Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain
By Gaurav Bhatnagar, Q. Wu, Zheng Liu. IEEE transactions on multimedia, 2013
469 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/113c384036772c777cbc831546596d6d7f465eee
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, unknown): “Widely used metrics includes spatial frequency (SF) [243] , the ratio of spatial frequency error (rSFe) [244] , wavelet entropy (WE) [245] , Signal noise ratio (SNR) [246] , mutual information (MI) [247] and directive contrast (DC) [248] .”
- Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, methodology): “onal matching pursuit (SR-SOMP) method [4], the guided filtering (GF) method [11], the phase congruency and directive contrast in NSCT domain (NSCT-PCDC) method [22], the NSCT-RPCNN method [23], the LP-SR method [8], the CTD-SR method [28], the LLF-IOI method [29], and the LP-CNN method [30].”
- 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, background): “The visual system is highly sensitive to the intensity contrast DC [42] which is the ration of high frequency intensity IH and low frequency intensity IL.”
- Pixel-level image fusion: A survey of the state of the art (2017, background): “puted tomography (SPECT) scan [131] and ultrasound imaging [132] can find applications through image fusion.”
- Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, methodology): “Works in [31] (using features of contourlets), [32] (using feature of wavelet), [33] (using features of wavelets), and [34] (using features learned by linear discriminant analysis) perform the classifier-level fusion.”
- A medical image fusion method based on convolutional neural networks (2017, methodology): “To improve the fusion performance, many complex decomposition approaches and elaborate weight assignment strategies have been recently proposed in the literature [6], [8]–[13], [15], [16].”