TY - JOUR
T1 - Fully Automated Segmentation of Fluid/Cyst Regions in Optical Coherence Tomography Images with Diabetic Macular Edema Using Neutrosophic Sets and Graph Algorithms
AU - Rashno, Abdolreza
AU - Koozekanani, Dara
AU - Drayna, Paul M.
AU - Nazari, Behzad
AU - Sadri, Saeed
AU - Rabbani, Hossein
AU - Parhi, Keshab K
N1 - Publisher Copyright:
© 1964-2012 IEEE.
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Diabetic macular edema
KW - fluid/cyst segmentation
KW - graph theory
KW - neutrosophic set
KW - optical coherence tomography
UR - http://www.scopus.com/inward/record.url?scp=85028393046&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028393046&partnerID=8YFLogxK
U2 - 10.1109/TBME.2017.2734058
DO - 10.1109/TBME.2017.2734058
M3 - Article
C2 - 28783619
AN - SCOPUS:85028393046
SN - 0018-9294
VL - 65
SP - 989
EP - 1001
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 5
ER -