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Brain Imaging

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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.
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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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An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

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Published on: September 24, 2017

Knowledge-based interpretation of MR brain images.

M Sonka¹, S K Tadikonda, S M Collins

¹Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA.

IEEE Transactions on Medical Imaging

|January 1, 1996

Summary

This summary is machine-generated.

A novel genetic algorithm (GA) method fully automates neuroanatomic structure segmentation and labeling in MR brain images. This validated approach accurately identifies structures, offering a significant advancement in automated image interpretation.

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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Published on: May 10, 2012

Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Accurate segmentation and labeling of neuroanatomic structures in magnetic resonance (MR) brain images are crucial for neurological research and clinical applications.
Manual segmentation is time-consuming, subjective, and prone to inter-observer variability.
Automated methods are needed to improve efficiency and consistency in brain image analysis.

Purpose of the Study:

To develop and validate a fully automated method for segmenting and labeling 17 key neuroanatomic structures in MR brain images.
To utilize a genetic algorithm (GA) optimization technique for hypothesis generation and verification in image interpretation.
To assess the accuracy and reliability of the proposed automated method compared to observer-defined standards.

Main Methods:

A hypothesize-and-verify approach was employed, incorporating a genetic algorithm (GA) for image interpretation.
The method was trained on 20 T1-weighted MR images using observer-defined contours as prior knowledge.
Performance validation was conducted on eight independent MR images, comparing automated results to observer-defined standards.

Main Results:

The GA-based method achieved accurate interpretation of all neuroanatomic structures in the test set.
Computer-identified and observer-defined structure areas showed a strong correlation (r=0.99).
A low root mean square (rms) border positioning error of 1.5+/-0.6 pixels was observed.

Conclusions:

The developed GA-based image interpretation method offers a novel and accurate approach to automated neuroanatomic structure labeling in MR brain images.
The method demonstrates high accuracy and reliability, comparable to observer-defined standards.
This automated technique has the potential to significantly advance the field of medical image analysis and neuroimaging research.