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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...

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Related Experiment Video

Updated: May 23, 2025

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

An automated deep learning framework for brain tumor classification using MRI imagery.

Muhammad Aamir¹, Ziaur Rahman², Uzair Aslam Bhatti³

¹School of Computer Science and Artificial Intelligence, Huanggang Normal University, Huanggang, 438000, Hubei, China. aamirshaikh86@hotmail.com.

Scientific Reports

|May 21, 2025

Summary

This summary is machine-generated.

This study presents an automated method for detecting brain tumors in MRI scans, significantly improving diagnostic accuracy and efficiency. The novel approach enhances image clarity and uses deep learning for precise tumor identification and classification.

Keywords:

Attention mechanism Brain tumor classification (BTC)Convolutional neural network (CNN)Ensemble classifier Healthcare Image enhancement MRI Segmentation Morphological process Multiscale feature extraction

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Last Updated: May 23, 2025

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

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Published on: January 7, 2019

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Accurate and timely brain tumor diagnosis is critical for patient outcomes.
Manual detection of brain tumors in MRI is challenging, time-consuming, and prone to errors.
There is a need for automated, robust, and efficient methods for brain tumor identification.

Purpose of the Study:

To develop an automated method for detecting and classifying brain tumors in MRI images.
To enhance image quality and improve the accuracy of tumor segmentation.
To reduce reliance on manual interpretation in the diagnostic process.

Main Methods:

Image enhancement using guided filtering and anisotropic Gaussian side windows (AGSW).
Morphological analysis for excluding non-tumor regions.
Deep neural networks with an attention module for segmentation and feature extraction.
Ensemble model for multi-class brain tumor classification.

Main Results:

Achieved 99.94% accuracy on the BraTS2020 dataset and 99.67% on the Figshare dataset.
The method demonstrates superior automation and robustness compared to existing techniques.
Successfully identified and classified brain tumors with high precision.

Conclusions:

The proposed automated approach significantly enhances brain tumor diagnosis in MRI.
The integration of advanced image processing and deep learning offers a reliable diagnostic tool.
This method has the potential to improve patient recovery and treatment planning.