A novel method of multimodal medical image fusion using fuzzy transform

Manchanda, Meenu; Sharma, Rajiv Kumar

doi:10.1016/j.jvcir.2016.06.021

Cited by 73 publications

(15 citation statements)

References 38 publications

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“…In HT transform no modification in domain is required that's why HT transform is different from another transform that is used in signal processing. With the help of HT if the contributed signal is available in time domain then the resultant signal is also in time domain only [9]. This characteristic of HT is also correct for the signal that is in frequency domain helps to decrease the complexity.…”

Section: Hilbert Transformmentioning

confidence: 99%

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A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing

Kaur¹,

Kaur²

2018

IJCA

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Image fusion procedure is helpful to attain a single output image that contains required data or information by merging two distorted input images. Image fusion is done by pulling out all the necessary information from two images or more than two images after which the extorted information is combined into a distinct fused image. This fused image has improved superiority as compare to the input images. Image fusion is prepared by implementing particular techniques. Those particular schemes for image fusion are introduced in this paper. This paper offers an overview to the work that has been done by in past.

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Section: Hilbert Transformmentioning

confidence: 99%

“…Meenu Manchanda et al [9] had proposed a new technique of multimodal medical image fusion using fuzzy-transform (FTR). The fusion on the basis of FTR was facilitated in conservation with the efficient transfer of complete information presented in input images into a fused image.…”

Section: Sabalan Daneshvar Et Al [8]mentioning

confidence: 99%

A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing

Kaur¹,

Kaur²

2018

IJCA

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“…The high-quality fused images are helpful for doctors to make the accurate diagnosis. The above five objective evaluation metrics (AG, EI, Q AB/F , MI, VIF) used in this paper are the internationally common methods to evaluate the performance of medical image fusion [80][81][82].…”

Section: Image Quality Comparisonmentioning

confidence: 99%

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Wang

Zhang

et al. 2017

Future Internet

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Abstract:Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

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“…In addition to these applications, various other models of image fusions are available in the literature . Unluckily, in all these image fusion techniques, there is lack of employing effective machine learning techniques which may simplify the fusion mechanism.…”

Section: Introductionmentioning

confidence: 99%

Neuro‐wavelet based intelligent medical image fusion

Hassan

Murtza

Khan

et al. 2019

Int J Imaging Syst Tech

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Imaging based sensitive clinical diagnosis is critically depending on image quality. In this article, the problem of enhancing fundus images is addressed by a novel fusion technique. The proposed approach utilizes the representation capability of wavelet transform and the learning ability of artificial neural networks. In this approach, input images are decomposed into wavelet transform followed by appropriate feature extraction for training of neural networks to obtain fused image. To ensure homogeneity, it employs consistency verification for minimizing the fusion artifacts. The visual and quantitative performance of the proposed approach is assessed using a number of experiments performed on the standard datasets of DRIVE and DRION‐DB. The experimental results demonstrate that the proposed fusion technique offers high average structural similarity of “0.99.” The proposed approach outperforms state‐of‐the‐art techniques which validates its effectiveness. The developed approach might be applied by the clinical diagnosis system for fundus related diseases.

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A novel method of multimodal medical image fusion using fuzzy transform

Cited by 73 publications

References 38 publications

A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing

A Survey on Image Fusion Techniques for Image Enhancement in Digital Image Processing

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Neuro‐wavelet based intelligent medical image fusion

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