2013
DOI: 10.1049/iet-ipr.2012.0410
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Multi‐level fuzzy contourlet‐based image fusion for medical applications

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Cited by 36 publications
(18 citation statements)
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“…However, being the most typical multiscale decomposition tool, wavelets fail to capture linear singularity, continuous edge, and detail of medical images [7][8]. Darwish et al presented an image fusion system based on contourlet transform and multilevel fuzzy reasoning technique, in which the fuzzy fusion rules were applied to low-and high-frequency coefficient approximation; the results showed the effectivity of the proposed method for applications in brain image fusion [9]. Clearly, contourlet transform can obtain multi-directional high-frequency information and capture the edge detail of images, although it is not shift-invariant.…”
Section: State Of the Artmentioning
confidence: 99%
“…However, being the most typical multiscale decomposition tool, wavelets fail to capture linear singularity, continuous edge, and detail of medical images [7][8]. Darwish et al presented an image fusion system based on contourlet transform and multilevel fuzzy reasoning technique, in which the fuzzy fusion rules were applied to low-and high-frequency coefficient approximation; the results showed the effectivity of the proposed method for applications in brain image fusion [9]. Clearly, contourlet transform can obtain multi-directional high-frequency information and capture the edge detail of images, although it is not shift-invariant.…”
Section: State Of the Artmentioning
confidence: 99%
“…It is expected to reduce uncertainty, provide more useful information and lead to supplementary clinical information that is not apparent in the separate images. Medical image fusion (MIF) aims at reducing storage costs, should not remove information inherent in the input images and avoid artifacts [7].…”
Section: Multimodal Image Fusionmentioning
confidence: 99%
“…Image fusion is a process of combining two or more images produced by single or multiple medical imaging modalities to provide a very detail fused image of higher quality and more reliable information [7][8][9][10][11]. This approach aims at providing supplementary clinical information that is not apparent in the individual images alone to deliver a better medical diagnosis, to suggest a prognosis or to prevent advances in serious diseases [7,9,11,12]. Successfully fused images should not contain any artefacts, not remove any relevant information from the original image and minimize redundancy.…”
Section: Image Fusionmentioning
confidence: 99%
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