Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Hu, Qiu; Hu, Shaohai; Zhang, Fengzhen

doi:10.1016/j.image.2019.115758

Cited by 41 publications

(30 citation statements)

References 29 publications

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“…The joint dictionary does not require any prior knowledge and can provide adaptive representation to source images. A separable dictionary learning-based method was proposed in [40]. This method combines separable dictionary optimization with a Gabor filter to solve the spatial inconsistency problem in flat regions.…”

Section: B Dictionary Learning-based Methodsmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

2020

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Medical image fusion techniques primarily integrate the complementary features of different medical images to acquire a single composite image with superior quality, reducing the uncertainty of lesion analysis. However, the simultaneous extraction of more salient features and less meaningless details from medical images by using multi-scale transform methods is a challenging task. This study presents a two-scale fusion framework for multimodal medical images to overcome the aforementioned limitation. In this framework, a guided filter is used to decompose source images into the base and detail layers to roughly separate the two characteristics of source images, namely, structural information and texture details. To effectively preserve most of the structural information, the base layers are fused using the combined Laplacian pyramid and sparse representation rule, in which an image patch selection-based dictionary construction scheme is introduced to exclude the meaningless patches from the source images and enhance the sparse representation capability of the pyramid-decomposed low-frequency layer. The detail layers are subsequently merged using a guided filtering-based approach, which enhances contrast level via noise filtering as much as possible. The fused base and detail layers are reconstructed to generate the fused image. We experimentally verify the superiority of the proposed method by using two basic fusion schemes and conducting comparison experiments on nine pairs of medical images from diverse modalities. The comparison of the fused results in terms of visual effect and objective assessment demonstrates that the proposed method provides better visual effect with an improved objective measurement because it effectively preserves meaningful salient features without producing abnormal details. INDEX TERMS Medical image fusion, guided filtering, sparse representation, salient features, meaningless details.

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Section: B Dictionary Learning-based Methodsmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

2020

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“…In the recent literature, there are plenty contributions, reporting image-based smoothing (filtering) for medical image data enhancement, typically for medical signals and images. Generally, the data smoothing are mostly performed either in spatial or frequency domain [ 23 ]. The filters, utilizing the pixel’s distribution have become mainly interesting for the research community.…”

Section: Recent Workmentioning

confidence: 99%

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Stanke

Kubíček

Vilímek

et al. 2020

Sensors

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Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

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“…These are dictionary learning (Hu et al. 2020 ), local-features fuzzy sets (Ullah et al. 2020 ), deep learning (Algarni 2020 ; Xia et al.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers have designed and implemented fusion approaches to obtain efficient multi-modality biomedical images (Ravi and Krishnan 2018;Hu et al 2020). However, many researchers have utilized the existing image fusion approaches.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the issues associated with the existing approaches, many researchers have utilized deep learning and dictionary learning kind of approaches. These are dictionary learning (Hu et al 2020), local-features fuzzy sets (Ullah et al 2020), deep learning (Algarni 2020;Xia et al 2019;Zhou et al 2019), deep learning and NSST , etc. that found to be the best tool to obtain efficient multi-modality fused images.…”

Section: Introductionmentioning

confidence: 99%

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Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks

Kaur

Singh

2020

J Ambient Intell Human Comput

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The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more information as compared to traditional medical images. However, the construction of multi-modality images is not an easy task. The proposed approach, initially, decomposes the image into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an extreme version of the Inception (Xception) is used for feature extraction of the source images. The multi-objective differential evolution is used to select the optimal features. Thereafter, the coefficient of determination and the energy loss based fusion functions are used to obtain the fused coefficients. Finally, the fused image is computed by applying the inverse NSCT. Extensive experimental results show that the proposed approach outperforms the competitive multi-modality image fusion approaches.

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Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Cited by 41 publications

References 29 publications

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks

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