Open AccessActive contour method for ILM segmentation in ONH volume scans in retinal OCT
Author(s) -
K. Gawlik,
Frank Haußer,
Friedemann Paul,
Alexander U. Brandt,
Ella Maria Kadas
Publication year - 2018
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.9.006497
Subject(s) - optical coherence tomography , segmentation , computer science , active contour model , inner limiting membrane , artificial intelligence , retinal , image segmentation , computer vision , optic nerve , pattern recognition (psychology) , optics , physics , anatomy , ophthalmology , medicine
The optic nerve head (ONH) is affected by many neurodegenerative and autoimmune inflammatory conditions. Optical coherence tomography can acquire high-resolution 3D ONH scans. However, the ONH's complex anatomy and pathology make image segmentation challenging. This paper proposes a robust approach to segment the inner limiting membrane (ILM) in ONH volume scans based on an active contour method of Chan-Vese type, which can work in challenging topological structures. A local intensity fitting energy is added in order to handle very inhomogeneous image intensities. A suitable boundary potential is introduced to avoid structures belonging to outer retinal layers being detected as part of the segmentation. The average intensities in the inner and outer region are then rescaled locally to account for different brightness values occurring among the ONH center. The appropriate values for the parameters used in the complex computational model are found using an optimization based on the differential evolution algorithm. The evaluation of results showed that the proposed framework significantly improved segmentation results compared to the commercial solution.