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Title: Method and apparatus for detecting a desired behavior in digital image data

Abstract

A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spacially filtered to enforce local consensus among neighboring pixels and the spacially filtered image is output.

Inventors:
 [1]
  1. (11755 Shadow Dr., Dublin, CA 94568)
Issue Date:
Research Org.:
Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
OSTI Identifier:
870973
Patent Number(s):
5633948
Assignee:
Kegelmeyer, Jr., W. Philip (11755 Shadow Dr., Dublin, CA 94568)
DOE Contract Number:  
AC04-76
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
method; apparatus; detecting; desired; behavior; digital; image; data; stellate; lesions; digitized; mammographic; steps; prestoring; plurality; reference; images; calculating; features; pixels; creating; binary; decision; tree; randomly; sampled; created; preferably; including; aloe; feature; analysis; local; oriented; edges; calculated; pixel; input; probability; determined; corresponding; likelihood; presence; lesion; create; finally; spacially; filtered; enforce; consensus; neighboring; output; reference image; reference images; image data; digital image; digitized mammographic; preferably including; stellate lesion; stellate lesions; desired behavior; detecting stellate; mammographic image; /382/

Citation Formats

Kegelmeyer, Jr., W. Philip. Method and apparatus for detecting a desired behavior in digital image data. United States: N. p., 1997. Web.
Kegelmeyer, Jr., W. Philip. Method and apparatus for detecting a desired behavior in digital image data. United States.
Kegelmeyer, Jr., W. Philip. Wed . "Method and apparatus for detecting a desired behavior in digital image data". United States. https://www.osti.gov/servlets/purl/870973.
@article{osti_870973,
title = {Method and apparatus for detecting a desired behavior in digital image data},
author = {Kegelmeyer, Jr., W. Philip},
abstractNote = {A method for detecting stellate lesions in digitized mammographic image data includes the steps of prestoring a plurality of reference images, calculating a plurality of features for each of the pixels of the reference images, and creating a binary decision tree from features of randomly sampled pixels from each of the reference images. Once the binary decision tree has been created, a plurality of features, preferably including an ALOE feature (analysis of local oriented edges), are calculated for each of the pixels of the digitized mammographic data. Each of these plurality of features of each pixel are input into the binary decision tree and a probability is determined, for each of the pixels, corresponding to the likelihood of the presence of a stellate lesion, to create a probability image. Finally, the probability image is spacially filtered to enforce local consensus among neighboring pixels and the spacially filtered image is output.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 00:00:00 EST 1997},
month = {Wed Jan 01 00:00:00 EST 1997}
}

Works referenced in this record:

Automated visual quality evaluation of CVD film
conference, March 1992


Classification of breast tissue by texture analysis
journal, June 1992


Investigation of methods for the computerized detection and analysis of mammographic masses
conference, July 1990


Automatic computer detection of clustered calcifications in digital mammograms
journal, August 1990


An approach to automated detection of tumors in mammograms
journal, January 1990


An iterative growing and pruning algorithm for classification tree design
journal, January 1991

  • Gelfand, S. B.; Ravishankar, C. S.; Delp, E. J.
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, Issue 2
  • https://doi.org/10.1109/34.67645

Computerized quantification of breast duct patterns.
journal, June 1982


Computer-assisted analysis of mammographic clustered calcifications
journal, May 1989


On techniques for detecting circumscribed masses in mammograms
journal, January 1989


Mammogram Inspection by Computer
journal, April 1979


Automated detection of breast tumors using the asymmetry approach
journal, June 1991


Algorithm for the detection of fine clustered calcifications on film mammograms.
journal, November 1988