Journal of Digital Imaging

, Volume 24, Issue 1, pp 75–85 | Cite as

Evaluation of Objective Similarity Measures for Selecting Similar Images of Mammographic Lesions

  • Ryohei Nakayama
  • Hiroyuki Abe
  • Junji Shiraishi
  • Kunio Doi


The purpose of this study was to investigate four objective similarity measures as an image retrieval tool for selecting lesions similar to unknown lesions on mammograms. Measures A and B were based on the Euclidean distance in feature space and the psychophysical similarity measure, respectively. Measure C was the sequential combination of B and A, whereas measure D was the sequential combination of A and B. In this study, we selected 100 lesions each for masses and clustered microcalcifications randomly from our database, and we selected five pairs of lesions from 4,950 pairs based on all combinations of the 100 lesions by use of each measure. In two observer studies for 20 mass pairs and 20 calcification pairs, six radiologists compared all combinations of 20 pairs by using a two-alternative forced-choice method to determine the subjective similarity ranking score which was obtained from the frequency with which a pair was considered as more similar than the other 19 pairs. In both mass and calcification pairs, pairs selected by use of measure D had the highest mean value of the average subjective similarity ranking scores. The difference between measures D and A (P = 0.008 and 0.024), as well as that between measures D and B (P = 0.018 and 0.028) were statistically significant for masses and microcalcifications, respectively. The sequential combination of the objective similarity measure based on the Euclidean distance and the psychophysical similarity measure would be useful in the selection of images similar to those of unknown lesions.

Key words

Similarity measure similar image mass clustered microcalcifications mammogram 


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Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Ryohei Nakayama
    • 1
  • Hiroyuki Abe
    • 1
  • Junji Shiraishi
    • 1
  • Kunio Doi
    • 1
  1. 1.Kurt Rossmann Laboratories for Radiologic Image Research, Department of RadiologyThe University of ChicagoChicagoUSA

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