Contrast enhancement and speckle suppression in OCT images based on a selective weighted variational enhancement model and an SP-FOOPDE algorithm

Lei Chen; Chen Tang; Zong Heng Huang; Min Xu; Zhenkun Lei

doi:10.1364/JOSAA.422047

Journal of the Optical Society of America A
Vol. 38,
Issue 7,
pp. 973-984
(2021)
•https://doi.org/10.1364/JOSAA.422047

Contrast enhancement and speckle suppression in OCT images based on a selective weighted variational enhancement model and an SP-FOOPDE algorithm

Lei Chen, Chen Tang, Zong Heng Huang, Min Xu, and Zhenkun Lei

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Lei Chen, Chen Tang, Zong Heng Huang, Min Xu, and Zhenkun Lei, "Contrast enhancement and speckle suppression in OCT images based on a selective weighted variational enhancement model and an SP-FOOPDE algorithm," J. Opt. Soc. Am. A 38, 973-984 (2021)

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Abstract

Simultaneous contrast enhancement and speckle suppression in optical coherence tomography (OCT) are of great significance to medical diagnosis. In this paper, we propose a selective weighted variational enhancement (SWVE) model to enhance the structural parts of OCT images, and then present a shape-preserving fourth-order-oriented partial differential equations (SP-FOOPDE) algorithm to suppress speckle noise. To be specific, in the SWVE model, we first introduce the fast and robust fuzzy c-means clustering (FRFCM) algorithm to generate masks based on the gray-level histograms of the reconstructed OCT images and utilize the masks to distinguish the structural parts from the background. Then the retinex-based weighted variational model, combined with gamma correction, is adopted to enhance the structural parts by multiplying the estimated reflectance with the adjusted illumination. In the despeckling process, we present an SP-FOOPDE algorithm with the fidelity term modified by the shearlet transform to strike a splendid balance between noise suppression and structural preservation. Experimental results show that the proposed method performs well in contrast enhancement and speckle suppression, with better quality metrics of the MSE, PSNR, CNR, ENL, EKI, and ${\nu}$ and better noise immunity than the related method. Moreover, the application to the segmentation preprocessing exhibits that the retinal structure of the OCT images processed by the proposed method can be completely segmented.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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OCT	MSR	BM3D
NORMAL	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 95$
CNV	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 95$
DME	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 90$
AMD	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 100$

OCT	SWVE	SP-FOOPDE
NORMAL	$p = 0.04$ , $q = 0.4$ , $l a m b d a = 1$ , $g a m m a = 1.2$	$N = 300$ , $Δ t = 0.04$ , $λ = 0.175$
CNV	$p = 0.04$ , $q = 0.4$ , $l a m b d a = 1$ , $g a m m a = 1.15$	$N = 350$ , $Δ t = 0.05$ , $λ = 0.175$
DME	$p = 0.04$ , $q = 0.4$ , $l a m b d a = 1$ , $g a m m a = 1.1$	$N = 300$ , $Δ t = 0.04$ , $λ = 0.175$
AMD	$p = 0.04$ , $q = 0.4$ , $l a m b d a = 1$ , $g a m m a = 1.15$	$N = 300$ , $Δ t = 0.04$ , $λ = 0.175$

OCT	Method	MSE	PSNR	CNR	ENL	EKI	$ν$
NORMAL	Original	—	33.13	2.53	76.91	—	—
	Ref. [31]	$1.75 \times 10^{3}$	38.85	3.76	90.29	0.71	17.47
	Proposed	511.51	52.51	7.25	238.78	0.86	25.43
CNV	Original	—	33.15	2.79	49.90	--	--
	Ref. [31]	$2.21 \times 10^{3}$	36.41	4.87	58.14	0.72	20.02
	Proposed	566.84	52.59	9.04	197.91	0.81	28.30
DME	Original	—	31.88	3.25	57.95	—	—
	Ref. [31]	$1.90 \times 10^{3}$	36.66	5.22	74.23	0.76	21.20
	Proposed	422.83	49.85	7.06	154.27	0.91	26.59
AMD	Original	—	34.65	2.88	117.54	—	—
	Ref. [31]	$1.91 \times 10^{3}$	35.94	3.96	144.95	0.68	21.25
	Proposed	407.49	49.70	8.46	182.74	0.81	27.37

	Noise	MSE				PSNR				CNR
OCT	Level	0.01	0.04	0.08	0.1	0.01	0.04	0.08	0.1	0.01	0.04	0.08	0.1
NORMAL	Original	—	—	—	—	31.10	29.17	27.14	26.39	2.31	1.93	1.60	1.49
	Ref. [31]	$1.93 \times 10^{3}$	$2.05 \times 10^{3}$	$2.11 \times 10^{3}$	$2.09 \times 10^{3}$	38.15	36.94	33.48	31.61	3.14	3.01	3.13	3.05
	Proposed	$4 .82 \times 10^{2}$	$5 .64 \times 10^{2}$	$6 .53 \times 10^{2}$	$7 .06 \times 10^{2}$	50.99	50.15	47.32	45.85	6.98	6.89	6.12	6.04
CNV	Original	—	—	—	—	29.75	28.03	26.24	25.49	2.58	2.12	1.78	1.61
	Ref. [31]	$2.28 \times 10^{3}$	$2.38 \times 10^{3}$	$2.35 \times 10^{3}$	$2.41 \times 10^{3}$	36.29	35.11	32.37	30.59	4.28	4.24	3.98	3.75
	Proposed	$5 .80 \times 10^{2}$	$6 .17 \times 10^{2}$	$6 .48 \times 10^{2}$	$8 .34 \times 10^{2}$	50.27	49.30	46.32	43.79	8.54	8.46	7.99	7.84
DME	Original	—	—	—	—	30.22	28.42	26.54	25.87	2.55	2.07	1.77	1.67
	Ref. [31]	$2.04 \times 10^{3}$	$2.15 \times 10^{3}$	$2.20 \times 10^{3}$	$2.16 \times 10^{3}$	36.09	35.23	32.52	31.15	4.50	4.99	4.35	3.96
	Proposed	$4 .56 \times 10^{2}$	$5 .75 \times 10^{2}$	$6 .58 \times 10^{2}$	$7 .15 \times 10^{2}$	48.08	46.98	43.80	41.84	6.84	6.44	6.25	6.04
AMD	Original	—	—	—	—	32.03	29.26	26.95	26.14	2.86	2.27	1.84	1.75
	Ref. [31]	$1.94 \times 10^{3}$	$1.98 \times 10^{3}$	$2.04 \times 10^{3}$	$2.27 \times 10^{3}$	35.48	34.50	32.49	31.02	3.88	3.70	3.51	3.55
	Proposed	$5 .57 \times 10^{2}$	$5 .79 \times 10^{2}$	$6 .04 \times 10^{2}$	$6 .84 \times 10^{2}$	48.38	46.97	44.22	42.08	6.73	6.22	5.82	5.83

OCT	MSR	BM3D
NORMAL	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 95$
CNV	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 95$
DME	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 90$
AMD	$c = 2$ , $m = 2$ , $σ = 20, 50, 120$	$δ = 100$

Abstract

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Equations (23)

Journal of the Optical Society of America A