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Related Experiment Video

Updated: May 31, 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

Detecting and classifying linear structures in mammograms using random forests.

Michael Berks1, Zezhi Chen, Sue Astley

  • 1Imaging Science and Biomedical Engineering, School of Cancer and Enabling Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK. michael.berks@manchester.ac.uk

Information Processing in Medical Imaging : Proceedings of the ... Conference
|July 19, 2011
PubMed
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This study introduces a novel method for detecting abnormal curvilinear structures in mammograms using discriminative learning and Dual-Tree Complex Wavelet Transform (DT-CWT). The approach significantly improves the accuracy of identifying normal versus abnormal structures.

Area of Science:

  • Medical image analysis
  • Computer-aided diagnosis
  • Biomedical engineering

Background:

  • Curvilinear structures in mammograms are crucial for detecting abnormalities.
  • Accurate detection and classification of these structures are challenging.

Purpose of the Study:

  • To develop and evaluate a discriminative learning approach for detecting and classifying curvilinear structures in mammograms.
  • To distinguish between normal and abnormal (spicules) structures.

Main Methods:

  • Utilized a Dual-Tree Complex Wavelet (DT-CWT) representation for feature extraction.
  • Employed a random forest classifier for discriminative learning.
  • Conducted quantitative comparisons with existing leading methods and their learning-based variants.

Related Experiment Videos

Last Updated: May 31, 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

Main Results:

  • Achieved an area under the ROC curve (A(z)) of 0.923 for curvilinear structure detection.
  • Attained an A(z) of 0.761 for distinguishing between normal and abnormal structures (spicules).
  • Demonstrated significantly superior performance compared to all other evaluated methods.

Conclusions:

  • The proposed discriminative learning approach combined with DT-CWT significantly enhances the detection and classification of mammographic curvilinear structures.
  • The DT-CWT representation's ability to provide local phase information and good angular resolution contributes to the improved performance.