Using Grey-Scale Hit-or-Miss Transform for Detection of Isolated Foreground Pixels in Cerebral RMN Datasets

Pana, Lenuta

doi:10.5593/sgem2019v/6.3/s08.030

Cited by 1 publication

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“…This method asks for a limit of agreement of 95% of the data points placed within ± 2SD of the mean difference. The standard deviation SD indicates how accurate is the mean (M) of the kurtosis and skewness, for each brain tissues xi [19][20][21],…”

Section: The Bland Altman Methodsmentioning

confidence: 99%

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3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

Pana

Moldoveanu

Moraru

2021

Ann_UGAL_Math_Phys_Mec

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This paper aims to provide a sound estimation of the true value and proportion of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) of the brain DTI images for a proper 3D volume reconstruction. During the pre-processing stage, two nonlinear filters are operated, i.e. bilateral and anisotropic diffusion. The segmentation of each brain tissue is performed using the k-means clustering algorithm. To minimize filters bias and for obtaining the best reproducible results, a statistical analysis has been performed. Thus, the skewness and kurtosis statistics features were computed for each segmented brain tissue and filter. The fuzzy k-means method allows for clustering analysis and the Bland-Altman analysis investigates the agreement between two filtering techniques of the same statistics feature and brain tissue. Then the 3D reconstruction method is presented using ImageJ and the image stacks for raw and processed data. We conclude that anisotropic diffusion filter offers the best results and 3D reconstruction of brain tissues is feasible.

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Section: The Bland Altman Methodsmentioning

confidence: 99%

“…The Pearson coefficient The Pearson correlation coefficient indicates the relationships between the two methods [19][20][21][22]:…”

Section: The Bland Altman Methodsmentioning

confidence: 99%

3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

Pana

Moldoveanu

Moraru

2021

Ann_UGAL_Math_Phys_Mec

View full text Add to dashboard Cite

show abstract

Using Grey-Scale Hit-or-Miss Transform for Detection of Isolated Foreground Pixels in Cerebral RMN Datasets

Cited by 1 publication

References 0 publications

3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

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