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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...

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: Aug 5, 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

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique.

Saravanan Srinivasan¹, Prabin Selvestar Mercy Bai², Sandeep Kumar Mathivanan³

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India.

Diagnostics (Basel, Switzerland)

|March 29, 2023

Summary

This summary is machine-generated.

A new automated method using a convolutional recurrent neural network (CRNN) accurately detects and classifies brain tumors from MRI scans. This advanced technique significantly improves diagnostic accuracy for glioma identification compared to existing methods.

Keywords:

convolution recurrent neural network enhanced fuzzy c-means clustering image classification local-binary grey level co-occurrence matrix magnetic resonance image

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Image Analysis
Artificial Intelligence in Medicine
Neurology

Background:

Accurate brain tumor identification is crucial for effective treatment.
Automated diagnostic methods are needed to improve accuracy and efficiency.
Brain image segmentation is a complex but vital step in medical image processing.

Purpose of the Study:

To propose a novel automated method for brain tumor detection and classification.
To enhance the accuracy of identifying glioma tumors in MRI scans.
To develop a reliable deep learning model for medical image analysis.

Main Methods:

Utilized an adaptive filter for MRI image pre-processing to reduce noise.
Employed enhanced fuzzy c-means clustering (EFCMC) for image segmentation.
Applied local-binary grey level co-occurrence matrix (LBGLCM) for feature extraction.
Developed a convolutional recurrent neural network (CRNN) for image classification.

Main Results:

The proposed CRNN method achieved 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.
The system demonstrated superior performance compared to traditional methods like BP, U-Net, and ResNet.
The method effectively classified MRI scans into glioma and normal categories using the REMBRANDT dataset.

Conclusions:

The novel automated detection and classification method shows high potential for clinical application.
The CRNN model offers a significant advancement in automated brain tumor diagnosis.
This approach provides a reliable and accurate tool for identifying brain tumors, aiding oncologists and radiologists.