An improved medical image fusion approach using PCA and complex wavelets

Himanshi,; Bhateja, Vikrant; Krishn, Abhinav; Sahu, Akanksha

doi:10.1109/medcom.2014.7006049

Cited by 24 publications

(8 citation statements)

References 39 publications

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“…IV. In this stage, the decomposed approximation and the detailed coefficients from each of the source images are fused by PCA [9], [28]- [29]. PCA being an orthogonal transform helps to reduce the redundancy present in both the source images as well as serves to improve upon the non-directionality limitation of SWT.…”

Section: Proposed Fusion Methodologymentioning

confidence: 99%

“…For instance, if contrast enhancement is performed on the CT and MRI scans, it can only serve to improve upon the contrast of the individual scans but still the problem of examining different modalities simultaneously prevails. This calls upon the need to integrate the useful as well as complimentary information from the images (which are the outcome of various sensor modalities for diagnosis) using fusion algorithms to yield a single image for optimum analysis and diagnosis [8]- [9]. Other potential advantages of image fusion include: the reduction of the storage capacity by compiling the data apparent in two images into a single fused one.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

et al. 2015

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Section: Proposed Fusion Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

et al. 2015

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“…In spatial domain, the values of pixels are manipulated directly i.e., all the operations are performed on original image pixels. Methodologies in this domain includes, Averaging of pixel values, Selecting maximum or minimum values, Principal component analysis (PCA), Intensity Hue saturation (IHS) [4][5][6].Fusion technique in spatial domain introduces spatial distortions in the finally fused image. Another is transform domain, in this, first the image is transformed to another domain and then the fusion algorithms are applied [3].…”

Section: Review On Fusion Approachesmentioning

confidence: 99%

“…Wavelets can be categorized as Discrete wavelet transforms (DWT), Stationary or Redundant wavelet transform (SWT or RWT) or Continuous wavelet transform (CWT). Wavelet transform basically is a multiresolution analysis in which the source image is decomposed into different levels and analyzed so that the features missing at one level can be restored at other [5]. After decomposition, different fusion rules can be applied to fuse these low and high band image coefficients.…”

Section: Review On Fusion Approachesmentioning

confidence: 99%

Adaptive Parameter estimation based Multimodal Medical Image Fusion Frame work in SWT domain

Goel¹

2018

IJRASET

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Multimodal medical image fusion is extracting the necessary information from the source images into a single image which could be more informative for resourceful clinical study. This paper presents multimodal medical image fusion framework using the stationary wavelet transform (SWT) for medical images (i.e., magnetic resonance imaging and computed tomography scan) acquired using two distinct medical imaging sensors. The main objectives of the proposed approach are to improve the quality of the image from the point of view of clinical diagnosis, to optimize the performance and reduce complexity. The main advantage of proposed methodology is improvement upon the directionality, contrast, and phase information in the fused image. In the proposed fusion methodology, principal component analysis is employed in SWT domain, to improve upon redundancy. Maximum fusion rule is also applied to enhance the contrast of the image features. Detailed study of fused images is carried out using different wavelet families and various fusion metrics. The comparative analysis of different fusion quality parameters of the improved approach with other state-of-the-art fusion methods is carried out.

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“…Wavelet transform family includes DTCWT [16], SWT [17], Redundant Wavelet Transform [18]. [22]- [24], DWT [19] etc. So far many works had been done in these fields.…”

Section: Introductionmentioning

confidence: 99%

Medical image fusion with Laplacian Pyramids

Sahu¹,

Bhateja²,

Krishn³

et al. 2014

2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom)

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Fusion of various images aids the rejuvenation of complementary attributes of the images. Similarly, medical image fusion constructs a composite image comprehending significant traits from multimodal source images. Current work exhibits medical image fusion utilizing Laplacian Pyramid (LP) employing DCT. LP decomposes the source medical images as different low pass filtered images, resembling a pyramidal structure. As the pyramidal level of decomposition increases, the quality of the fused image also increases. The proposed technique provides a fused image with better edges and information content from human visual system (HVS) point of view. Qualitative and quantitative analysis of the proposed technique is found to be superior than that of Daubechies complex wavelet transform (DCxWT).

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An improved medical image fusion approach using PCA and complex wavelets

Cited by 24 publications

References 39 publications

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Adaptive Parameter estimation based Multimodal Medical Image Fusion Frame work in SWT domain

Medical image fusion with Laplacian Pyramids

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