基于NSST域像素相关分析的医学图像融合

doi:10.13229/j.cnki.jdxbgxb.20200365

Abstract

Abstract:

To solve the problem of information loss in pixel-level multimodal medical image fusion, an image fusion method using pixel correlation analysis (PCA) in Non-subsampled Shearlet Transform (NSST) domain is proposed. First, NSST decomposition is performed on the source images to obtain high and low frequency sub-bands. The intensity correlation factor between neighborhood pixels and central pixel is calculated using the proposed center pixel variance, and the correlation coefficient matrix of neighborhood pixels is constructed. The proposed correlation-sum of modified laplacian (C-SML) is used as the fusion rule for high-frequency sub-bands. The energy of the central pixel and the energy gradient information of the neighboring pixels of the low-frequency sub-bands are calculated to obtain the fusion decision map for low-frequency sub-bands. Finally, the fused image is obtained by inverse NSST. The experimental results about magnetic resonance imaging (MRI) and computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography （SPECT） brain images indicate that the proposed fusion method can well retain the significant information and texture details of the source images.

Key words: computer application, image processing, image fusion, non-subsampled shearlet transform（NSST）, pixel correlation

CLC Number:

TP391

Ming-yao XIAO,Xiong-fei LI,Rui ZHU. Medical image fusion based on pixel correlation analysis in NSST domain[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(9): 2640-2648.

Figures/Tables 12

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References 16

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方法	EN	Q₀	Q_E	Q_W	SSIM	VIFF
CBF	4.5089	0.3739	0.6888	0.5437	0.4434	0.2796
CSMCA	4.5977	0.4172	0.7705	0.7435	0.5885	0.4263
CVT	4.4325	0.2485	0.4074	0.4301	0.2746	0.3645
DWT	4.3306	0.2717	0.4735	0.4610	0.2928	0.3606
GFF	4.6525	0.3750	0.7072	0.5603	0.5600	0.2483
MSTSR	4.5321	0.4138	0.7479	0.7003	0.5851	0.3650
NSCT	4.3689	0.2549	0.4460	0.4423	0.2859	0.3676
NSCT_LLE	4.8174	0.4257	0.7316	0.7687	0.6702	0.4455
NSST_PCNN	4.8998	0.3932	0.5647	0.5853	0.6384	0.3884
本文	4.8315	0.4279	0.7895	0.7951	0.6978	0.4457

方法	EN	Q₀	Q_E	Q_W	SSIM	VIFF
CBF	4.1089	0.3192	0.7428	0.5706	0.4962	0.3733
CSMCA	3.9229	0.3566	0.8441	0.7757	0.7012	0.5107
CVT	4.1180	0.2560	0.4491	0.4645	0.3740	0.4271
DWT	4.0214	0.2726	0.5275	0.4955	0.3894	0.4253
GFF	4.2733	0.3261	0.8399	0.7611	0.7214	0.4718
MSTSR	4.0168	0.3422	0.8465	0.7778	0.7507	0.5187
NSCT	4.0416	0.2593	0.4951	0.4763	0.3825	0.4309
NSCT_LLE	4.3168	0.3859	0.7553	0.7931	0.8548	0.5657
NSST_PCNN	4.7590	0.3510	0.5395	0.5631	0.7958	0.4584
本文	4.3983	0.3893	0.8572	0.8204	0.9035	0.5711

方法	EN	Q₀	Q_E	Q_W	SSIM	VIFF
CBF	4.4407	0.4400	0.7580	0.7218	0.7459	0.4807
CSMCA	4.1450	0.3864	0.7400	0.7085	0.7205	0.4939
CVT	4.2760	0.2668	0.4771	0.5136	0.4504	0.3620
DWT	4.1654	0.3003	0.5321	0.5352	0.4667	0.3735
GFF	4.5299	0.4519	0.7882	0.7203	0.7175	0.4832
MSTSR	4.4779	0.3791	0.7374	0.7317	0.7884	0.5112
NSCT	4.2083	0.2794	0.5116	0.5240	0.4598	0.3710
NSCT_LLE	4.5889	0.4576	0.7758	0.7622	0.8072	0.5402
NSST_PCNN	4.5503	0.4497	0.7660	0.7445	0.7938	0.5424
本文	4.6213	0.4580	0.7795	0.7624	0.8137	0.5442

方法	EN	Q₀	Q_E	Q_W	SSIM	VIFF
CBF	2.9244	0.2479	0.8378	0.7696	0.8727	0.5064
CSMCA	2.8241	0.2400	0.8296	0.7685	0.8394	0.5060
CVT	3.2507	0.2382	0.8182	0.7597	0.8173	0.5197
DWT	2.9895	0.2192	0.5864	0.5914	0.4378	0.4527
GFF	3.1406	0.2411	0.8684	0.7642	0.7207	0.4551
MSTSR	3.1921	0.2485	0.8315	0.7748	0.7267	0.5396
NSCT	3.0286	0.2096	0.5682	0.5775	0.4279	0.4577
NSCT_LLE	3.2026	0.2556	0.8355	0.7891	0.9110	0.5600
NSST_PCNN	3.2543	0.2512	0.8375	0.7983	0.9163	0.5595
本文	3.2674	0.2613	0.8346	0.7811	0.9176	0.5601

Medical image fusion based on pixel correlation analysis in NSST domain

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