Enhanced regularized least square based discriminative projections for feature extraction

Yuan, Ming‐Dong; Feng, Da‐Zheng; Liu, Wenjuan; Xiao, Chun-Bao

doi:10.1016/j.sigpro.2017.04.018

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…The spatial resolution of all test images is set to 256 256  , and to make sure the registration has been realized, we take the feature-based registration algorithm for each pair, i.e. the method of complementary Harris feature point extraction based on mutual information [37], which has strong robustness and is able to adapt to various image characteristics and variations.…”

Section: Source Imagesmentioning

confidence: 99%

MRI Image Fusion Based on Sparse Representation with Patch-based Multiple Salient Features Measurement

Hu¹,

Cai²,

Xu³

et al. 2023

Preprint

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Multimodal medical image fusion is a fundamental but challenging problem in the fields of brain science research and brain disease diagnosis, and it is challenging for sparse representation (SR)-based fusion to characterize activity level with single measurement and no loss of effective information. In this paper, the Kronecker-criterion-based SR framework is applied for medical image fusion with a patch-based activity level integrating salient features of multiple domains. Inspired by the formation process of vision system, the spatial saliency is characterized by textural contrast (TC), which is composed of luminance and orientation contrasts to promote more highlighted texture information to participate in the fusion process. As substitution of the conventional l1-norm-based sparse saliency, a metric of sum of sparse salient features (SSSF) is used for promoting more significant coefficients to participate in the composition of activity level measure. The designed activity level measure is verified to be more conducive to maintain the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verify the effectiveness of the proposed fusion method on both visual quality and objective assessment. Furthermore, the research work of this paper is helpful for further detection and segmentation of medical images.

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Section: Source Imagesmentioning

confidence: 99%

MRI Image Fusion Based on Sparse Representation with Patch-based Multiple Salient Features Measurement

Hu¹,

Cai²,

Xu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Yang et al [12] developed a regularised least square based discriminative projections, which maximises the between‐class scatter adopted by LDA and minimises the within‐class compactness by the reconstruction residual from the same class. Yuan et al [13] propose an enhanced regularised least square based discriminative projections, where each sample is reconstructed by all the associated coefficients and the distances between each sample and all its reconstructed within‐class samples are minimised. However, both

ℓ_{1}

‐graph and

ℓ_{2}

‐graph only collect the local structure of each datum and lose sight of the global structure of the data from the perspective of the entire dataset.…”

Section: Introductionmentioning

confidence: 99%