Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms

Abdolreza Rashno, Dara Koozekanani, Paul M. Drayna, Behzad Nazari, Saeed Sadri, Hossein Rabbani, Keshab K Parhi

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65 Scopus citations


This paper presents a fully automated algorithm to segment fluid-associated (fluid-filled) and cyst regions in optical coherence tomography (OCT) retina images of subjects with diabetic macular edema. The OCT image is segmented using a novel neutrosophic transformation and a graph-based shortest path method. In neutrosophic domain, an image g is transformed into three sets: T (true), I (indeterminate) that represents noise, and F (false). This paper makes four key contributions. First, a new method is introduced to compute the indeterminacy set I , and a new \lambda -correction operation is introduced to compute the set T in neutrosophic domain. Second, a graph shortest-path method is applied in neutrosophic domain to segment the inner limiting membrane and the retinal pigment epithelium as regions of interest (ROI) and outer plexiform layer and inner segment myeloid as middle layers using a novel definition of the edge weights. Third, a new cost function for cluster-based fluid/cyst segmentation in ROI is presented which also includes a novel approach in estimating the number of clusters in an automated manner. Fourth, the final fluid regions are achieved by ignoring very small regions and the regions between middle layers. The proposed method is evaluated using two publicly available datasets: Duke, Optima, and a third local dataset from the UMN clinic which is available online. The proposed algorithm outperforms the previously proposed Duke algorithm by 8% with respect to the dice coefficient and by 5% with respect to precision on the Duke dataset, while achieving about the same sensitivity. Also, the proposed algorithm outperforms a prior method for Optima dataset by 6%, 22%, and 23% with respect to the dice coefficient, sensitivity, and precision, respectively. Finally, the proposed algorithm also achieves sensitivity of 67.3%, 88.8%, and 76.7%, for the Duke, Optima, and the university of Minnesota (UMN) datasets, respectively.

Original languageEnglish (US)
Pages (from-to)989-1001
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Issue number5
StatePublished - May 2018

Bibliographical note

Funding Information:
Manuscript received April 25, 2017; revised June 15, 2017; accepted July 21, 2017. Date of publication July 31, 2017; date of current version April 19, 2018. This work was supported in part by the Minnesota Lions and in part by an unrestricted Grant from Research to Prevent Blindness. (Corresponding author: Keshab K. Parhi.) A. Rashno is with the Isfahan University of Technology and also with the University of Minnesota.

Publisher Copyright:
© 1964-2012 IEEE.


  • Diabetic macular edema
  • fluid/cyst segmentation
  • graph theory
  • neutrosophic set
  • optical coherence tomography


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