Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)

Ihab Elaff

doi:10.53525/jster.1250050

TR EN

Medical image enhancement based on volumetric tissue segmentation fusion (Uni-stable 3D method)

Öz

The 3D Uni-stable is a new method for 3D medical image enhancement which produces 3D Images of high contrast from the scanned anisotropic scaling images. This is done by estimating some intermediate slices through resizing the original scans. Rescaling has been achieved at three different levels: rescaling of eigenvalues of diffusion, rescaling the Scalar Indexes from the original eigenvalues, and rescaling the cluster maps of the segmentation of the original Scalar Indexes. Four interpolation methods have been employed at each level and four clustering algorithms have been employed in the process. The 3D Uni-stable image is almost universal as it combines variety of algorithms points of views into one 3D probability map. This reduces boundary-overlapping among different tissues, and hence improves the uniqueness of the segmentation problem solution. The stability factor of the 3D Uni-stable-Images is measured by maximum match analysis between the cluster maps which are generated from 3D Uni-stable images using variety of clustering methods with respect to true fact references for 5 different brains and the resultant standard deviations of Uni-stable images maximum match analysis in both threshold and tissue to brain ratio are much lower than Mean Diffusivity and Fractional Anisotropy scalar indexes for both CSF/non-CSF and WM/non-WM respectively

Anahtar Kelimeler

Uni-stable , WM , GM , CSF , Brain Segmentation

Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)

Abstract

The 3D Uni-stable is a new method for 3D medical image enhancement which produces 3D Images of high contrast from the scanned anisotropic scaling images. This is done by estimating some intermediate slices through resizing the original scans. Rescaling has been achieved at three different levels: rescaling of eigenvalues of diffusion, rescaling the Scalar Indexes from the original eigenvalues, and rescaling the cluster maps of the segmentation of the original Scalar Indexes. Four interpolation methods have been employed at each level and four clustering algorithms have been employed in the process. The 3D Uni-stable image is almost universal as it combines variety of algorithms points of views into one 3D probability map. This reduces boundary-overlapping among different tissues, and hence improves the uniqueness of the segmentation problem solution. The stability factor of the 3D Uni-stable-Images is measured by maximum match analysis between the cluster maps which are generated from 3D Uni-stable images using variety of clustering methods with respect to true fact references for 5 different brains and the resultant standard deviations of Uni-stable images maximum match analysis in both threshold and tissue to brain ratio are much lower than Mean Diffusivity and Fractional Anisotropy scalar indexes for both CSF/non-CSF and WM/non-WM respectively

Keywords

Uni-stable , WM , GM , CSF , Brain Segmentation

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yazarlar

Ihab Elaff ^*
0000-0002-0913-5476
Türkiye

Yayımlanma Tarihi

21 Aralık 2023

Gönderilme Tarihi

11 Şubat 2023

Kabul Tarihi

6 Haziran 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 4 Sayı: 2

DOI

https://doi.org/10.53525/jster.1250050

IZ

https://izlik.org/JA39XE44CU

APA

Elaff, I. (2023). Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research, 4(2), 78-89. https://doi.org/10.53525/jster.1250050

AMA

1.Elaff I. Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research. 2023;4(2):78-89. doi:10.53525/jster.1250050

Chicago

Elaff, Ihab. 2023. “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”. Journal of Science, Technology and Engineering Research 4 (2): 78-89. https://doi.org/10.53525/jster.1250050.

EndNote

Elaff I (01 Aralık 2023) Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research 4 2 78–89.

IEEE

[1]I. Elaff, “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”, Journal of Science, Technology and Engineering Research, c. 4, sy 2, ss. 78–89, Ara. 2023, doi: 10.53525/jster.1250050.

ISNAD

Elaff, Ihab. “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”. Journal of Science, Technology and Engineering Research 4/2 (01 Aralık 2023): 78-89. https://doi.org/10.53525/jster.1250050.

JAMA

1.Elaff I. Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research. 2023;4:78–89.

MLA

Elaff, Ihab. “Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)”. Journal of Science, Technology and Engineering Research, c. 4, sy 2, Aralık 2023, ss. 78-89, doi:10.53525/jster.1250050.

Vancouver

1.Ihab Elaff. Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method). Journal of Science, Technology and Engineering Research. 01 Aralık 2023;4(2):78-89. doi:10.53525/jster.1250050

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Medical image enhancement based on volumetric tissue segmentation fusion (Uni-stable 3D method)

Öz

Anahtar Kelimeler

Medical Image Enhancement Based on Volumetric Tissue Segmentation Fusion (Uni-Stable 3D Method)

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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