MRI and PET image fusion by combining IHS and retina-inspired models
By S. Daneshvar, Hassan Ghassemian. Information Fusion, 2010
327 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/565ac112e0b18abd63b07d3da39231fe70de266c
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In Biomedical Engineering
In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Image matting for fusion of multi-focus images in dynamic scenes (2013, methodology): “Recently, some novel transform domain analysis methods, such as curvelet transform [5], contourlet transform [6–9], log-Gabor wavelet transform [10], support value transform [11], retina-inspired model [12] and sparse representation [13] are also applied to image fusion.”
- DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion (2020, methodology): “These medical imaging can be divided into structural and functional systems [40].”
- Medical Image Fusion: A survey of the state of the art (2013, methodology): “In this example, the image fusion is achieved in MRI-PET fusion [119] using a contourlet method, MRISPECT [119] uses a wavelet based approach, MRI-CT [82] uses a Integer Wavelet Transform and Neuro-Fuzzy method, PET-CT [120] uses a wavelet coefficients fusion method, and Vibroacoustography images wi”
- An overview of multi-modal medical image fusion (2016, methodology): “It is of interest to look at a snapshot of five different key methodologies: 1) color space [3, 4], 2) pyramid [5, 6], 3) wavelet [7-16], 4) sparse representation [17-21] and 5) salient feature [22-24].”
- Pixel-level image fusion: A survey of the state of the art (2017, methodology): “…and the adopted fusion strategy used for the fusion of the multi-scale representations. component substitution[89], integration of component substitution and weighted average [88] level of coefficients, and the correlations among adjacent pixels and coefficients of different scales may 70 be considered.”
- Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, background): “In medical imaging, the functional images such as PET and SPECT images are usually shown as pseudocolor images, and they can be treated as color images with RGB channels in the fusion process [6], [27], [30], [42].”
- Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, background): “The system includes components such as visibility, smallest univalve segment assimilating nucleus (SUSAN) [239] , and retina-inspired model (RIM) [240, 241] .”
- Performance comparison of different multi-resolution transforms for image fusion (2011, background): “During the past two decades, many image fusion methods are developed [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12].”
- Pixel-level image fusion with simultaneous orthogonal matching pursuit (2012, background): “Firstly, the overcompleteness of the dictionary makes the sparse representation is shift-invariant [4].”