MR and PET Image Fusion Using Nonparametric Bayesian Joint Dictionary Learning

Shabanzade, Fahim; Khateri, Mohammad; Liu, Zheng

doi:10.1109/lsens.2019.2925072

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The sparse representation model has also been widely used in several image fusion applications [32][33][34][35]. A comprehensive review of this group is given in [36].…”

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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“…The sparse representation model has also been widely used in several image fusion applications [32][33][34][35]. A comprehensive review of this group is given in [36].…”

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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“…The multi‐scale transform method divides images into different layers, and fuses layers to ensure the fusion result completely. Popular multi‐scale transforms are mainly wavelet, curvelet, pyramid and some effective edge‐preserving filters, such as cartoon and texture image decomposition, relative total variation (RTV) structure extraction method and

L_{0}

gradient minimization smoothing method [13–15]. The edge‐preserving filter is widely used in image fusion because of its simple operation, easy implementation and effective decomposition.…”

Section: Introductionmentioning

confidence: 99%

Medical image fusion and noise suppression with fractional‐order total variation and multi‐scale decomposition

Zhang

Hui

2021

IET Image Processing

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Fusion and noise suppression of medical images are becoming increasingly difficult to be ignored in image processing, and this technique provides abundant information for the clinical diagnosis and treatment. This paper proposes a medical image fusion and noise suppression model in pixel level. This model decomposes the original image into a noiseless base layer, a large-scale noiseless detail layer and a small-scale detail layer which contains details and noise information. The fractional-order derivative and saliency detection are used to construct the weight functions to fuse the base layers. The proposed total variation model combines the fractional-order derivative to fuse the small-scale detail layers. The mathematical properties and time complexity of the total variation model are also analysed. And choose-max method is used to fuse the large-scale detail medical layers simply. Our approach is based on fractional-order derivative, which enables keep more information and decrease blocky effects more effectively compared with the integer-order derivative. To verify the validity, the proposed method is compared with some fusion methods in the subjective and objective aspects. Experiments show that the proposed model fuses the source information fully and decreases noise cleanly.

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