Retinal nerve fiber layer (RNFL) thickness, a measure of glaucoma progression, can be measured in images acquired
by spectral domain optical coherence tomography (OCT). The accuracy of RNFL thickness estimation, however, is
affected by the quality of the OCT images. In this paper, a new parameter, signal deviation (SD), which is based on the
standard deviation of the intensities in OCT images, is introduced for objective assessment of OCT image quality. Two
other objective assessment parameters, signal to noise ratio (SNR) and signal strength (SS), are also calculated for each
OCT image. The results of the objective assessment are compared with subjective assessment. In the subjective
assessment, one OCT expert graded the image quality according to a three-level scale (good, fair, and poor). The OCT
B-scan images of the retina from six subjects are evaluated by both objective and subjective assessment. From the
comparison, we demonstrate that the objective assessment successfully differentiates between the acceptable quality
images (good and fair images) and poor quality OCT images as graded by OCT experts. We evaluate the performance
of the objective assessment under different quality assessment parameters and demonstrate that SD is the best at
distinguishing between fair and good quality images. The accuracy of RNFL thickness estimation is improved
significantly after poor quality OCT images are rejected by automated objective assessment using the SD, SNR, and
SS.
We introduce a method based on optical reflectivity changes to segment the retinal nerve fiber layer (RNFL) in images recorded using swept source spectral domain optical coherence tomography (OCT). The segmented image is used to determine the RNFL thickness. Simple filtering followed by edge detecting techniques can successfully be applied to segment the RNFL from recorded images and estimate RNFL thickness. The method is computationally more efficient than previously reported approaches. Higher computational efficiency allows faster segmentation and provides the ophthalmologist segmented retinal images that better utilize advantages of spectral domain OCT instrumentation. OCT B-scan and fundus images of the retina are recorded for 5 patients. The segmentation method is applied on B-scan images recorded from all patients. An expert ophthalmologist separately demarcates the RNFL layer in the OCT images from the same patients in each B-scan image. Results from automated image processing software are compared to the boundary demarcated by the expert ophthalmologist. The absolute error between the boundaries demarcated by the expert and the algorithm is expressed in terms of area and is used as an error metric. Ability of the algorithm to accurately segment the RNFL in comparison with an expert ophthalmologist is reported.
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