Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Hybrid Deep Learning Approach for Performance Prediction in Optical Communication Systems Based on PON Scenarios.

Sensors (Basel, Switzerland)·2026

Same author

Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review.

Journal of cheminformatics·2026

Same author

Scalable Unimodal and Multimodal Deep Learning for Multi-Label Chest Disease Detection: A Comparative Analysis.

Diagnostics (Basel, Switzerland)·2026

Same author

A lightweight transformer-based hybrid encoder-decoder model for chest X-ray medical report generation.

Scientific reports·2026

Same author

Identification of Potential Biomarkers in Prostate Cancer Microarray Gene Expression Leveraging Explainable Machine Learning Classifiers.

Cancers·2025

Same author

Eye-XAI: an explainable artificial intelligence approach for eye disease detection using symptom analysis.

BMC medical informatics and decision making·2025

Search research articles

Related Experiment Video

Updated: Jul 5, 2026

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

Published on: May 2, 2014

A survey on brain tumor image analysis.

Kashfia Sailunaz¹, Sleiman Alhajj², Tansel Özyer³

¹Department of Computer Science, University of Calgary, Alberta, Canada.

Medical & Biological Engineering & Computing

|September 12, 2023

Summary

This review covers brain tumor image analysis, detailing methods from traditional image processing to deep learning. It highlights challenges and future research in diagnosing brain tumors from medical imaging like MRI and CT scans.

Keywords:

Brain tumor Deep learning MRI Machine learning Medical image analysis Tumor detection Tumor features Tumor segmentation

More Related Videos

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

Related Experiment Videos

Last Updated: Jul 5, 2026

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

In Vivo Optical Imaging of Brain Tumors and Arthritis Using Fluorescent SapC-DOPS Nanovesicles

Published on: May 2, 2014

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

Area of Science:

Medical imaging and radiology.
Advanced image processing techniques.

Background:

Medical imaging is crucial for diagnosing injuries and diseases non-invasively.
Brain image analysis, particularly anomaly detection, is a significant research area due to the brain's complexity.
Brain tumors present diagnostic challenges due to varied characteristics.

Purpose of the Study:

To provide a comprehensive review of brain tumor image analysis.
To discuss various analysis models, features, and performance metrics.
To highlight challenges and future research directions in the field.

Main Methods:

Review of image processing methods including filtering, thresholding, and graph models.
Inclusion of machine learning (ML) and deep learning (DL) models.
Analysis of brain tumor image features and performance evaluation metrics.

Main Results:

Detailed overview of brain tumor characteristics and imaging modalities (MRI, CT, PET).
Exploration of diverse analytical approaches, from classical methods to modern AI.
Identification of key challenges in accurate brain tumor detection and analysis.

Conclusions:

Brain tumor image analysis is complex but advancing with ML and DL.
Further research is needed to address current challenges in automated diagnosis.
This review serves as a guide to the current landscape and future of brain tumor imaging analysis.