3D medical image fusion using dual tree complex wavelet transform

Kushwaha, Arati; Khare, Ashish; Prakash, Om; Song, Jong−In; Jeon, Moongu

doi:10.1109/iccais.2015.7338671

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…This database provides a large number of coregistered infrared and visible images. All the experiments are implemented The proposed method is compared with six typical fusion methods: multiscale singular value representation (MSVD) [34], discrete harmonic wavelet transform (DCHWT) [35], discrete wavelet transform (DWT) [21], two-scale fusion (TS) [36], dual-tree complex wavelet transform (DTCWT) [22], and curvelet transform (CVT) [37].…”

Section: Methodsmentioning

confidence: 99%

“…The image fusion method based on multiscale transformation has been widely investigated because of its compatibility with human visual perception. In recent years, a variety of fusion methods based on multiscale transform have been proposed, such as low-pass pyramid (RP) [18], gradient pyramid (GP) [19], nonsubsampled contourlet transform (NSCT) [20], discrete wavelet transform (DWT) [21], and dual-tree complex wavelet transform (DTCWT) [22]. However, image fusion methods based on multiscale transform are usually complex and suffer from long processing time and energy consumption issues, which limit their application.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Heterogeneous Image Fusion Method Based on DCT and Anisotropic Diffusion for UAVs in Future 5G IoT Scenarios

Hao

Wen

et al. 2020

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles, with their inherent fine attributes, such as flexibility, mobility, and autonomy, play an increasingly important role in the Internet of Things (IoT). Airborne infrared and visible image fusion, which constitutes an important data basis for the perception layer of IoT, has been widely used in various fields such as electric power inspection, military reconnaissance, emergency rescue, and traffic management. However, traditional infrared and visible image fusion methods suffer from weak detail resolution. In order to better preserve useful information from source images and produce a more informative image for human observation or unmanned aerial vehicle vision tasks, a novel fusion method based on discrete cosine transform (DCT) and anisotropic diffusion is proposed. First, the infrared and visible images are denoised by using DCT. Second, anisotropic diffusion is applied to the denoised infrared and visible images to obtain the detail and base layers. Third, the base layers are fused by using weighted averaging, and the detail layers are fused by using the Karhunen–Loeve transform, respectively. Finally, the fused image is reconstructed through the linear superposition of the base layer and detail layer. Compared with six other typical fusion methods, the proposed approach shows better fusion performance in both objective and subjective evaluations.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Heterogeneous Image Fusion Method Based on DCT and Anisotropic Diffusion for UAVs in Future 5G IoT Scenarios

Hao

Wen

et al. 2020

Wireless Communications and Mobile Computing

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show abstract

“…Furthermore, DWT efficiently manages different resolutions, making it practical for medical images. Several extensions of wavelet transform methods have been proposed in recent years, such as multi-wavelet transform, Daubechies wavelet transform, wavelet with PCA, fuzzy wavelet approaches dual-tree complex wavelet transform 49,63 . Burrus presents the appropriate mathematical representation of DWT in equation [1], where two variable sets are presumed to perform essential functions and are used similarly to the short-time Fourier transform or the Gabor transform 64 .…”

Section: A General Discrete Wavelet Image Fusionmentioning

confidence: 99%

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Das

Chandra

2022

ECS Trans.

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Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information.

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