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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: May 11, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Contour-based brain segmentation method for magnetic resonance imaging human head scans.

K Somasundaram¹, P Kalavathi

¹Department of Computer Science and Applications, Gandhigram Rural Institute-Deemed University, Dindigul, Tamil Nadu, India.

Journal of Computer Assisted Tomography

|May 16, 2013

Summary

This study introduces a novel contour-based method for automatic brain segmentation in MRI scans. The technique accurately extracts brain regions from various weighted MRI images, outperforming existing methods.

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Last Updated: May 11, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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Area of Science:

Medical Imaging
Neuroscience
Computer Vision

Background:

Magnetic resonance imaging (MRI) brain scans often include non-brain tissues, complicating analysis.
Automatic brain tissue segmentation is challenging due to anatomical variations, imaging artifacts, and overlapping signal intensities.

Purpose of the Study:

To develop and validate a robust, contour-based automatic brain segmentation method for T1-, T2-, and proton density-weighted human head MRI scans.
To improve the accuracy and consistency of brain extraction compared to existing techniques.

Main Methods:

A two-stage contour-based approach is proposed.
Stage 1: Extracts brain regions from a middle slice and establishes a landmark circle.
Stage 2: Utilizes the landmark circle to segment brain regions in remaining slices, accommodating multi-component brain structures.

Main Results:

The proposed method accurately segments brain regions across different MRI sequences (T1, T2, proton density) and anatomical variations.
Demonstrated robust performance on normal and abnormal brain images.
Outperformed popular methods like Brain Extraction Tool, Brain Surface Extraction, watershed, and graph cuts in experiments on 100 brain volumes.

Conclusions:

The developed contour-based method offers a reliable and accurate solution for automatic brain segmentation in MRI.
The landmark circle approach effectively handles complex brain anatomies and multi-component slices.
This technique shows superior and consistent performance compared to existing skull-stripping algorithms.