J. Eur. Opt. Society-Rapid Publ. 21, 10( 2025) 95
Figure 1.( a, b) Illustrate the aerial and OCT scan of the ex vivo intrastromal flap cut and( c, d) the lenticule cuts with SCHWIND ATOS on porcine eyes. Note the edge cut of the flap delineates as a circle in( a) and the reflection of plasma bubble layers( anterior and posterior) can be seen in( c).
Table 2. OCT GAN111 acquisition parameters set for capturing both intrastromal flap and lenticule cuts on porcine eyes.
|
Lateral |
Axial |
Number of pixel |
10,000 |
1024 |
Pixel size( lm) |
1.2 |
2.46 |
Field of view( mm) |
12 |
2.52 |
Scan averaging |
A-Scan of( 6) |
A-Scan of( 5) |
We used a corneal refractive index of 1.37 and the correction model proposed by [ 49 ] to justify thickness measurements.
The effects of optical refraction on the OCT images might affect lateral measurements primarily by overestimating lateral extents [ 50 ]. However, due to the shallow cuts and the naturally flat shape of porcine corneas, optical refraction-induced distortion was minimized. Based on Snell’ s law, the estimated lateral distortion due to refraction at a depth of 150 lm is predicted to be less than 1 %, as discussed in [ 51 ]. This small percentage reflects a very minimal distortion in the OCT image, making this effect negligible in our practical application.
2.2 Image processing
Given a discrete gray scale and noisy image, we utilized the NL Means algorithm for denoising [ 52 – 54 ]. The intensity of a pixel at position q in the noisy image thus is given,
I ðpÞ ¼ X p W ðp; qÞ I ðqÞ; ð1Þ
where W( p, q) is a non-local weighting function as follows,
1 Eðp; qÞ
W ðp; qÞ ¼ P h i exp L HUB � exp LHUB � Ep ð; qÞ h 2 h 2
Eðp; qÞ ¼jjP p; P q jj:
Here E( p, q) is the Euclidean distance of pixel patches centered at p and q h 2 is a smoothing parameter, which controls the sensitivity of the weighting function to differences in E( p, q).
To measure how different two patches are, W( p, q) calculates their distance. Further, W( p, q) incorporates the Huber loss as the robust penalty operator like the conventional L 1 and L 2 norms. TheHuberlossisgiven,
8 < 1
L HUB ðf Þ¼ 2 f 2 2jfjd ð3Þ
: d ðjfj� 1 f Þ otherwise
2
;
ð2Þ
where f is the residual and d is a threshold parameter that determines the switch between the L 2 and L 1 norms [ 55, 56 ]. The L 2 and L 1 terms are two commonly used metrics to quantify differences between two entities, such as image patches in this context [ 57 ]. The Huber loss function transitions smoothly between the quadratic behavior of L 2 norms and the linear behavior of L 1 norms, depending on the value of the input f:
For | f | d, the quadratic term behaves like the L 2 norm and penalizes small differences strongly.
Otherwise, the linear term behaves like L 1 norm and provide robustness to large outliers.