Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection

Abstract

The goal of this project is to develop a computer-aided diagnosis (CAD) system for interval change analysis of lesions on mammograms. An important component of the CAD system is the multistage regional registration technique for identifying corresponding microcalcification clusters on temporal pairs of mammograms. In the first stage, an initial search region was estimated on the prior mammogram based on the lesion location on the current mammogram. In the second stage the search region was refined. In the third stage the lesion was detected within the search region. In the first stage we compared the regional registration method (RRM) to the use of linear and nonlinear warping techniques for the initial estimation of the lesion location. 390 temporal pairs of mammograms were used for evaluation. The average distance between the estimated and the true lesion centroids on the previous mammogram after the initial stage was 8.5%+/- 6.2mm.2mm for RRM and 9.0 +/- 6.7mm for the best of the warping techniques. The RRM method outperformed the warping techniques. In the second step, automated detection of microcalcification cluster within the search region is performed. Using our current cluster detection program with standard thresholds, 69.4% (50/72)TP with 0.21 false positives (FP) were detected within the search region. Using a high-sensitivity threshold, 84.7% (61/72) TP with 0.75 FP were detected.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2003
Accession Number
ADA420484

Entities

People

  • Lubomir Hadjiiski

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Breast Cancer
  • Computer Vision
  • Computers
  • Data Sets
  • Databases
  • Detection
  • Diagnostic Imaging
  • Feature Extraction
  • Feature Selection
  • Identification
  • Image Processing
  • Information Science
  • Machine Learning
  • Neoplasms
  • North America
  • Pattern Recognition

Fields of Study

  • Medicine
  • Physics

Readers

  • Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.