Automatic blood vessels segmentation based on different retinal maps from OCTA scans. (1st October 2017)

• Record Type: Journal Article
• Authors: Eladawi, Nabila; Elmogy, Mohammed; Helmy, Omar; Aboelfetouh, Ahmed; Riad, Alaa; Sandhu, Harpal; Schaal, Shlomit; El-Baz, Ayman
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 150
• Page End: 161
• Publication Date: 2017-10-01
• Subjects: Optical coherence tomography angiography (OCTA) -- Retinal blood vessels segmentation -- Diabetic retinopathy (DR) -- Generalized Gauss-Markov random field (GGMRF) -- Higher-order spatial Markov-Gibbs random field (MGRF); Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.08.008
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images. The system segments blood vessels from the superficial and deep retinal maps for normal and diabetic cases. Initially, we reduced the noise and improved the contrast of the OCTA images by using the Generalized Gauss-Markov random field (GGMRF) model. Secondly, we proposed a joint Markov-Gibbs random field (MGRF) model to segment the retinal blood vessels from other background tissues. It integrates both appearance and spatial models in addition to the prior probability model of OCTA images. The higher order MGRF (HO-MGRF) model in addition to the 1 s t -order intensity model are used to consider the spatial information in order to overcome the low contrast between vessels and other tissues. Finally, we refined the segmentation by extracting connected regions using a 2D connectivity filter. The proposed segmentation system was trained and tested on 47 data sets, which are 23 normal data sets and 24 data sets for diabetic patients. To evaluate the accuracy and robustness of the proposed segmentation framework, we used three different metrics, which are Dice similarity coefficient (DSC), absolute vessels volume difference (VVD), and area under the curve (AUC). The results on OCTA data sets ( D S C = 95.04 ± 3.75 %, V V D = 8.51 ± 1.49 %, and A U C = 95.20 ± 1.52 % ) show the promise of the proposed segmentation approach. Highlights: Present an automatic segmentation system for retinal blood vessels from OCTA images. Segment vessels from superficial & deep retinal maps for normal and diabetic cases. Propose a joint Markov-Gibbs random field model to segment the blood vessels. Evaluate the accuracy and robustness by using 3 different metrics (DSC, VVD, AUC). The results on OCTA datasets show the promise of the proposed segmentation approach.