PCA-DWT based medical image fusion using non sub-sampled contourlet transform

Madanala, Sindhuja; Rani, K. Jhansi

doi:10.1109/scopes.2016.7955608

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…Madanala and Jhansi Rani [6] combined the advantages of frequency and time localization of wavelet transform and displacement invariance of nonsubsampled contourlet transform and proposed a fusion framework based on DWC +NSCT domain cascade. In this framework, wavelet transform was used to decompose the source image in the first stage in order to obtain the detailed coefficient and approximate coefficient, and principal component analysis method was used to fuse the detailed coefficient and approximate coefficient to minimize the redundancy.…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

“…From the papers of the past two years, it can be seen that there is almost no method for the proposed fusion algorithm to using spatial domain alone. However, there are many new methods that combine spatial domain methods with transform domain, such as PCA-DWT [6]. With the advent of the deep learning boom, a medical image fusion method based on deep learning emerged in 2017.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

132

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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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Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

132

View full text Add to dashboard Cite

show abstract

“…Finally, the fused image is evaluated using degree of focus map which is computed with the use of weight. In [17], an image fusion technique is presented for medical images using two cascaded algorithm such as Non sub-sampled Contourlet transform (NSCT) domains and discrete wavelet transform (DWT). Here, MRI (magnetic resonance imaging) and CT (Computed Tomography) images are used for training of the model.…”

Section: Literature Surveymentioning

confidence: 99%

Fine grained medical image fusion using type-2 fuzzy logic

H.R

Sujatha

2019

IJEECS

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<p>In recent years, many fast-growing technologies coupled with wide volume of medical data for the digitalization of that data. Thus, researchers have shown their immense interest on Multi-sensor image fusion technologies which convey image information based on data from various sensor modalities into a single image. The image fusion technique is a widespread technique for the diagnosis of medical instrumentation and measurement. Therefore, in this paper we have introduced a novel multimodal sensor medical image fusion method based on type-2 fuzzy logic is proposed using Sugeno model. Moreover, a Gaussian smoothen filter is introduced to extract the detailed information of an image using sharp feature points.Type-2 fuzzy algorithm is used to achieve highly efficient feature points from both the b images to provide high visually classified resultant image. The experimental results demonstrate that the proposed method can achieve better performance than the state-of-the- art methods in terms of visual quality and objective evaluation.</p>

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“…At present, many high-quality MMIF methods have been proposed ( Arif and Wang, 2020 ; Wang K. P. et al, 2020 ; Duan et al, 2021 ; Ma et al, 2022 ; Xu et al, 2022 ). Madanala and Rani (2016) proposed a two-stage fusion framework based on the cascade of discrete wavelet transform (DWT) and non-subsampled contour transform (NSCT) domains, realizing the combination of spatial domain and transform domain. Inspired by the Tchebichef moments’ ability to effectively capture edge features, Tang et al (2017) used the Tchebichef moments energy to characterize the image shape, and thus designed an MMIF method based on the pulse coupled neural network (PCNN).…”

Section: Introductionmentioning

confidence: 99%

Medical image fusion quality assessment based on conditional generative adversarial network

et al. 2022

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Multimodal medical image fusion (MMIF) has been proven to effectively improve the efficiency of disease diagnosis and treatment. However, few works have explored dedicated evaluation methods for MMIF. This paper proposes a novel quality assessment method for MMIF based on the conditional generative adversarial networks. First, with the mean opinion scores (MOS) as the guiding condition, the feature information of the two source images is extracted separately through the dual channel encoder-decoder. The features of different levels in the encoder-decoder are hierarchically input into the self-attention feature block, which is a fusion strategy for self-identifying favorable features. Then, the discriminator is used to improve the fusion objective of the generator. Finally, we calculate the structural similarity index between the fake image and the true image, and the MOS corresponding to the maximum result will be used as the final assessment result of the fused image quality. Based on the established MMIF database, the proposed method achieves the state-of-the-art performance among the comparison methods, with excellent agreement with subjective evaluations, indicating that the method is effective in the quality assessment of medical fusion images.

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PCA-DWT based medical image fusion using non sub-sampled contourlet transform

Cited by 16 publications

References 12 publications

A Review of Multimodal Medical Image Fusion Techniques

A Review of Multimodal Medical Image Fusion Techniques

Fine grained medical image fusion using type-2 fuzzy logic

Medical image fusion quality assessment based on conditional generative adversarial network

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