Iterative block level principal component averaging medical image fusion

Rajangam, Vijayarajan; Muttan, S.

doi:10.1016/j.ijleo.2014.04.068

Cited by 23 publications

(10 citation statements)

References 27 publications

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“…The essence in statistics-based methods lies in the data-driven technique and high order statistics that can reveal the underlying pattern across multiple modes of data. Principal component analysis (PCA) [232] , [233] , [234] , [235] together with Hidden Markow Tree (HMT) [236] , [237] , [238] are two typical examples of statistic methods in the field of multi-modal medical image fusion.…”

Section: Multimodal Imaging Data Fusion: Methodologymentioning

confidence: 99%

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

et al. 2020

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Highlights We analysed over 450 references from all well-famed databases. We provided a comprehensive survey on multimodal data fusion in neuroimaging. This review encompassed current challenges & applications, strengths &limitations. Fundamental fusion rules, and fusion quality assessment methods were reviewed. Atlas-based fusion segmentation, quantification, & applications were reviewed.

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Section: Multimodal Imaging Data Fusion: Methodologymentioning

confidence: 99%

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

et al. 2020

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“…Examples of statics methods for multi-modal medical image fusion are principal component analysis (PCA) [28][29][30] and Hidden Markov Tree (HMT) [31,32].…”

Section: B Statisticsmentioning

confidence: 99%

“…Inspired by the HVS, the algorithms for detecting the corners, edges and saliency features are used as fusion rules in multi-modal medical image fusion, such as visibility [30], smallest univalve segment assimilating nucleus (SUSAN) [33], artificial neural networks (ANN) [34][35][36][37] and retina-inspired model (RIM) [4,38,39].…”

Section: Human Visual Systemmentioning

confidence: 99%

An overview of multi-modal medical image fusion

et al. 2016

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“…PCA is a dimension reduction technique [23] which represents a whole data set with very few principal components [24] and hence the principal components derived from the covariance properties of the source data sets deliver meaningful weights for a linear weighted spatial domain fusion [25]. This spatial domain fusion is often degraded by spectral distortions [26,27] and thus frequency domain fusion is often preferred.…”

Section: Pca Fusionmentioning

confidence: 99%

Linear weighted watermarking using normalized principal components

Sangeetha¹,

Anita²

2017

Complex Intell. Syst.

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This manuscript introduces a novel linear weighted watermarking through normalized principal components using discrete wavelet transform (DWT) and singular value decomposition (SVD). Weight evaluation for embedding a singular value matrix of a watermark into a host is a trivial task in watermark embedding process. This task is accomplished by the normalized principal components derived from the singular value matrices of HH subbands of the host and the watermark images. Experiments are conducted to analyze the effectiveness of the proposed watermark embedding and watermark-host extraction processes against the other DWT-SVD based watermarking schemes. Performance of this method is analyzed by peak signal to noise ratio, structural similarity index and correlation coefficient. The proposed method is also tested against various geometrical and non-geometrical attacks on watermarked images.

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Iterative block level principal component averaging medical image fusion

Cited by 23 publications

References 27 publications

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

An overview of multi-modal medical image fusion

Linear weighted watermarking using normalized principal components

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