Medical image fusion using discrete fractional wavelet transform

Xu, Xiaojun; Wang, Youren; Shuai, Chen

doi:10.1016/j.bspc.2016.02.008

Cited by 101 publications

(38 citation statements)

References 17 publications

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“…SWT is basically an inherently redundant based scheme as the particular output of every level of SWT having the similarratio [2] of samples similar as the input so for a decomposition phase of N levels and there is a kind of redundancy of N in the wavelet based coefficients [3].…”

Section: A Stationary Wavelet Transform (Swt)mentioning

confidence: 99%

“…Non local mean is applicable for the redundant data of the image in pixel [2] or spatial domain to reduce the noise very [14] [16] effectively and each neighbourhood in a general image have various duplicate copies in similar images. Generally non local mean is a kind of filter where it estimates the intensity of pixel noise frees [15].…”

Section: B Non-local Mean Filter (Nlm)mentioning

confidence: 99%

“…The useful information is enrolled all in focus information. Image fusion [2] [7] [8] technique has became more strongly [9] [12] and widely used technique to enhance the human visual elucidation of any image in different application such as multi-focus [20] image, military affairs fields, robotics, medical health diagnosis area, biometric machine, remote sensing fields, monitoring application, surveillance area, computer vision etc [11] [12] [15]. The major objective of image fusion is to retain important, comprehensive and accurate information than the separate source of images [21].…”

Section: Introductionmentioning

confidence: 99%

“…Xiaojunxe et.al. [2][2016] also discussed on multimodal concept and presented a multimodal scheme based on discrete fractional wavelet (DFRWT). They decomposed source images by this DFRWT technique in various order and further applied inverse DFRWT to get the resultant fused images.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

Joshi¹,

Joshi²,

Diwakar³

et al. 2019

IJITEE

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Today’s research era, image fusion is a actual step by step procedure to develop the visualization of any image. It integrates the essential features of more than a couple of images into a individual fused image without taking any artifacts. Multifocus image fusion has a vital key factor in fusion process where it aims to increase the depth of field using extracting focused part from different multiple focused images. In this paper multi-focus image fusion algorithm is proposed where non local mean technique is used in stationary wavelet transform (SWT) to get the sharp and smooth image. Non-local mean function analyses the pixels belonging to the blurring part and improves the image quality. The proposed work is compared with some existing methods. The results are analyzed visually as well as using performance metrics.

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Section: A Stationary Wavelet Transform (Swt)mentioning

confidence: 99%

Section: B Non-local Mean Filter (Nlm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

Joshi¹,

Joshi²,

Diwakar³

et al. 2019

IJITEE

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show abstract

“…The basic wavelets transforms include Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Lifting Wavelet transform (LWT) , Discrete Dyadic Wavelet Transform (DDWT), Dual Tree Complex Wavelet Transform (DTCWT) and Discrete Fractional Wavelet transform. (DFRWT) extended to complex wavelets as Ripplet Transform (RT), Shearlet Transform (ST), Curvelet Transform (CVT), the Contourlet Transform (ConT), Non-subsampled Contourlet Transform (NSCT) [7], [26] and Discrete Fractional Wavelet Transform [27]. The multiscale techniques in transform domain are enlisted in Fig.…”

Section: B Transform Domain Image Fusionmentioning

confidence: 99%

Multimodal Image Fusion in Clinical Research

Kaur*¹,

Singh²,

Vig³

2019

IJRTE

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With the advancement in medical imaging, far reaching changes are perceptible in clinical analysis. Most of the diagnosed medical evaluation is a resultant of imaging or it is in close synchronization with imaging techniques. This promotes the need to closely read, evaluate and collaborate images. Medical image fusion is a technique to collaborate the clinical images acquired from one or many modalities. Multimodal image analysis during fusion well capitalizes the strength of each medical modality. Incorporating the features from multimodal input images thus holds added potential to abet better-quality diagnosis. Medical image fusion is an intricate task specially when high quality fused image, possessing all relevant information and reasonable operating speed is aimed. Many efforts have been undertaken in this field resulting in diverse research approaches. Image fusion can be performed using medical images obtained from single modality or from multiple modalities. This paper has been designed collaborating the work based on multimodal medical images in multiscale image fusion domain. When the same region, organ or a tissue is captured from various different perceptions, complimentary information is maximized and diagnostic value is reinforced. The fusion framework using Mexican Hat wavelet is proposed using adaptive median filtering detailing each executable fusion block. The Fusion techniques, pre and post processing aspects and evaluation mechanisms are illustrated from literature. The drifts of researchers from single processing to multiple processing hybrid techniques are discussed. The medical modality aspects are detailed. These may provide as a valuable reference to understand the image fusion trade-offs comprehensively with future viabilities.

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Medical image fusion method by using Laplacian pyramid and convolutional sparse representation

Liu

Chen

et al. 2019

Concurrency and Computation

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SummaryMedical image fusion is a technology of combining multi‐modal images to generate a composite image, which is favorable to improve the capability of doctors in diagnosis and treatment of the disease. In order to achieve good performance, a fusion method by combining Laplacian pyramid (LP) and convolutional sparse representation (CSR) is proposed. In the proposed fusion method, LP transform is performed on each pair of pre‐registered computed tomography image and magnetic resonance image to obtain their detail layers and base layer. Then, the base layer is fused with a CSR‐based approach, whereas the detail layers are merged using the popular “max‐absolute” rule. Finally, the fused image is reconstructed by performing the inverse LP transform over the fused base layer and detail layers. The advantages of our method are that the texture detail information contained in source images can be fully extracted and the overall contrast of the final fused image will not be decreased. Experimental results demonstrate the superiority of the proposed method.

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Medical image fusion using discrete fractional wavelet transform

Cited by 101 publications

References 17 publications

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

Multimodal Image Fusion in Clinical Research

Medical image fusion method by using Laplacian pyramid and convolutional sparse representation

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