Wavelet Representation for Digital Mammography.

Abstract

This report describes significant progress in the development of a methodology for accomplishing adaptive contrast enhancement by multiscale representations. Our studies have demonstrated that features extracted from multiresolution representations can provide an adaptive mechanism for the local emphasis of salient and subtle features of importance to mammography. We show that subtle features characteristic of mammographic findings required a finer parameterization of scale space than provided by traditional methods of wavelet analysis carried out at dyadic scales. The improved contrast of mammographic features make these techniques appealing for computed aided diagnosis (CAD) and screening mammography. Screening mammography examinations are certain to grow substantially in the next few years, and analytic methods that can assist general radiologists in reading mammograms shall be of great importance.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA314289

Entities

People

  • Andrew F. Laine

Organizations

  • University of Florida

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Cancer
  • Computer Vision
  • Computers
  • Detection
  • Detectors
  • Digital Images
  • Electrical Engineering
  • Feature Extraction
  • Filtration
  • Frequency Bands
  • Frequency Domain
  • Health Services
  • Image Processing
  • Medical Personnel
  • Noise Reduction
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Computational Fluid Dynamics (CFD)
  • Image Processing and Computer Vision.
  • Medical Imaging.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space