GPU Computing based fast discrete wavelet transform for <i>l</i><sub>1</sub>-regularized SPIRiT reconstruction

Yao, Tiechui; Xiao, Li; Zhao, Debin; Sun, Yuzhong

doi:10.1080/13682199.2018.1496220

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…The normalized brain area is 160*160*192, and the number of convolution kernels increases with the network layers. We use parallel computing to speed up the processing of images [ 33 ]. Other DeepHipp’s parameters can be defined by users according to actual needs.…”

Section: Methodsmentioning

confidence: 99%

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

Wang,

Lei,

Zhao

et al. 2023

BMC Med Imaging

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Background The hippocampus is a key area of the brain responsible for learning, memory, and other abilities. Accurately segmenting the hippocampus and precisely calculating the volume of the hippocampus is of great significance for predicting Alzheimer’s disease and amnesia. Most of the segmentation algorithms currently involved are based on templates, such as the more popular FreeSufer. Methods This study proposes Deephipp, a deep learning network based on a 3D dense block using an attention mechanism for accurate segmentation of the hippocampus. DeepHipp is based on the following novelties: (i) DeepHipp adopts powerful data augmentation schemes to enhance the segmentation ability. (ii) DeepHipp is designed to incorporate 3D dense-block to capture multiple-scale features of the hippocampus. (iii) DeepHipp creatively uses the attention mechanism in the field of hippocampal image segmentation, extracting useful hippocampus information in a massive feature map, and improving the accuracy and sensitivity of the model. Conclusions We describe the illustrative results and show extensive qualitative and quantitative comparisons with other methods. Our achievement demonstrates that the accuracy of DeepHipp can reach 83.63%, which is superior to most existing methods in terms of accuracy and efficiency of hippocampus segmentation. It is noticeable that deep learning can potentially lead to an effective segmentation of medical images.

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Section: Methodsmentioning

confidence: 99%

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

Wang,

Lei,

Zhao

et al. 2023

BMC Med Imaging

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“…31 Coefficients of the obtained details are passed through three different thresholds to decrease the number of coefficients and also to remove non-significant value. 32 Detailed coefficients based on DWT of order (j) can be estimated by the following equation:…”

Section: Dctmentioning

confidence: 99%

“…There are three different specific coefficients; those coefficients are vertical (V), horizontal (H), and diagonal (D) 31 . Coefficients of the obtained details are passed through three different thresholds to decrease the number of coefficients and also to remove non‐significant value 32 . Detailed coefficients based on DWT of order ( j ) can be estimated by the following equation:

W_{ϕ}^{i} false(j, u, v false) = \frac{1}{\sqrt{MN}} \sum_{x = 0}^{M - 1} \sum_{y = 0}^{N - 1} f false(x, y false) ϕ_{j, u, v}^{i} false(x, y false),

Where:…”

Section: Feature Extraction and Fusionmentioning

confidence: 99%

Robust features fusion utilization for supervised palmprint recognition

Ata

Elgamily²,

Mohamed

2022

Concurrency and Computation

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This article proposes robust features fusion methodology for supervised palmprint recognition. The process of features fusion has been formulated according to the hybridization between robust morphological features of principal lines and the following features extractors: principal component analysis (PCA), transformed domain extractors, and invariant moments. The region of interest (ROI) has been extracted based on the identification of the valley points locations. Moreover, both morphological operations and edge detection have been applied in order to formulate the correct ROI. First, both length and slope of each extracted principal line have been estimated morphologically. Second, different features extraction techniques have been appliedfor the resultant ROI including (PCA), transformed domain, and invariant moments.Finally, seven supervised machine learning methods have been manipulated in order to achieve the highest palmprint recognition accuracy. The accuracy of the recognition was evaluated by measuring sensitivity, dice, precision, Jaccard coefficients, correlation, accuracy, and recognition time. Experimental findings show that, among all tested machine learning methods, the feed forward neural network with back propagation based on features vector of the fusion between invariant moments and principal lines morphology has achieved approximately 99.9% of successful palmprints recognition. K E Y W O R D Sdiscrete cosine transform, discrete wavelet transform, invariant moments, principal component analysis, Hough transform, morphological features, transformed domain.

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GPU Computing based fast discrete wavelet transform for l₁-regularized SPIRiT reconstruction

Cited by 2 publications

References 21 publications

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

Robust features fusion utilization for supervised palmprint recognition

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GPU Computing based fast discrete wavelet transform for l1-regularized SPIRiT reconstruction

Cited by 2 publications

References 21 publications

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

Robust features fusion utilization for supervised palmprint recognition

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GPU Computing based fast discrete wavelet transform for l₁-regularized SPIRiT reconstruction