 May 2014
Tognetto D, Toto L, Sanguinetti G, Ravalico G (2002) Glistenings in foldable intraocular lenses. J Cataract Refract Surg 28(7):1211–1216
Usher D, Dumskyj M, Himaga M et al (2004) Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 21(1):84–90
van der Mooren M, Franssen L, Piers P (2013) Effects of glistenings in intraocular lenses. Biomedical Optics Express 4(8):1294–1304
Werner L (2010) Glistenings and surface light scattering in intraocular lenses. J Cataract Refract Surg 36(8):1398–1420
Zhang JG, Tan T, Li M (2002) Invariant Texture Segmentation via Circular Gabor Filters. In Proceedings of the 16th IAPR International Conference on Pattern Recognition (ICPR), Quebec City, 11–15 Aug 2002, pp 901–904
Acknowledgements
This research is funded by the National Research University Project of Thailand Office of Higher Education Commission (Thammasat University). We would like to thank the Applied Vision Research Centre, School of Health Sciences, City University London, for the IOL images and ground truth data.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jitpakdee, P., Uyyanonvara, B. Computer-aided detection and quantification in glistenings on intra-ocular lenses. Multimed Tools Appl 76, 18915–18928 (2017). https://doi.org/10.1007/s11042-017-4474-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-4474-7