Fusion of Multimodality Medical Images- A Review

Sebastian, Jinu; King, G. R. Gnana

doi:10.1109/stcr51658.2021.9588882

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…In this section, we apply our method to the medical image fusion to further demonstrate the effectiveness of our method. Medical images of different modalities provide information from different aspects [62]. Specifically, Magnetic Resonance Imaging (MRI) is structural imaging modality which shows soft tissues with high contrast, as illustrated in Fig.…”

Section: Extension To Medical Image Fusionmentioning

confidence: 99%

Two-Stream Edge-Aware Network for Infrared and Visible Image Fusion With Multi-Level Wavelet Decomposition

Wang,

Shu,

et al. 2024

IEEE Access

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Infrared and visible image fusion (IVIF) aims to generate a fused image with both salient target and rich textures from two different complementary modality images. To better integrate valuable edge information into the fused image, we first propose a novel two-stream network based on Auto-Encoder (AE) framework, which extracts deep hierarchical detail information at coarse scale from base stream by multi-level wavelet decomposition progressively and incorporates them into detail stream for information compensation. The aggregation of edge information ranging from coarse to fine facilitates a more comprehensive representation of contours and textures. Then, we propose a new feature fusion strategy, termed as Structural Feature Map Decomposition (SFMD). The first step is to decompose local patches of feature map with each modality into three independent components by Structural Patch Decomposition (SPD). In the second step, appropriate fusion rules are carefully designed for each component and the fused patch can be derived by inverse SPD. Our extensive experiments on several benchmark datasets show that our method outperforms seven compared state-of-the-art methods, especially in human visual perception.

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Section: Extension To Medical Image Fusionmentioning

confidence: 99%

Two-Stream Edge-Aware Network for Infrared and Visible Image Fusion With Multi-Level Wavelet Decomposition

Wang,

Shu,

et al. 2024

IEEE Access

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“…As a result, researchers increasingly integrate spatial domain and transform domain methodologies to achieve superior outcomes. Some of the Hybrid image fusion techniques in the literature are explained in [22]. Many other methods are also available to obtain fused images of better quality.…”

Section: Comparative Analysis and Fusion Of Mri And Pet Images Based ...mentioning

confidence: 99%

Comparative Analysis and Fusion of MRI and PET Images based on Wavelets for Clinical Diagnosis

Sebastian,

King

2022

International Journal of Electronics and Telecommunications

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Nowadays, Medical imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT), and Computed Tomography (CT) play a crucial role in clinical diagnosis and treatment planning. The images obtained from each of these modalities contain complementary information of the organ imaged. Image fusion algorithms are employed to bring all of this disparate information together into a single image, allowing doctors to diagnose disorders quickly. This paper proposes a novel technique for the fusion of MRI and PET images based on YUV color space and wavelet transform. Quality assessment based on entropy showed that the method can achieve promising results for medical image fusion. The paper has done a comparative analysis of the fusion of MRI and PET images using different wavelet families at various decomposition levels for the detection of brain tumors as well as Alzheimer's disease. The quality assessment and visual analysis showed that the Dmey wavelet at decomposition level 3 is optimum for the fusion of MRI and PET images. This paper also compared the results of several fusion rules such as average, maximum, and minimum, finding that the maximum fusion rule outperformed the other two.

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A Novel MRI and PET Image Fusion in the NSST Domain Using YUV Color Space Based on Convolutional Neural Networks

Sebastian

King

2023

Wireless Pers Commun

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Fusion of Multimodality Medical Images- A Review

Cited by 5 publications

References 31 publications

Two-Stream Edge-Aware Network for Infrared and Visible Image Fusion With Multi-Level Wavelet Decomposition

Two-Stream Edge-Aware Network for Infrared and Visible Image Fusion With Multi-Level Wavelet Decomposition

Comparative Analysis and Fusion of MRI and PET Images based on Wavelets for Clinical Diagnosis

A Novel MRI and PET Image Fusion in the NSST Domain Using YUV Color Space Based on Convolutional Neural Networks

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