Union Laplacian pyramid with multiple features for medical image fusion

Du, Jiao; Li, Weisheng; Xiao, Bin; Nawaz, Qamar

doi:10.1016/j.neucom.2016.02.047

Cited by 187 publications

(79 citation statements)

References 27 publications

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“…In this section, two examples of functional medical image decomposition are shown in Figures . In Figures and , they show that the coarse images and detailed images from the input PET functional imaging data by LAP, NSCT, ST, BF, GFF, LES, and proposed methods, respectively. The visual evaluation phase of PET functional imaging data is summarized in the following list.…”

Section: Resultsmentioning

confidence: 99%

“…To effectively extract the salient information from the input medical images, a large number of advanced methods have been proposed in recent years. They use multiscale image decomposition scheme in multiscale transform (MST) based fusion methods as the image modeling tool, including Laplacian pyramid (LAP), ratio pyramid (RAP), and the discrete wavelet transform (DWT), tetrolet transform, nonsubsampled contourlet transform (NSCT), and shearlet transform (ST). The salient features are referred to high‐frequency coefficients in these MST methods.…”

Section: Introductionmentioning

confidence: 99%

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Two‐scale image decomposition based image fusion using structure tensor

2019

Int J Imaging Syst Tech

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The PET/SPECT image generates functional information of the human brain. In the field of multimodal medical image fusion, the decomposition scheme of PET/SPECT image in pseudo-color is thought to be negligible. In this article, a two-step model for PET/SPECT image decomposition is proposed to achieve contrast enhancement that inherently captures gradient to distinguish individual edge information from detail information. First, sharp structure is preserved using structure tensor on the intensity component of PET/SPECT image. Second, sharp structure in gray is transformed back into the image in pseudo-color by generalized intensity-hue-saturation, one of the color space methods. The combination of structure tensor and generalized intensity-hue-saturation based fusion technique can preserve not only more intensity information but also functional information content. Finally, we demonstrate the effectiveness of our proposed decomposition method in the context of PET/SPECT image and then make a comparison of fusion result by two-scale image fusion method in terms of the full-reference objective metric structural similarity and no-reference objective metric natural image quality evaluator. K E Y W O R D Sfunctional medical image in pseudo-color decomposition scheme, generalized intensity-huesaturation, multimodal medical image fusion, structure tensor

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two‐scale image decomposition based image fusion using structure tensor

2019

Int J Imaging Syst Tech

Self Cite

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“…Based on this theory, multiple pyramid fusion algorithms (e.g., Gaussian pyramid and Laplacian Pyramid) have been proposed with different pyramid decomposition structures, fusion rules and reconstruction methods. In the medical field, the pyramid method has been applied in fusing multimodal medical images, such as MRI/CT, PET/MRI and SPECT/MRI [57][58][59].…”

Section: Pyramid Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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“…Most of the past image fusion methods proposed a three step approach to the fusion problem. First, the source images were transformed into a particular domain using approaches such as multi-scale decomposition [3,4,5,6,7], sparse representation [8,9], mixture of multi-scale decomposition and sparse representation [10] and Intensity-Hue-Saturation [11] among others. Then, the transformed coefficients are combined using a predefined coefficient grouping based fusion strategy such as max selection and weighted-averaging.…”

Section: Introductionmentioning

confidence: 99%

Structural Similarity Based Anatomical and Functional Brain Imaging Fusion

Kumar

Hoffmann

Oelschlägel

et al. 2019

Lecture Notes in Computer Science

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Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image. One of the pivotal clinical applications of medical image fusion is the merging of anatomical and functional modalities for fast diagnosis of malign tissues. In this paper, we present a novel end-to-end unsupervised learning based Convolutional neural network (CNN) for fusing the high and low frequency components of MRI-PET grayscale image pairs publicly available at ADNI by exploiting Structural Similarity Index (SSIM) as the loss function during training. We then apply color coding for the visualization of the fused image by quantifying the contribution of each input image in terms of the partial derivatives of the fused image. We find that our fusion and visualization approach results in better visual perception of the fused image, while also comparing favorably to previous methods when applying various quantitative assessment metrics.

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Union Laplacian pyramid with multiple features for medical image fusion

Cited by 187 publications

References 27 publications

Two‐scale image decomposition based image fusion using structure tensor

Two‐scale image decomposition based image fusion using structure tensor

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Structural Similarity Based Anatomical and Functional Brain Imaging Fusion

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