Paper Info
Title
Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging
Abstract
Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.
Year
DOI
Venue
2021
10.3390/s21238027
SENSORS
Keywords
DocType
Volume
optical coherence tomography (OCT), peripapillary OCT, automatic layer segmentation, retinal imaging analysis, mathematical morphology, active contours, glaucoma
Journal
21
Issue
ISSN
Citations 
23
1424-8220
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Rafael Berenguer-Vidal100.34
Rafael Verdú-Monedero200.34
Juan Morales-Sánchez300.34
Inmaculada Sellés-Navarro400.34
Rocío del Amor501.69
Gabriel García642.18
Valery Naranjo700.34