Information fusion in the realm of medical applications – A bibliographic glimpse at its growing appeal

Dasarathy, Belur V.

doi:10.1016/j.inffus.2011.06.003

Cited by 45 publications

(16 citation statements)

References 224 publications

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“…The framework for fusion, imaging modality, and certain organ are the three basic elements of studies on medical image fusion [5]. To date, few reports or studies have addressed the optimal combination of the aforementioned three factors due to the physical, even mental, differences for observers and diverse mathematical framework.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…For instance, if contrast enhancement is performed on the CT and MRI scans, it can only serve to improve upon the contrast of the individual scans but still the problem of examining different modalities simultaneously prevails. This calls upon the need to integrate the useful as well as complimentary information from the images (which are the outcome of various sensor modalities for diagnosis) using fusion algorithms to yield a single image for optimum analysis and diagnosis [8]- [9]. Other potential advantages of image fusion include: the reduction of the storage capacity by compiling the data apparent in two images into a single fused one.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

et al. 2015

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“…Therefore, combining different information from several images to get a new improved composite image becomes important area of research. Image fusion applications are found in diverse areas including medical imaging [5][6][7], forensic science [8], remote sensing [9], surveillance [9] etc.…”

Section: Introductionmentioning

confidence: 99%

Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule

Prakash

Srivastava

Khare

2013

2013 Ieee Conference on Information and Communication Technologies

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The objective of image fusion is to combine relevant information from two or more images of the same scene into a single composite image which is more informative and is more suitable for human and machine perception. In recent past, different methods of image fusion have been proposed in literature both in spatial domain and wavelet domain. Spatial domain based methods produce spatial distortions in the fused image. Spatial domain distortion can be well handled by the use of wavelet transform based image fusion methods. In this paper, we propose a pixel-level image fusion scheme using multiresolution Biorthogonal wavelet transform (BWT). Wavelet coefficients at different decomposion levels are fused using absolute maximum fusion rule. Two important properties wavelet symmetry and linear phase of BWT have been exploited for image fusion because they are capable to preserve edge information and hence reducing the distortions in the fused image. The performance of the proposed method have been extensively tested on several pairs of multifocus and multimodal images both free from any noise and in presence of additive white Gaussian noise and compared visually and quantitatively against existing spatial domain methods. Experimental results show that the proposed method improves fusion quality by reducing loss of significant information available in individual images. Fusion factor, entropy and standard deviation are used as quantitative quality measures of the fused image.

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Information fusion in the realm of medical applications – A bibliographic glimpse at its growing appeal

Cited by 45 publications

References 224 publications

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

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

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule

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