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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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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 structure segmentation from MRI by geometric surface flow.

Greg Heckenberg¹, Yongjian Xi, Ye Duan

¹Department of Computer Science, College of Engineering, University of Missouri-Columbia, Columbia 65211-2060, USA.

International Journal of Biomedical Imaging

|November 21, 2012

Summary

This summary is machine-generated.

This study introduces a new semiautomatic method for segmenting brain structures like the thalamus in MRI scans. The technique uses geometric surface flow and variational analysis for robust and accurate segmentation, even with noisy data.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Medical Imaging
Computational Anatomy
Neuroscience

Background:

Accurate segmentation of brain structures, such as the thalamus, is crucial for neurological research and clinical diagnosis.
Existing segmentation methods often struggle with low image contrast, noise, and inhomogeneity in MRI data.

Purpose of the Study:

To develop a robust semiautomatic method for segmenting brain structures from MRI images.
To improve segmentation accuracy by addressing challenges like low contrast and image artifacts.

Main Methods:

A semiautomatic segmentation approach based on geometric surface flow and variational analysis.
Interactive initialization of a seed model within the region of interest.
A nonparametric kernel-based method to update interior probability distribution for low-contrast images.

Main Results:

The method demonstrates robustness to image noise and inhomogeneity.
The segmentation model effectively handles spurious edge gaps, preventing leakage.
Successful application on both 2D and 3D MRI image data.

Conclusions:

The proposed geometric surface flow method offers a reliable approach for semiautomatic brain structure segmentation.
This technique enhances the accuracy and reliability of thalamus segmentation in MRI.
The method provides a valuable tool for neuroimaging analysis.