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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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Updated: Mar 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

MRI Image Processing Based on Fractal Analysis

Mariya Y Marusina¹, Alexandra P Mochalina, Ekaterina P Frolova

¹ITMO University, Saint Petersburg, Russia.

Asian Pacific Journal of Cancer Prevention : APJCP

|February 28, 2017

Summary

This summary is machine-generated.

Fractal analysis of liver magnetic resonance images can detect colorectal cancer metastases. The Hurst exponent, a fractal feature, distinguishes healthy tissue from cancerous lesions, aiding early diagnosis.

Keywords:

Computer aided diagnosis cancer fractal analysis data set screening machine learning

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Last Updated: Mar 7, 2026

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Area of Science:

Medical imaging analysis
Oncology
Biophysics

Background:

Cancer is a leading cause of mortality globally.
Early cancer detection through screening improves patient outcomes.
Fractal analysis shows potential for medical image interpretation.

Purpose of the Study:

To investigate the utility of fractal analysis in detecting colorectal cancer liver metastases.
To evaluate the Hurst exponent and fractal dimension as diagnostic features.

Main Methods:

Magnetic resonance (MR) images of healthy and cancerous livers were analyzed.
ImageJ software with FracLac was used for fractal analysis.
The box-counting method calculated fractal dimensions and Hurst exponents.

Main Results:

Healthy liver tissue exhibited a Hurst exponent greater than 0.5 (stable characteristics).
Pathological liver tissue (metastases) showed a Hurst exponent less than 0.5 (unstable characteristics).

Conclusions:

Fractal analysis can rapidly detect focal liver lesions.
The Hurst exponent serves as a valuable diagnostic marker for medical image analysis in oncology.