An efficient medical image fusion method using contourlet transform based on PCM

Al-Azzawi, Nemir Ahmed; Sakim, Harsa Amylia Mat; Abdullah, Ali A.

doi:10.1109/isiea.2009.5356493

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…For example, combination of complex contourlet transform with wavelet has been shown to result in robust image fusion [95,96]. Transforms based methods are also applied for liver diagnosis [99], risk factor fusion [100], prediction of multifactorial diseases [100], parametric classification [100], local image analysis [101], multi-modality image fusion [102,103,95,104,96,81]. Possibilistic clustering methods show improvement over the fuzzy c-means clustering and have a wide range of application in registration stages of image fusion.…”

Section: Other Methodsmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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Section: Other Methodsmentioning

confidence: 99%

Medical image fusion: A survey of the state of the art

2014

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“…There are several methods that are based on dimensionality reduction techniques such as independent component analysis (ICA) [86,93] and principal component analysis (PCA) [94][95][96][97]. These dimensionality reduction techniques often find their use as feature processing methods and are used in combination with techniques such as ones based on wavelets [86].…”

Section: H Other Methodsmentioning

confidence: 99%

“…There are different mathematical transforms on features that can enhance the performance of the image fusion. For example, combination of complex contourlet transform with wavelet has been shown to result in robust image fusion [95,96]. Transforms based methods are also applied for liver diagnosis [99], risk factor fusion [100], prediction of multifactorial diseases [100], parametric classification [100], local image analysis [101], multimodality image fusion [102,103,95,104,96,81].…”

Section: H Other Methodsmentioning

confidence: 99%

A Survey on Multimodal Medical Image Fusion

Devanna¹,

Kumar²

2017

IOSR JCE

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“…The PSNR is the most commonly used as a measure of quality of reconstruction in image denoising [10]. The P SN R for both noisy and denoised images were identified using the following formula:…”

Section: The Contourlet Transformmentioning

confidence: 99%

A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images

Hamdi¹

2011

Image Processing & Communications

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A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical ImagesA special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that the contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio

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An efficient medical image fusion method using contourlet transform based on PCM

Cited by 12 publications

References 13 publications

Medical image fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

A Survey on Multimodal Medical Image Fusion

A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images

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