Biological image fusion using a NSCT based variable-weight method

Li, Tianjie; Wang, Yuan‐Yuan

doi:10.1016/j.inffus.2010.03.007

Cited by 143 publications

(58 citation statements)

References 31 publications

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“…But the edges and curves representations are not good in wavelet schemes. Novel multi-scale geometric tools like curvelet transform [7], contourlet transform are used for the representation of spatial structures [8]. Points discontinuities are detected by using Laplacian pyramid in the contourlet transform [9].…”

Section: (A) Multi-scale Decomposition Based Methodsmentioning

confidence: 99%

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Elaiyaraja¹,

Kumar²

2018

IJET

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Image Processing is an art to get an enriched image or it can be used to retrieve information. This image processing methods are used in medical field also. Numerous modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) etc. are used to analyze and diagnose diseases.Pixel-level image fusion is a combination of several images collected from various inputs and gives more information than any other input messages. Pixel-level image fusion shows a vital role in medical imaging. In this paper, pixel-level image fusionsmethods are survived and review the fusion quality measures are being used. Finally this surveycomplete with different kinds of image fusion methods proposed and still there are so many imminent ways in image fusion applications. Hence image fusions fields are pointedly develop in the forthcoming years.

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Section: (A) Multi-scale Decomposition Based Methodsmentioning

confidence: 99%

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Elaiyaraja¹,

Kumar²

2018

IJET

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“…Therefore, NSCT is more suitable for medical image fusion. Although medical image fusion methods based NSCT have achieved good results [10,11,12,13,14], most existing fusion methods neglect the dependencies between subband coefficients at the interscale and intrascale. However, the dependencies between decomposition coefficients commonly exist.…”

Section: G Non-subsampled Contourlet Transform Based Methodsmentioning

confidence: 99%

A Survey on Multimodal Medical Image Fusion

Devanna¹,

Kumar²

2017

IOSR JCE

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“…The down-sampling step is removed in NSCT to avoid drawbacks in contourlets. However, NSCT requires high computational complexity and a considerable amount of time when managing actual medical images, which limits further development [13] [14].…”

Section: State Of the Artmentioning

confidence: 99%

Medical Image Fusion Combined with Accelerated Non-negative Matrix Factorization and Expanded Laplacian Energy in Shearlet Domain

Lai¹,

Wang²,

He³

et al. 2017

JESTR

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Shearlet transform has been widely applied in related fields due to its admirable properties in image approximation. Image fusion method based on accelerated non-negative matrix factorization (ANMF) and expanded energy of Laplace (EEOL) rules was proposed in this study to integrate the complementary information of medical images with multiple modalities and improve the accuracy of clinical diagnosis and therapy. First, the registered medical images were decomposed into low-and high-frequency sub-band coefficients in shearlet domain. Then, the ANMF rule was used in merging low-frequency coefficients. Next, the visual-contrast-based EEOL rule was adopted in extracting details of source images from high-frequency coefficients. Finally, the ultimate fused image was reconstructed by applying inverse shearlet transform. Experimental results reveal that aside from visual effect, the proposed method achieves the best in three of five criteria and the run time is reduced by 29.21% compared with a method based on non-subsampled contourlet transform (NSCT) in computed tomography (CT)-magnetic resonance imaging (MRI) fusion. Moreover, the proposed method takes the first place in four of five criteria with run time reduced by 48.32% and 24.55% compared with two shearlet-based methods in a MRI-positron emission tomography (PET) case. This study indicates that the proposed method is superior to the selected approaches in visual and statistical evaluation, which is conducive to clinical practice of medical image fusion.

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Biological image fusion using a NSCT based variable-weight method

Cited by 143 publications

References 31 publications

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

Fusion Imaging in Pixel Level Image Processing Technique – A Literature Review

A Survey on Multimodal Medical Image Fusion

Medical Image Fusion Combined with Accelerated Non-negative Matrix Factorization and Expanded Laplacian Energy in Shearlet Domain

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