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

Year 2023, Volume: 4 Issue: 2, 78 - 89, 21.12.2023

Ihab Elaff

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

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

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

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