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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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Related Experiment Video

Updated: May 23, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Characterizing mammographic images by using generic texture features.

Lothar Häberle1, Florian Wagner, Peter A Fasching

  • 1University Breast Center for Franconia, Erlangen-Nuremberg Comprehensive Cancer Center, Erlangen University Hospital, Department of Gynecology and Obstetrics, Universitaetsstrasse 21-23, 91054 Erlangen, Germany.

Breast Cancer Research : BCR
|April 12, 2012
PubMed
Summary
This summary is machine-generated.

Automated mammogram texture analysis shows promise for predicting breast cancer risk. These texture features were found to be as effective as percentage mammographic density (PMD) and did not require additional value from PMD.

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Last Updated: May 23, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Published on: August 30, 2013

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Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Mammographic density is a known breast cancer risk factor.
  • Clinical use is limited by a lack of automated, standardized measurement methods.

Purpose of the Study:

  • Evaluate automated texture features in mammograms as breast cancer risk factors.
  • Compare texture features with percentage mammographic density (PMD).

Main Methods:

  • Case-control study (864 cases, 418 controls).
  • Analyzed 470 automated texture features using logistic regression.
  • Assessed and included PMD in the regression model.

Main Results:

  • 46 texture features remained in the final model.
  • Achieved an area under the curve of 0.79.
  • PMD did not improve the prediction model's accuracy.

Conclusions:

  • Automated texture features show feasibility for breast cancer risk prediction.
  • PMD provided no additional predictive value in this study.
  • Further investigation in larger studies is needed to confirm the predictive accuracy of these texture features.