Medical Image Fusion Based on Deep Decomposition and Sparse Representation

Vanitha, K.; Satyanarayana, D.; Prasad, M. N. Giri

doi:10.1007/978-981-15-6315-7_22

Cited by 2 publications

(5 citation statements)

References 13 publications

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“…To demonstrate the efficacy of our method, we compared it to other newly developed fusion methods [27], including as: The pulse coupled convolutional neural network (PCNN), the adaptive -parameter PA-PCNN in NSST domain (NSST-PAPCNN) [14], the image gradient motivation PCNN in NSCT domain (NSCT-G-PCNN) [15], and in the NSST domain, the bounded measured PCNN technique BM-PCNN-NSST [17]. The parameters settings in our experience as follows:…”

Section: Results and Analysismentioning

confidence: 99%

“…For a better comparison between our proposed method against the four state-of-the-art fusion methods. The parameters of modified M-PCNN that we used in our experiments are the same as in their papers [14,15,17].…”

Section: Results and Analysismentioning

confidence: 99%

“…Wang et al [13] propose a new method for multi-focus image fusion based on PCNN and random walks. Vanitha et al [15] had focused on primary to the adaptive -parameter PA-PCNN in NSST domain (NSST-PAPCNN), the image gradient motivation PCNN in NSCT domain (NSCT-G-PCNN) [15], in the NSST domain [16], the bounded measured PCNN technique BM-PCNN-NSST. Wang et al [17], Multimodal Medical Image Fusion Based on Multichannel Coupled Neural P Systems and Max-Cloud Models in Spectral Total Variation Domain.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The average gradient (AG) can be utilized as an objective record to specific the picture sharpness, primarily since it can reflect the alter rate of the dim level of the image boundary. The larger the AG, we get good image quality [15]. It is characterized by Eq.…”

Section: 𝐹𝑖𝑗(𝑛) = 𝑆𝑖𝑗mentioning

confidence: 99%

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An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

Abdelfatih¹,

Ismail²

2022

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The neurodegenerative disease such as: Parkinson's disease (PD), mild Alzheimer’s affects many people and has a serious influence on their life, With the quick advancement of computer-aided diagnostic (CAD) methods, early detection is crucial since effective treatment halts the spread of the disease. Image fusion is useful for medical diagnostics. In this paper we propose a multi-modality medical image fusion algorithm in NSST domain. Shearlets (NSST) are decomposed similarly to contourlets (NSCT), except that instead of applying the Laplacian pyramid followed by directional filtering, shearlets use a shear matrix. In this article the Biorthogonal CDF9/7 filter is applied in the shift-invariant shearlet filter banks, then the coefficients of low frequency bands are selected using maximum rule, and using the gradient in each subband high frequency image to motivate the modified pulse coupled neural networks (Modified PCNN). Finally reverse IHS to get the fused color image, all this to optimize the calculation performance and improve the characteristics of the fused image for medical diagnosis. Our approach was validated with several brain diseases modalities: Alzheimer’s…etc. The findings reveal that the suggested image fusion technique has a higher quality than those fused by previous algorithms existing.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: 𝐹𝑖𝑗(𝑛) = 𝑆𝑖𝑗mentioning

confidence: 99%

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An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

Abdelfatih¹,

Ismail²

2022

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“…Extensive research has been carried out in recent years on the topic of multimodal image fusion. According to the image transformation strategy adopted in Li et al (2016), fusion categories are mainly concentrated on four aspects: MST-based schemes (Tannaz et al 2020, Goyal et al 2021, Singh et al 2022, sparse representation (SR)based schemes (Yin 2018, Shibu and Priyadharsini 2021, Zhang 2021, deep learning (DL)-based schemes (Vasanthi et al 2021, Liu et al 2022, and the combination of different transformations (Maqsood and Javed 2020, Vanitha et al 2020, Reddy et al 2021. It is worth noting that the applications of SR in voxel selection, compressed sensing, and bioelectrical signal detection prove that SR has become an effective tool for image processing.…”

Section: Introductionmentioning

confidence: 99%

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Hu,

Cai,

et al. 2024

Phys. Med. Biol.

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ObjectiveMultimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF.ApproachThe fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain's sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction.Main resultsExperimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations.SignificanceThe proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level with sub-band correlation in the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhances sub-band correlation to promote the preservation of structural and textural details.

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Medical Image Fusion Based on Deep Decomposition and Sparse Representation

Cited by 2 publications

References 13 publications

An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

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