Technology of intellectual feature selection for a system of automatic formation of a coagulate plan on retina
Ilyasova N.Yu., Shirokanev A.S., Kupriyanov A.V., Paringer R.A.

IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, Russia;
Samara National Research University, Moskovskoye shosse, 34, 443086, Samara, Russia

Abstract:
The paper proposes a technology for effective feature selection to localize individual characteristics of anatomical and pathological structures in the human eye fundus. Such an approach allows the intellectual analysis of features to be conducted using color subspaces and the regions of interest to be identified. This problem is relevant because in this way the efficiency of laser coagulation surgery can be improved. The technology is based on the texture analysis of certain image patterns. The initial textural attributes are derived from different statistical image descriptors calculated using the MaZda library (image histogram, image gradient, series length and adjacency matrices). The analysis of the feature space informativity and selection of the most effective features are carried out using the discriminant data analysis. The best-size image fragmentation windows for eye fundus clustering and sets of features that provide the necessary accuracy in identifying the regions of interest were derived via analyzing the following four image classes: exudates, thick vessels, thin vessels, and healthy areas. The feature selection technology was based on clustering using a K-means method, with the Euclidean and Mahalanobis distance used as a similarity measure. The required minimum size of the fragmentation window and the similarity measure were chosen from a criterion of the minimum clustering error among all the smallest window sizes. The article also presents a system for automatically forming a coagulate plan, expected to be used to support the decision-making during laser retinal coagulation surgery in the treatment of diabetic macular edema. This system is currently being developed based on the proposed technology.

Keywords:
laser coagulation, eye fundus, fundus images; textural features; data mining; feature selection.

Citation:
Ilyasova NYu, Shirokanev AS, Kupriyanov AV, Paringer RA. Technology of intellectual feature selection for a system of automatic formation of a coagulate plan on retina. Computer Optics 2019; 43(2): 304-315. DOI: 10.18287/2412-6179-2019-43-2-304-315.

References:

  1. Dedov II, Shestakova MV, Vikulova OK. The state register of diabetes in the Russian Federation: status of 2014 and development perspectives [In Russian]. Diabetes 2015; 18(3): 5-23.
  2. Dedov II, Shestakova MV, Galstyan GP. The prevalence of type 2 diabetes in the adult population of Russia (NATION study) [In Russian]. Diabetes 2016; 19(2): 104-112.
  3. Zhang X, Saaddine JB, Chou CF, Cotch MF, Cheng YJ, Geiss LS, Gregg EW, Albright AL, Klein BE, Klein R. Prevalence of diabetic retinopathy in the United States, 2005-2008. JAMA 2010; 304: 649-656.
  4. Wong TY, Liew G, Tapp RJ, Schmidt MI, Wang JJ, Mitchell P, Klein R, Klein BE, Zimmet P, Shaw J. Relation between fasting glucose and retinopathy for diagnosis of diabetes: three population-based cross-sectional studies. Lancet 2008; 371(9614): 736-743.
  5. Sakata K, Funatsu H, Harino S, Noma H, Hori S. Relationship of macular microcirculation and retinal thickness with visual acuity in diabetic macular edema. Ophthalmology 2007; 114(11): 2061-2069.
  6. Doga AV,Pedanova EK, Buryakov DA. Modern diagnostic and treatment aspects of diabetic macular edema [In Russian]. Ophthalmology, Diabetes 2014; 4: 51-59.
  7. Astakhov YS, Shadrichev FE, Krasavina MI, Grigoryeva NN. Modern approaches to the treatment of a diabetic macular edema. Ophthalmologic sheets 2009; 4: 59-69.
  8. Zamytskiy EA, Zolotarev AV, Karlova EV, Zamytskiy PA. Analysis of the coagulates intensity in laser treatment of diabetic macular edema in a Navilas robotic laser system [In Russian]. Saratov Journal of Medical Scientific Research 2017; 13(2): 375-378.
  9. Krylova IA, Goidin AP, Fabrikantov OL. Diabetic macular edema laser treatment [In Russian]. Modern Technology in Ophthalmology 2017; 1: 147-149.
  10. Chuprov AD, Iluhin DA. Micropulse laser effects in diabetic macular edema treatment [In Russian]. Modern Technology in Ophthalmology 2017; 1: 327-329.
  11. Kernt M, Cheuteu R, Liegl RG, Seidensticker F, Cserhati S, Hirneiss C, Haritoglou C, Kampik A, Ulbig M, Neubauer AS. Navigated focal retinal laser therapy using the NAVILAS system for diabetic macula edema. Ophthalmology 2012; 109(7): 692-700.
  12. Park HY, Kim IT, Park CK. Early diabetic changes in the nerve fibre layer at the macula detected by spectral domain optical coherence tomography. The British Journal of Ophthalmology 2011; 95(9): 1223-1228.
  13. Thomas RL, Dunstan F, Luzio SD, Chowdury SR, Hale SL, North RV, Gibbins RL, Owens DR. Incidence of diabetic retinopathy in people with type 2 diabetes mellitus attending the diabetic retinopathy screening service for wales: retrospective analysis. BMJ 2012; 344: e874.
  14. Litjens G, Kooi T, Bejinordi BE, Adiyoso AAS, Ciompi F, Ghafoorian M, van der Laak JAWM, Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Medical Image Analysis 2017; 42: 60-88.
  15. Deák GG, Bolz M, Ritter M, Prager S, Benesch T, Schmidt-Erfurth U. A systematic correlation between morphology and functional alterations in diabetic macular edema. Invest Ophthalmol Vis Sci 2010; 51(12): 6710-6714.
  16. Ilyasova NYu, Paringer RA, Kupriyanov AV. Regions of interest in a fundus image selection technique using the discriminative analysis methods. In Book: Chmielewski LJ, Datta A, Kozera R, Wojciechowski K, eds. Computer vision and graphics (ICCVG 2016). Cham: Springer; 2016: 408-417. DOI: 10.1007/978-3-319-46418-3_36.
  17. Daginawala N, Li B, Buch K, Yu H, Tischler B, Qureshi MM, Soto JA, Anderson S. Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. European Journal of Radiology 2016; 85(3): 511-517.
  18. Gentillon H, Stefańczyk L, Strzelecki M, Respondek M, Liberska. Parameter set for computer-assisted texture analysis of fetal brain. BMC Research Notes 2016; 9: 496.
  19. Acharya UR, Ng EY, Tan JH, Sree SV, Ng KH. An integrated index for the identification of diabetic retinopathy stages using texture parameters. Journal of Medical Systems 2012; 36(3): 2011-2020.
  20. Hajek M, Dezortova M, Materka A, Lersk R, eds. Texture analysis for magnetic resonance imaging. Prague: Med4publishing; 2006.
  21. Strzelecki M, Szczypinski P, Materka A, Klepaczko A. A software tool for automatic classification and segmentation of 2D/3D medical images. Nuclear Instruments and Methods In Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2013; 702: 137-140.
  22. Szczypiński M, Strzelecki M, Materka A, Klepaczko A. MaZda – A software package for image texture analysis. Computer Methods and Programs in Biomedicine 2009; 94(1): 66-76.
  23. Ilyasova NYu, Kupriyanov AV, Paringer RA. The discriminant analysis application to refine the diagnostic features of blood vessels images. Optical Memory and Neural Networks (Information Optics) 2015; 24: 309-313. DOI: 10.3103/S1060992X15040037.
  24. Fukunaga K. Introduction to statistical pattern recognition. New York, London: Academic Press; 1972.
  25. Kim J-O, Mueller ChW, Klecka WR. Factor, discriminant, and cluster analysis. Beverly Hills, CA: Sage Publications; 1989.
  26. Ilyasova NYu, Shirokanev AS, Paringer RA, Kupriyanov AV. A modified technique for smart textural feature selection to extract retinal regions of interest using image pre-processing. Journal of Physics: Conference Series 2018; 1096: 012095. DOI: 10.1088/1742-6596/1096/1/012095.
  27. Ilyasova NYu, Kupriyanov AV, Paringer RA, Kirsh DV, Shirokanev AS, Soifer VA. Big data application for smart features formation in medical diagnostic tasks.               Proc Int Conf Patt Recog Artif Intell 2018: 597-601.
  28. Shirokanev AS, Ilyasova NYu, Paringer PA. A smart feature selection technique for segmentation of fundus images. Proc IV International Conference on Information Technology and Nanotechnology (ITNT-2018) 2018: 2463-2473.
  29. Shirokanev AS, Kirsh DV, Ilyasova NYu, Kupriyanov AV. Investigation of algorithms for coagulate arrangement in fundus images. Computer Optics 2018; 42(4): 712-721.
  30. Ilyasova NYu, Kirsh DV, Paringer RA, Kupriyanov AV, Shirokanev AS, Zamycky EA. Coagulate map formation algorithms for laser eye treatment. 3rd International Conference on Frontiers of Signal Processing, ICFSP 2017: 120-124. DOI: 10.1109/ICFSP.2017.8097154.
  31. Nikitaev VG, Pronichev AN, Chistov KS, Khorkin VA. Method for recognition of cell texture image. Pat RF of Invent N 2385494 of March 3, 2010, Russian Bull of Inventions N9, 2010.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: ko@smr.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20