Medical Image Fusion Based on Low-Level Features

Zhang, Yongxin; Guo, Chenrui; Zhao, Peng

doi:10.1155/2021/8798003

Cited by 6 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparison experiments were conducted using three methods: high-pass filtering, weighted averaging, and traditional wavelet fusion. Moreover, according to the explanation of the feature-based data fusion approach which was proposed by Zhang et al [ 53 ], they decomposed images from the source into two layers, detail layers and base layers, employing a local binary pattern method to obtain features in low-levels. Using saliency detection, the detail and base layers of the low-level features were used to construct weight maps.…”

Section: Different Data Fusion Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

2022

View full text Add to dashboard Cite

Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.

show abstract

Section: Different Data Fusion Techniquesmentioning

confidence: 99%

“…There are numerous traditional fusion methods based on mutual information levels and their associated enlarged regions. Reprinted with permission from reference [ 53 ], 2017, Zhang et al…”

Section: Figurementioning

confidence: 99%

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

2022

View full text Add to dashboard Cite

show abstract

“…Feature-level fusion is usually based on the assumption that different modal features share the same feature space. It is a big challenge to construct a shared latent space and build a fusion model based on feature correlations and modality complementarity ( Zhang et al, 2021 ). Compared with input-level fusion and decision-level fusion, feature-level fusion can more effectively explore the relationship between different modal features.…”

Section: Related Workmentioning

confidence: 99%

A layer-wise fusion network incorporating self-supervised learning for multimodal MR image synthesis

Zhou

Zou

2022

Front. Genet.

View full text Add to dashboard Cite

Magnetic resonance (MR) imaging plays an important role in medical diagnosis and treatment; different modalities of MR images can provide rich and complementary information to improve the accuracy of diagnosis. However, due to the limitations of scanning time and medical conditions, certain modalities of MR may be unavailable or of low quality in clinical practice. In this study, we propose a new multimodal MR image synthesis network to generate missing MR images. The proposed model comprises three stages: feature extraction, feature fusion, and image generation. During feature extraction, 2D and 3D self-supervised pretext tasks are introduced to pre-train the backbone for better representations of each modality. Then, a channel attention mechanism is used when fusing features so that the network can adaptively weigh different fusion operations to learn common representations of all modalities. Finally, a generative adversarial network is considered as the basic framework to generate images, in which a feature-level edge information loss is combined with the pixel-wise loss to ensure consistency between the synthesized and real images in terms of anatomical characteristics. 2D and 3D self-supervised pre-training can have better performance on feature extraction to retain more details in the synthetic images. Moreover, the proposed multimodal attention feature fusion block (MAFFB) in the well-designed layer-wise fusion strategy can model both common and unique information in all modalities, consistent with the clinical analysis. We also perform an interpretability analysis to confirm the rationality and effectiveness of our method. The experimental results demonstrate that our method can be applied in both single-modal and multimodal synthesis with high robustness and outperforms other state-of-the-art approaches objectively and subjectively.

show abstract

“…In 42 , the authors proposed a medical image fusion technique to improve diagnostic accuracy and assessment. The method uses low-level features to enable the preservation of clinical features upon image fusion.…”

Section: Ecs Transactions 107 (1) 3649-3673 (2022)mentioning

confidence: 99%

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Das

Chandra

2022

ECS Trans.

View full text Add to dashboard Cite

Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information.

show abstract

Medical Image Fusion Based on Low-Level Features

Cited by 6 publications

References 36 publications

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

A Review on Data Fusion of Multidimensional Medical and Biomedical Data

A layer-wise fusion network incorporating self-supervised learning for multimodal MR image synthesis

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Contact Info

Product

Resources

About