66
Z. Amini et al.
Table 3.9 The results of MSNR and CNR using several ROIs such as shown in Fig. 3.10
Methods
Local (L)
Non-Local
(NL)
Homomorphic
(H) NonHomomorphic
(NH)
Gaussian
Noise (G)
Two-sided
Rayleigh
Noise (R)
MSNR ROI1
MSNR ROI2
CNR
L
H
G
7.00
15.76
8.76
NL
H
G
7.56
17.03
9.47
L
NH
G
12.27
27.76
13.49
NL
NH
G
10.77
22.73
11.95
L
H
R
5.89
13.11
7.22
NL
H
R
8.63
19.59
10.95
L
NH
R
10.75
22.55
11.81
NL
NH
R
10.88
23.05
12.17
Original image
2.56
5.30
2.74
no. of selected ROIs
CNR
Fig. 3.11 A comparison between CNR curves for 156 selected ROIs from OCT dataset
3.6 Conclusion
In this chapter we discussed about several methods of OCT despeckling and enhancement in the modeling point of view. Considering statistical and transform based modeling, three algorithms were introduced based on (1) statistical gaussinization of each
OCT intraretinal layer, (2) 3D data-adaptive sparse modeling of OCT, and (3) statistical modeling of OCT in 3D non-data-adaptive sparse domain. Incorporating the
structural information of OCT data (geometric modeling) on top of the advantages