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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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Related Experiment Video

Updated: Jun 18, 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

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

Rasool Reddy Kamireddy¹, Rajesh N V P S Kandala², Ravindra Dhuli²

¹Department of ECE, NRI Institute of Technology (Autonomous), Vijayawada, India.

|August 1, 2024

Summary

This study introduces a novel method for brain tumor detection using enhanced Magnetic Resonance (MR) images and Convolutional Neural Networks (CNNs). The approach achieves high accuracy in identifying tumors, improving automated disease diagnosis.

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Last Updated: Jun 18, 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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

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

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Brain tumor detection from Magnetic Resonance (MR) images is challenging due to variations in tumor characteristics.
Accurate detection is crucial for clinical applications and automated disease diagnosis.
Existing methods struggle with the complexity of brain structures and tumor heterogeneity.

Purpose of the Study:

To develop and evaluate a novel method for accurate brain tumor detection using MR imaging.
To combine multi-level thresholding and Convolutional Neural Networks (CNNs) for enhanced detection.
To improve automated diagnosis of brain tumors.

Main Methods:

Image contrast enhancement using intensity transformations.
Classification of MR images into normal and abnormal categories using a CNN architecture.
Tumor region detection via multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE).
Refinement of tumor segmentation using morphological operations.

Main Results:

Achieved 99.5% classification accuracy for brain MR images.
Obtained a 92.84% Dice Similarity Coefficient for tumor region detection.
Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

The proposed method effectively detects brain tumors from MR images.
The combination of CNNs and multi-level thresholding offers a promising approach for automated brain tumor diagnosis.
This technique has the potential to significantly aid clinical applications.