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Updated: Feb 20, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Brain segmentation in MR images using a texture-based classifier associated with mathematical morphology.

Herng-Hua Chang, Chih-Chung Hsieh

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 25, 2017

Summary

This summary is machine-generated.

This study introduces a new hybrid algorithm for accurate brain magnetic resonance imaging (MRI) skull stripping. The method effectively segments brain tissue, outperforming existing techniques in preliminary tests.

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Area of Science:

Medical Image Analysis
Neuroimaging
Computer Vision

Background:

Skull stripping, the segmentation of brain tissue from non-brain tissue in MRI, is crucial for medical image analysis but remains challenging.
Difficulties arise from complex human brain structures and variability in MRI acquisition parameters.

Purpose of the Study:

To develop a novel hybrid skull stripping algorithm for improved accuracy in brain MRI segmentation.
To address the limitations of existing skull stripping methods.

Main Methods:

A hybrid approach combining texture feature analysis, Fuzzy Possibilistic C-Means (FPCM) clustering, and morphological operations.
Texture feature maps were generated from input MR images, followed by FPCM for initial brain/non-brain masking.
Morphological operations were applied to refine the brain extraction.

Main Results:

The proposed hybrid algorithm demonstrated high accuracy in skull stripping.
Preliminary evaluations on the Internet Brain Segmentation Repository (IBSR) datasets showed superior performance compared to two established methods.

Conclusions:

The developed hybrid skull stripping framework shows significant potential for various brain MRI segmentation applications.
The method offers an effective solution for accurate brain tissue segmentation.