Date of Award
12-1995
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Department of Electrical and Computer Engineering
First Advisor
Steven K. Rogers, PhD
Abstract
A comparative study of texture measures for the classification of breast tissue is presented. The texture features investigated include Angular Second Moments, Power Spectrum Analysis and a novel feature, Laws Energy Ratios. The texture study was accomplished as part of the development of a Model Based Vision (MBV) system for the automatic detection of microcalcifications. An overview of the Microcalcification Detection System is presented, which applies image differencing techniques, feature selection methods, and neural networks for locating microcalcification clusters in mammograms. The Power Spectrum Analysis feature set had the best overall performance with an 83% Probability of Detection and an average False ROl Rate of 2.17 ROIs per image over 53 mammograms. A combination of Laws Energy Ratio and Power Spectrum Analysis features selected using Ruck Saliency metrics achieved an increased Probability of Detection of 85% with an average 4 false ROIs per image.
AFIT Designator
AFIT-GEO-ENG-95D-01
DTIC Accession Number
ADA306443
Recommended Citation
Dauk, Ronald C., "Computer Aided Detection of Microcalcifications Utilizing Texture Analysis" (1995). Theses and Dissertations. 6209.
https://scholar.afit.edu/etd/6209