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Updated: Jun 17, 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

A Scalable Framework For Segmenting Magnetic Resonance Images.

Prodip Hore¹, Lawrence O Hall, Dmitry B Goldgof

¹Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA.

Journal of Signal Processing Systems

|January 5, 2010

Summary

This summary is machine-generated.

Related Concept Videos

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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A new method for segmenting magnetic resonance images (MRIs) of the human brain offers fast, accurate, and automatic results. This scalable approach significantly improves processing speed for large datasets compared to traditional methods.

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Biology

Background:

Accurate segmentation of magnetic resonance images (MRIs) is crucial for understanding brain structure and function.
Existing segmentation methods can be computationally intensive and may not scale well for high-resolution or large datasets.
The fuzzy c-means (FCM) algorithm is a powerful clustering technique but can be slow for large-scale applications.

Purpose of the Study:

To introduce a fast, accurate, and fully automatic method for segmenting human brain MRIs.
To develop scalable modifications of the fuzzy c-means algorithm for efficient processing of large neuroimaging datasets.
To evaluate the performance of the proposed segmentation framework against established software.

Main Methods:

Related Experiment Videos

Last Updated: Jun 17, 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

Modifications of the fuzzy c-means (FCM) algorithm were developed to create incremental versions, enabling processing of data in subsets.

These incremental FCM algorithms were integrated into a framework incorporating inhomogeneity correction and smoothing for MRI segmentation.

The framework was tested on a diverse set of normal human brain MRIs acquired across different scanners and parameters.

Main Results:

The incremental fuzzy c-means approach demonstrated significant speed-up compared to standard FCM for medium to extremely large datasets.
Segmentation quality was comparable to applying the standard FCM algorithm to the entire dataset.
Results were comparable to widely used software like Statistical Parametric Mapping and FMRIB Software Library (FSL), with improved scalability.

Conclusions:

The proposed method provides a fast, accurate, and scalable solution for automatic human brain MRI segmentation.
This approach offers a viable alternative to existing methods, particularly for large-scale neuroimaging studies.
The framework's ability to handle data from various acquisition settings enhances its generalizability.