Bone SPECT/CT image fusion based on the discrete Hermite transform and sparse representation

Barba-J, Leiner; Vargas-Quintero, Lorena; Calderón-Agudelo, Jose A.

doi:10.1016/j.bspc.2021.103096

Cited by 12 publications

(3 citation statements)

References 42 publications

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“…Yang and Li were the first to propose using a DCT dictionary for multi-focus image fusion (Yang and Li 2010). However, Aharon demonstrated that an adaptive dictionary derived from input images outperformed a fixed dictionary (Aharon et al 2006), and there was a similar statement in Barba et al (2022). Thus, Yu proposed a joint-sparsity-model-based fusion scheme in which by incorporating the complete source of overlapping image patches, a redundant dictionary was trained (Yu et al 2011).…”

Section: Introductionmentioning

confidence: 95%

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Hu,

Cai,

et al. 2024

Phys. Med. Biol.

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ObjectiveMultimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF.ApproachThe fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain's sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction.Main resultsExperimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations.SignificanceThe proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level with sub-band correlation in the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhances sub-band correlation to promote the preservation of structural and textural details.

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Section: Introductionmentioning

confidence: 95%

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Hu,

Cai,

et al. 2024

Phys. Med. Biol.

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“…In order to identify the significant regions that are defined by edges of the right size and high amplitude, the morphological processing of linear filter residuals serves as the basis for the selection of those regions. Furthermore, a fusion method to fuse CT-SPECT images using discrete Hermite transform and SR was proposed by Barba-J et al (2022). The traditional sparse representation (CSR) (Wang et al, 2021a) and the Joint Sparse Model (JSM) (Zhang et al, 2023a) are two examples of improvement strategies that are based on sparse representation.…”

Section: Introductionmentioning

confidence: 99%

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

Lepcha,

Goyal,

Dogra

et al. 2024

Journal of Computer Science

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Medical imaging has been widely used to diagnose diseases over the last two decades. Medical professionals still struggle to diagnose diseases using a single modality since there is a shortage of data in this domain. Therefore, images of specific organs with diseases from a variety of medical imaging systems can be combined using a technique called image fusion. Medical image fusion has prompted immense requisite applications in clinical applications in recent years. However, the fusion of medical images still facing a variety of challenges due to the input image quality. Protonema such as noise and low-contrast input medical images significantly reduces the quality of medical images. Still, recent image fusion methods are not significantly able to address the image quality problems. In order to address these problems, this study introduces a novel image fusion method that provides effective fusion performance even if the input images are noisy or low-contrast by combining the benefits of synthetic focusing degree criterion with a special kernel set. First, a Gaussian Curvature Filter (GCF) is used to sharpen the images in order to perform a Salient Feature Extraction (SFE). Then, we create a synthetic Focusing Degree Condition (FDC) that combines the Spatial Frequency (SF) and the Local Variance (LV) of the images to get the coarse fusion maps. The course fusion maps are then processed using median and morphological filters. The weighted fusion technology is used to generate the fused image. Finally, image enhancement is achieved by adding a special kernel to the fused image to obtain the final fusion result. Experimental results on the publicly available datasets exhibited that the proposed research article obtains the best results in terms of noisy and low-contrast medical images. Overall, it achieves significant performance both qualitatively and quantitively when compared to other competing state-of-the-art methods.

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“…Over the past few decades, a vast array of fusion algorithms has been suggested by researchers, such as: Laplacian pyramid (LP) [7][8][9], non-subsampled contourlet transform (NSCT) [10][11][12][13], non-subsampled Shearlet transform (NSST) [3,[14][15][16], and wavelet transform [17,18]. Barba-J, Vargas-Quintero, and Calderón-Agudelo [19] proposed a transform-based method for the fusion of CT and SPECT images, which used discrete Hermite transform to decompose the source images, and then fused them. Li et al [20] proposed a method that combines Laplace Pyramid (LP) and SR, using LP to decompose the image and SR to merge the coefficients, respectively.…”

Section: Introductionmentioning

confidence: 99%

CFIFusion: Dual‐Branch Complementary Feature Injection Network for Medical Image Fusion

Xie,

Yu,

Ding

2024

Int J Imaging Syst Tech

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The goal of fusing medical images is to integrate the diverse information that multimodal medical images hold. However, the challenges lie in the limitations of imaging sensors and the issue of incomplete modal information retention, which make it difficult to produce images encompassing both functional and anatomical information. To overcome these obstacles, several medical image fusion techniques based on CNN or transformer architectures have been presented. Nevertheless, CNN technique struggles to establish extensive dependencies between the fused and source images, and transformer architecture often overlooks shallow complementary features. To augment both the feature extraction capacity and the stability of the model, we introduce a framework, called dual‐branch complementary feature injection fusion (CFIFusion) technique, a for multimodal medical image fusion framework that combines unsupervised models of CNN model and transformer techniques. Specifically, in our framework, the entire source image and segmented source image are input into an adaptive backbone network to learn global and local features, respectively. To further retain the source images' complementary information, we design a multi‐scale complementary feature extraction framework as an auxiliary module, focusing on calculating feature differences at each level to capture the shallow complementary information. Then, we design a shallow information preservation module tailored for sliced image characteristics. Experimental results on the Harvard whole brain atlas dataset demonstrate that CFIFusion shows greater benefits than recent state‐of‐the‐art algorithms in terms of both subjective and objective evaluations.

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Bone SPECT/CT image fusion based on the discrete Hermite transform and sparse representation

Cited by 12 publications

References 42 publications

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

CFIFusion: Dual‐Branch Complementary Feature Injection Network for Medical Image Fusion

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