Review of Different Image Fusion Techniques: Comparative Study

El-Masry, Shrouk A.; Awad, Wael A.; S.A.El-Hafeez,

doi:10.1007/978-981-15-3075-3_3

Cited by 6 publications

(2 citation statements)

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“…Image fusion may be considered as a merging tool for relevant knowledge from a series of medical images into one enlightening and comprehensive image. More accurately, fusion is the incorporation of evidence from a set of recorded medical images not including the establishment of misrepresentation [2]- [7].…”

Section: Priliminaries Of Medical Imaging Fusionmentioning

confidence: 99%

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

et al. 2021

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The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.INDEX TERMS Medical imaging, fusion process, imaging modalities, dual-tree complex wavelet transform, curvelet transform, discrete wavelet transform, principal component analysis.

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Section: Priliminaries Of Medical Imaging Fusionmentioning

confidence: 99%

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

et al. 2021

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“…These items are known as picture elements, pixels and image elements. The term pixel is the most usable form of the digital picture elements (Elmasry et al, 2020;Shinde and Dani, 2012). Bole's prototype operates in building a one-dimensional representation of the gray-level profiles of the iris.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Iris Recognition Using Edge Detection Along Benchmark Datasets

KHAN,

BAIG

et al. 2023

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An individual's identity has grown more essential for individuals to satisfy modern corporate the community's higher safety standards. Iris represents one of the finest and most precise biometric systems now in utilization, with multiple edge detection methods used. As a consequence, understanding the different kinds of edge detection algorithmic methods which are now under operation is crucial. Three edge detection methods are used to be assessed on iris data during the present research. These methodologies are carried out using the MATLAB environment, which provides the evaluation with verified results. CASIA and MMU are the datasets that are used for this purpose. In contrast, the results reveal that the canny edge detection method works quite efficiently. Visual quality is crucial in vision-based recognition of objects. The present research assesses the visual aesthetic using various quality indicators such as PSNR and MSE. When compared to PSNR and MSE, however, MSE is recognized as the best image quality statistic.

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Fusion of two image of the same scene into one image depending on discrete cosine transform

AL-Rashid,

AL-Assadi

2024

Fifth International Conference on Applied Sciences: Icas2023

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Review of Different Image Fusion Techniques: Comparative Study

Cited by 6 publications

References 10 publications

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

Assessment of the Iris Recognition Using Edge Detection Along Benchmark Datasets

Fusion of two image of the same scene into one image depending on discrete cosine transform

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