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Magnetic Resonance Imaging

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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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Related Experiment Video

Updated: May 17, 2026

Optical Sectioning and Visualization of the Intervertebral Disc from Embryonic Development to Degeneration

Optical Sectioning and Visualization of the Intervertebral Disc from Embryonic Development to Degeneration

Published on: July 8, 2021

Intervertebral disc segmentation in MR images using anisotropic oriented flux.

Max W K Law¹, Kengyeow Tay, Andrew Leung

¹GE Healthcare Canada, 268 Grosvenor Street, London, Ontario N6A 4V2, Canada. max.w.k.law@gmail.com

Medical Image Analysis

|October 31, 2012

Summary

This study introduces an unsupervised system for segmenting intervertebral discs from spine MR scans. The novel method accurately identifies discs despite variations, improving segmentation accuracy.

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Area of Science:

Medical Imaging
Biomedical Engineering
Radiology

Background:

Accurate intervertebral disc segmentation is crucial for diagnosing spinal conditions.
Existing segmentation methods often struggle with variations in disc shape, intensity, and ambiguous boundaries.

Purpose of the Study:

To develop an unsupervised system for accurate intervertebral disc segmentation using middle sagittal spine MR scans.
To address challenges posed by similar neighboring structures and variable disc characteristics.

Main Methods:

Utilizes a novel anisotropic oriented flux detection scheme for enhanced disc boundary recognition.
Employs vertebral body tracking for initial disc position and orientation inference.
Applies a level set-based active contour model to minimize an energy functional for segmentation.

Main Results:

The system demonstrates accurate segmentation of intervertebral discs.
Evaluated on a database of 455 intervertebral discs from 69 middle sagittal slices.
Successfully handles shape and intensity variations, distinguishing discs from adjacent tissues.

Conclusions:

The proposed unsupervised system provides accurate intervertebral disc segmentation.
Offers a robust solution for analyzing spinal MR images.
Has potential for clinical applications in spinal disorder assessment.