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Method for selection macular edema region using optical coherence tomography data

Kompʹûternaâ optika, 2020-04, Vol.44 (2), p.250-258 [Peer Reviewed Journal]

ISSN: 0134-2452 ;EISSN: 2412-6179 ;DOI: 10.18287/2412-6179-CO-691

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  • Title:
    Method for selection macular edema region using optical coherence tomography data
  • Author: Ilyasova, N.Yu ; Demin, N.S. ; Shirokanev, A.S. ; Kupriyanov, A.V. ; Zamytskiy, E.A.
  • Subjects: diabetic retinopathy ; eye fundus ; graph-based image segmentation ; laser coagulation ; oct images ; sift ; surf descriptors
  • Is Part Of: Kompʹûternaâ optika, 2020-04, Vol.44 (2), p.250-258
  • Description: The paper proposes a method for selection the region of diabetic macular edema in fundus images using OCT data. The relevance of the work is due to the need to create support systems for laser coagulation to increase its effectiveness. The proposed algorithm is based on a set of image segmentation methods, as well as searching for specific points and compiling their descriptors. The Canny method is used to find the boundary between the vitreous body and the retina in OCT images. The segmentation method, based on the Kruskal algorithm for constructing the minimum spanning tree of a weighted connected undirected graph, is used to select the retina to the pigment layer in the image. Using the results of segmentation, a map of the thickness of the retina of the eye and its deviation from the norm were constructed. In the course of the research, the optimal parameter values were selected in the Canny and graph segmentation algorithms, which allow to achieve a segmentation error of 5 %. SIFT, SURF, and AKAZE methods were considered for super-imposing calculated maps of the retina thickness and its deviation from the norm on the fundus image. In cases where a picture from the fundus camera of the OCT apparatus is provided along with OCT data, using the SURF method, it is possible to accurately combine with the fundus image.
  • Publisher: Samara National Research University
  • Language: English
  • Identifier: ISSN: 0134-2452
    EISSN: 2412-6179
    DOI: 10.18287/2412-6179-CO-691
  • Source: DOAJ : Directory of Open Access Journals
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