A Survey on Multimodal Medical Image Fusion

Devanna, H.; Kumar, G. A. E. Satish

doi:10.9790/0661-1902010714

Cited by 2 publications

(3 citation statements)

References 99 publications

(141 reference statements)

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“…Ultrasonography, single photon emission computed tomography, positron emission tomography, magnetic resonance imaging, computed tomography, Raman spectroscopy, MALDI imaging, mammography, and fluorescence lifetime imaging microscopy are the most popular imaging modalities employed in different organ investigations. These varieties of modalities are used in lung disease investigation, bone marrow inspection, prostate cancer detection, breast area projection for breast cancer, brain imaging conduction [ 2 ], and oral cancer identification [ 4 ].…”

Section: Overview Of Data Typesmentioning

confidence: 99%

“…Various algorithms are employed in biomedical image fusion experiments resulting in an improved prediction of properties of biomedical specimen, thereby the enhanced effectiveness of clinical functions are demonstrated [ 2 ]. In pixel-level fusion, the pixels of the images should overlap with each other and carry information about the same sample regions.…”

Section: Introductionmentioning

confidence: 99%

“…Feature-level fusion reduces data preprocessing, including the alignment of the image data. The methods used for traditional data fusion are fuzzy logic, wavelet transforms, neural networks, support vector machines, and morphological approaches [ 2 ]. Besides these classical approaches, deep learning methods with multiple inputs can be used that fuse features in the network.…”

Section: Introductionmentioning

confidence: 99%

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A Review on Data Fusion of Multidimensional Medical and Biomedical Data

2022

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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.

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Section: Overview Of Data Typesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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