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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...

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Combining two mammographic projections in a computer aided mass detection method.

Saskia van Engeland1, Nico Karssemeijer

  • 1Department of Radiology, Radboud University Medical Centre Nijmegen, Geert Grooteplein Zuid 18, Nijmegen 6525 GA, The Netherlands.

Medical Physics
|April 19, 2007
PubMed
Summary

This study enhances mammogram analysis by fusing data from two breast views, improving computer-aided detection (CAD) for masses. The new method boosts lesion sensitivity while maintaining a low false positive rate.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Breast Cancer Detection

Background:

  • Mammography is crucial for early breast cancer detection.
  • Computer-aided detection (CAD) systems aim to improve mammogram interpretation accuracy.
  • Current CAD systems often analyze single mammographic views, potentially missing subtle findings.

Purpose of the Study:

  • To enhance computer-aided detection (CAD) performance for mammographic masses.
  • To develop a novel CAD approach by fusing information from two standard mammographic views (mediolateral oblique and craniocaudal).
  • To evaluate the effectiveness of a two-view fusion CAD system compared to single-view analysis.

Main Methods:

  • A cascaded multiple-classifier system was developed, integrating information from linked suspicious regions in MLO and CC views.
  • The system computes the suspiciousness of a region based on its correspondence and similarity to a candidate region in the contralateral view.
  • Performance was evaluated using free-response receiver operating characteristic (FROC) analysis and cross-validation on a dataset of 948 four-view mammograms.

Main Results:

  • The two-view CAD system demonstrated a statistically significant improvement in lesion-based detection performance.
  • At a false positive rate of 0.1 FP/image, lesion sensitivity increased from 56% to 61% compared to single-view detection.
  • Case-based sensitivity, however, did not show significant improvement.

Conclusions:

  • Fusing information from two mammographic views significantly improves lesion detection sensitivity in CAD systems.
  • The developed cascaded classifier approach offers a promising method for enhancing mammographic mass detection accuracy.
  • Further research may be needed to improve case-based sensitivity and address other mammographic findings.