Review of Image Fusion Based on Pulse-Coupled Neural Network

Wang, Zhaobin; Wang, Shuai; Zhu, Ying; Ma, Yide

doi:10.1007/s11831-015-9154-z

Cited by 49 publications

(20 citation statements)

References 80 publications

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“…For image fusion, a pulse-coupled neural network (PCNN) is often used as a feature extraction method [87,88]. As shown in Figure 2, the PCNN adopts a single layer, two-dimensional and laterally-connected neural network.…”

Section: Pulse-coupled Neural Networkmentioning

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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Section: Pulse-coupled Neural Networkmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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“…Many researchers have paid their more attention to image fusion technique based on pulse coupled neural network. Literature [35] described the models and modified ones. As to the multi-focus image fusion problem, Veshki et al utilized the sparse representation using a coupled dictionary to address the focused and blurred feature problem for higher quality [36].…”

Section: Coupled Data Fusionmentioning

confidence: 99%

“…The original images which are the low spatial resolution multispectral image located in Beijing, China and the corresponding high spatial resolution panchromatic image are as follows. (35) ϒ(θ, θ and 256 × 256 pixels respectively. The multispectral and panchromatic image data of area I are read and initialized by MATLAB, and stored as tensor X ∈ R 300×300×3 and matrix Y ∈ R 300×300 .…”

Section: Cif-opt Algorithmmentioning

confidence: 99%

Exploring coupled images fusion based on joint tensor decomposition

Ren

Yeh

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Image data fusion has been a hot research topic in neuroscience, metabonomics and other fields, and has been widely used in real life. The coupled data fusion algorithm usually utilizes the information of one data set to improve the estimation accuracy and the interpretation of related potential variables of other data sets. With the development of electronic and imaging technology, it is difficult to find accurate data for digital images for human beings, such as medical science [1], information remote sensing and so on. In this situation, people hope to primitively analyze mass images and select the information quickly and effectively by more convenient calculation way. Moreover, traditional data processes mechanisms are less efficient when faced with large amounts of data in human-centric computing. And we apply the tensor structures to represent massive data to solve above problems in this paper because of its multi-dimensional property. Multi-source image data fusion refers to making the comprehensive image analysis for the image data obtained from different acquisition devices (known as multisource heterogeneous image data), so as to achieve complementary information from

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“…Although PCNN achieves excellent results, the PCNNbased fusion methods are complex and inefficient for dealing with different source images. Wang et al illustrated that the amount of the channels of the PCNN and parameters limits its application [20]. Many researchers have improved the original PCNN model for making it more appropriate Mathematical Problems in Engineering for image fusion.…”

Section: Introductionmentioning

confidence: 99%

A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

Chen

et al. 2018

Mathematical Problems in Engineering

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An algorithm based on pulse-coupled neural network (PCNN) constructed in the Tetrolet transform domain is proposed for the fusion of the visible and passive millimeter wave images in order to effectively identify concealed targets. The Tetrolet transform is applied to build the framework of the multiscale decomposition due to its high sparse degree. Meanwhile, a Laplacian pyramid is used to decompose the low-pass band of the Tetrolet transform for improving the approximation performance. In addition, the maximum criterion based on regional average gradient is applied to fuse the top layers along with selecting the maximum absolute values of the other layers. Furthermore, an improved PCNN model is employed to enhance the contour feature of the hidden targets and obtain the fusion results of the high-pass band based on the firing time. Finally, the inverse transform of Tetrolet is exploited to obtain the fused results. Some objective evaluation indexes, such as information entropy, mutual information, and / , are adopted for evaluating the quality of the fused images. The experimental results show that the proposed algorithm is superior to other image fusion algorithms.

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Review of Image Fusion Based on Pulse-Coupled Neural Network

Cited by 49 publications

References 80 publications

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Exploring coupled images fusion based on joint tensor decomposition

A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

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