An efficient image fusion method based on dual tree complex wavelet transform

Chabi, Negar; Yazdi, Mehran; Entezarmahdi, Mohammad

doi:10.1109/iranianmvip.2013.6780019

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…With the progression of multi-resolution fusion, wavelet (WT) transformation was widely used [205,217,220,250,253,254,[256][257][258] to merge medical images, particularly DWT, since it maintains spectral information. However, the spatial characteristics are poorly expressed.…”

Section: Critical Discussion About Multimodal Fusion Techniquesmentioning

confidence: 99%

“…However curvelet do not have time invariance. • Wavelet Transform: In wavelet-based fusion, used in different studies [217,220,[253][254][255][256][257][258], once the image is decomposed by wavelet transformation, a multi-scale composite representation is constructed by selecting the salient wavelet coefficients. The most applied techniques based on wavelet transform are discrete wavelet transform (DWT), stationary wavelet transform (SWT) and Kekre's Wavelet transform (KWT).…”

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confidence: 99%

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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

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Section: Critical Discussion About Multimodal Fusion Techniquesmentioning

confidence: 99%

mentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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“…The dual-tree complex wavelet transform (DTCWT) proposed by Kingsbury [41] has directional selectivity and displacement invariance and can preserve the edge details of the source image. It is also an effective image fusion method [42,43], but in image decomposition, the factors that are affected by the direction are relatively large. In recent years, researchers have often combined DTCWT with other algorithms to form new methods.…”

Section: Frei-chen Operator Medical Image Fusion Methodsmentioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

120

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The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

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“…After being combined by certain guidelines, the fused coefficients are inversely transformed back to spatial domain to obtain the expected fusion image. Many transform domain based algorithms are brought forward, such as the Laplacian pyramid (LP) [ 11 ], the Discrete Wavelet Transform (DWT) [ 12 ] [ 13 ], the gradient pyramid [ 14 ] [ 15 ], the contrast pyramid (CP) [ 16 ], the ratio-of-low pass pyramid [ 17 ], the Shift-invariant DWT (SIDWT) [ 18 ], the Complex Wavelet Transform (CWT) [ 19 ] [ 20 ] and the Contourlet Transform [ 21 ] [ 22 ] [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

A multi-focus image fusion method via region mosaicking on Laplacian pyramids

Liang

Zhang

et al. 2018

PLoS ONE

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In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets.

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An efficient image fusion method based on dual tree complex wavelet transform

Cited by 18 publications

References 14 publications

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

A Review of Multimodal Medical Image Fusion Techniques

A multi-focus image fusion method via region mosaicking on Laplacian pyramids

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