Clustered Microcalcification Detection Using Optimized Difference of Gaussians.

Abstract

The objective of this thesis is to design an automated microcalcification detection system to be used as an aid in radiologic mammogram interpretation. This research proposes the following methodology for clustered microcalcification detection. First, preprocess the digitized film mammogram to reduce digitization noise. Second, spatially filter the image with a difference of Gaussians (DoG) kernel. To detect potential microcalcifications, segment the filtered image using global and local thresholding. Next, cluster and index these detections into regions of interest (ROIs). Identify ROIs on the digitized image (or hardcopy printout) for final radiologic diagnosis.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1996
Accession Number
ADA325042

Entities

People

  • Edward M. Ochoa

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Detection

Fields of Study

  • Medicine
  • Physics

Readers

  • Emergency Management and Homeland Security.
  • Image Processing and Computer Vision.
  • Regression Analysis.