Multimodal Medical Image Fusion Based on Discrete Wavelet Transform and Genetic Algorithm

Bhardwaj, Jayant; Nayak, Abhijit; Gambhir, Deepak

doi:10.1007/978-981-15-5113-0_89

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…Hence, frequency domain fusion is preferred, even though it presents shift invariance challenges [3]. Various optimization strategies, including genetic algorithms (GA) [4,5], grasshopper optimization (GO) [6], grey wolf optimization, particle swarm optimization [7], and hybrid combinations [8] are explored to address these issues. The effectiveness of these methods depends on their exploration and exploitation rates, with hybridization increasing computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Techniques Based on Optimization Algorithms: A Review

Goel,

Wasim,

Srivastava

et al. 2024

RAiSE-2023

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Section: Introductionmentioning

confidence: 99%

Image Fusion Techniques Based on Optimization Algorithms: A Review

Goel,

Wasim,

Srivastava

et al. 2024

RAiSE-2023

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“…Then, the processed coefficients can be converted to the fused image by inverse MST. According to the different decomposition methods of source images, MST can be divided into pyramid-based methods [2][3][4], wavelet-based methods [5][6][7][8], and multiscale geometric analysis-(MGA-) based methods [9][10][11][12][13][14][15][16][17][18]. Due to the limitation of preset functions in the MST-based algorithm, some essential features of the source images, such as edge and texture information, may not be well expressed and extracted, which significantly reduces the fusion performance.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation

Lou

Feng

2021

Computational and Mathematical Methods in Medicine

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A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes.

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SDR2Tr‐GAN: A Novel Medical Image Fusion Pipeline Based on GAN With SDR2 Module and Transformer Optimization Strategy

Cheng,

Fang,

Tang

et al. 2024

Int J Imaging Syst Tech

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In clinical practice, radiologists diagnose brain tumors with the help of different magnetic resonance imaging (MRI) sequences and judge the type and grade of brain tumors. It is hard to realize the brain tumor computer‐aided diagnosis system only with a single MRI sequence. However, the existing multiple MRI sequence fusion methods have limitations in the enhancement of tumor details. To improve fusion details of multi‐modality MRI images, a novel conditional generative adversarial fusion network based on three discriminators and a Staggered Dense Residual2 (SDR2) module, named SDR2Tr‐GAN, was proposed in this paper. In the SDR2Tr‐GAN network pipeline, the generator consists of an encoder, decoder, and fusion strategy that can enhance the feature representation. SDR2 module is developed with Res2Net into the encoder to extract multi‐scale features. In addition, a Multi‐Head Spatial/Channel Attention Transformer, as a fusion strategy to strengthen the long‐range dependencies of global context information, is integrated into our pipeline. A Mask‐based constraint as a novel fusion optimization mechanism was designed, focusing on enhancing salient feature details. The Mask‐based constraint utilizes the segmentation mask obtained by the pre‐trained Unet and Ground Truth to optimize the training process. Meanwhile, MI and SSIM loss jointly improve the visual perception of images. Extensive experiments were conducted on the public BraTS2021 dataset. The visual and quantitative results demonstrate that the proposed method can simultaneously enhance both global image quality and local texture details in multi‐modality MRI images. Besides, our SDR2Tr‐GAN outperforms the other state‐of‐the‐art fusion methods regarding subjective and objective evaluation.

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Multimodal Medical Image Fusion Based on Discrete Wavelet Transform and Genetic Algorithm

Cited by 9 publications

References 20 publications

Image Fusion Techniques Based on Optimization Algorithms: A Review

Image Fusion Techniques Based on Optimization Algorithms: A Review

Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation

SDR2Tr‐GAN: A Novel Medical Image Fusion Pipeline Based on GAN With SDR2 Module and Transformer Optimization Strategy

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