4.2 Article

Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior

Journal

Publisher

HINDAWI LTD
DOI: 10.1155/2019/8973287

Keywords

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Funding

  1. National Natural Science Foundation of China [61602221]
  2. Project of Doctoral Foundation of Shenyang Aerospace University [19YB01]
  3. Scientific Research Fund Project of Liaoning Provincial Department of Education [JYT19040]
  4. Natural Science Foundation of Liaoning Province Science and Technology Department [2019-ZD-0234]
  5. Science and Technology Research Project of Jiangxi Provincial Department of Education [GJJ160333]
  6. Natural Science Foundation of Jiangxi Province [20171BAB212009]

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Accurate optic disc and optic cup segmentation plays an important role for diagnosing glaucoma. However, most existing segmentation approaches suffer from the following limitations. On the one hand, image devices or illumination variations always lead to intensity inhomogeneity in the fundus image. On the other hand, the spatial prior knowledge of optic disc and optic cup, e.g., the optic cup is always contained inside the optic disc region, is ignored. Therefore, the effectiveness of segmentation approaches is greatly reduced. Different from most previous approaches, we present a novel locally statistical active contour model with the structure prior (LSACM-SP) approach to jointly and robustly segment the optic disc and optic cup structures. First, some preprocessing techniques are used to automatically extract initial contour of object. Then, we introduce the locally statistical active contour model (LSACM) to optic disc and optic cup segmentation in the presence of intensity inhomogeneity. Finally, taking the specific morphology of optic disc and optic cup into consideration, a novel structure prior is proposed to guide the model to generate accurate segmentation results. Experimental results demonstrate the advantage and superiority of our approach on two publicly available databases, i.e., DRISHTI-GS and RIM-ONE r2, by comparing with some well-known algorithms.

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