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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Digital mammogram spiculated mass detection and spicule segmentation using level sets.

John E Ball1, Lori Mann Bruce

  • 1GeoResources Institute, Mississippi State University, Starkville, MS 39759, USA. johnball@msubulldogs.org

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces an automated mammographic computer-aided diagnosis (CAD) system, spiculation segmentation with level sets (SSLS), for detecting spicules in digital mammograms. The SSLS system achieved high accuracy in classifying lesions as benign or malignant.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Accurate detection of spiculated lesions in mammograms is crucial for early breast cancer diagnosis.
  • Existing computer-aided diagnosis (CAD) systems require improvement in spicule segmentation and classification accuracy.

Purpose of the Study:

  • To develop and evaluate an automated system for detecting and segmenting spicules in digital mammograms.
  • To improve the classification accuracy of suspicious masses as benign or malignant.

Main Methods:

  • Developed a novel system termed spiculation segmentation with level sets (SSLS).
  • Utilized adaptive level set segmentation algorithm (ALSSM) for mass periphery segmentation.
  • Employed Dixon and Taylor Line Operator (DTLO) for linear structure enhancement and feature extraction.
  • Classified suspicious masses using 1-NN/2-NN and maximum likelihood classifiers.

Main Results:

  • The initial spiculation detection achieved 100% detection rate with no false positives (Area under ROC curve A(Z)=1.0).
  • SSLS improved the Area under ROC curve to 0.9862 compared to ALSSM alone (A(Z)=0.9687-0.9708).
  • The system achieved up to 93% overall accuracy with 1-NN/2-NN classifiers.

Conclusions:

  • The developed SSLS system demonstrates high efficacy in detecting and segmenting spiculated lesions in digital mammograms.
  • SSLS significantly enhances the diagnostic performance of computer-aided diagnosis systems for breast cancer detection.
  • The automated system shows promise for improving the accuracy and efficiency of mammographic analysis.