CLC number: TN911.73
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 6
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DANESHVAR Sabalan, GHASSEMIAN Hassan. MRI and PET images fusion based on human retina model[J]. Journal of Zhejiang University Science A, 2007, 8(10): 1624-1632.
@article{title="MRI and PET images fusion based on human retina model",
author="DANESHVAR Sabalan, GHASSEMIAN Hassan",
journal="Journal of Zhejiang University Science A",
volume="8",
number="10",
pages="1624-1632",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1624"
}
%0 Journal Article
%T MRI and PET images fusion based on human retina model
%A DANESHVAR Sabalan
%A GHASSEMIAN Hassan
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 10
%P 1624-1632
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1624
TY - JOUR
T1 - MRI and PET images fusion based on human retina model
A1 - DANESHVAR Sabalan
A1 - GHASSEMIAN Hassan
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 10
SP - 1624
EP - 1632
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1624
Abstract: The diagnostic potential of brain positron emission tomography (PET) imaging is limited by low spatial resolution. For solving this problem we propose a technique for the fusion of PET and MRI images. This fusion is a trade-off between the spectral information extracted from PET images and the spatial information extracted from high spatial resolution MRI. The proposed method can control this trade-off. To achieve this goal, it is necessary to build a multiscale fusion model, based on the retinal cell photoreceptors model. This paper introduces general prospects of this model, and its application in multispectral medical image fusion. Results showed that the proposed method preserves more spectral features with less spatial distortion. Comparing with hue-intensity-saturation (HIS), discrete wavelet transform (DWT), wavelet-based sharpening and wavelet-à trous transform methods, the best spectral and spatial quality is only achieved simultaneously with the proposed feature-based data fusion method. This method does not require resampling images, which is an advantage over the other methods, and can perform in any aspect ratio between the pixels of MRI and PET images.
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