Medical Image Fusion: A survey of the state of the art

By Alex Pappachen James, Belur V. Dasarathy. Information Fusion, 2013

955 citations recorded by the source corpus.

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Subfields: Biomedical Engineering, Media Technology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/91270a01c74e2c60fb696e9ade6c48f7a1a9469d

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In Biomedical Engineering

In Media Technology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Pixel-level image fusion: A survey of the state of the art (2017, background): “U S In recent years, pixel-level image fusion has been used in a wide variety of applications, such as remote sensing [7, 116], medical diagnosis[10], surveillance[29], and photography[70] applications.”
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation (2015, background): “A more precise description of the problem formulation is provided in the following paragraph.”
Visible and Infrared Image Fusion Using Deep Learning (2023, background): “Image fusion [122] has been studied for more than 30 years and contains several variants, including visible-infrared image fusion (VIF) [19], multi-focus image fusion (MFIF) [123], multi-exposure image fusion (MEF) [124], medical image fusion (MEDIF) [125], and remote sensing image fusion [126].”
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion (2023, background): “While MIF can assist in diagnosis and treatment by fusing multiple medical imaging modalities for precise detection of abnormality locations [12].”
Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, background): “An effective way to solve this problem is known as medical image fusion technique [1], [2], which aims at generating a composite image to integrate the complementary information contained in multiple medical images with different modalities.”
A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, methodology): “Some of the popular methods of image fusions are [48] based on morphology, knowledge, wavelets and fuzzy logic methods.”
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion (2022, background): “Similarly, MIF aims to clearly exhibit the abnormalities by fusing multiple medical imaging modalities to reveal comprehensive information to assist diagnosis and treatment [21].”
Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, methodology): “Motivated by this perspective, we advance one step further from the abstraction of image fusion methods in [7] and propose an algorithmic architecture for image fusion strate-”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “Multimodal fusion has a very broad range of applications, including audio-visual speech recognition (AVSR) [170], multimodal emotion recognition [200], medical image analysis [93], and multimedia event detection [122].”
A medical image fusion method based on convolutional neural networks (2017, background): “Multi-modal medical image fusion aims at combining the complementary information contained in different source images by generating a composite image for visualization, which can help physicians make easier and better decisions for various purposes [1].”