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Title: Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms

Abstract

The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a ''gold standard'' of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural networkmore » (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists' similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists' impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features was selected. The correlation coefficient between the gold standard and the psychophysical similarity measure through the use of seven features was relatively high (r=0.71) and was comparable to the correlation coefficients between the ratings by one radiologist and the average ratings by nine radiologists (r=0.69{+-}0.07). The correlation coefficient was improved compared to that of a distance-based method (r=0.58). The result indicated that similar images selected by the psychophysical similarity measure may be useful to radiologists in the diagnosis of clustered microcalcifications on mammograms.« less

Authors:
; ; ; ;  [1]
  1. Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637 (United States) and Department of Intelligent Image Information, Gifu University, 1-1 Yanagido, Gifu (Japan)
Publication Date:
OSTI Identifier:
22095290
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 35; Journal Issue: 12; Other Information: (c) 2008 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 97 MATHEMATICAL METHODS AND COMPUTING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMEDICAL RADIOGRAPHY; CORRELATIONS; DIAGNOSIS; FLORIDA; IMAGE PROCESSING; IMAGES; MAMMARY GLANDS; NEOPLASMS; TRAINING

Citation Formats

Muramatsu, Chisako, Qiang, Li, Schmidt, Robert, Shiraishi, Junji, Doi, Kunio, Department of Radiology, Duke Advanced Imaging Labs, Duke University, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, and Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637. Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms. United States: N. p., 2008. Web. doi:10.1118/1.3020760.
Muramatsu, Chisako, Qiang, Li, Schmidt, Robert, Shiraishi, Junji, Doi, Kunio, Department of Radiology, Duke Advanced Imaging Labs, Duke University, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, & Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637. Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms. United States. doi:10.1118/1.3020760.
Muramatsu, Chisako, Qiang, Li, Schmidt, Robert, Shiraishi, Junji, Doi, Kunio, Department of Radiology, Duke Advanced Imaging Labs, Duke University, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705, and Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637. Mon . "Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms". United States. doi:10.1118/1.3020760.
@article{osti_22095290,
title = {Investigation of psychophysical similarity measures for selection of similar images in the diagnosis of clustered microcalcifications on mammograms},
author = {Muramatsu, Chisako and Qiang, Li and Schmidt, Robert and Shiraishi, Junji and Doi, Kunio and Department of Radiology, Duke Advanced Imaging Labs, Duke University, 2424 Erwin Road, Suite 302, Durham, North Carolina 27705 and Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, Illinois 60637},
abstractNote = {The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a ''gold standard'' of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural network (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists' similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists' impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features was selected. The correlation coefficient between the gold standard and the psychophysical similarity measure through the use of seven features was relatively high (r=0.71) and was comparable to the correlation coefficients between the ratings by one radiologist and the average ratings by nine radiologists (r=0.69{+-}0.07). The correlation coefficient was improved compared to that of a distance-based method (r=0.58). The result indicated that similar images selected by the psychophysical similarity measure may be useful to radiologists in the diagnosis of clustered microcalcifications on mammograms.},
doi = {10.1118/1.3020760},
journal = {Medical Physics},
issn = {0094-2405},
number = 12,
volume = 35,
place = {United States},
year = {2008},
month = {12}
}