Share Email Print
cover

Proceedings Paper

Determination of subjective and objective similarity for pairs of masses on mammograms for selection of similar images
Author(s): Chisako Muramatsu; Qiang Li; Robert A. Schmidt; Junji Shiraishi; Kenji Suzuki; Gillian M. Newstead; Kunio Doi
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Presentation of images with known pathology similar to that of a new unknown lesion would be helpful for radiologists in their diagnosis of breast cancer. In order to find images that are really similar and useful to radiologists, we determined the radiologists' subjective similarity ratings for pairs of masses, and investigated objective similarity measures that would agree well with the subjective ratings. Fifty sets of images, each of which included one image in the center and six other images to be compared with the center image, were selected; thus, 300 pairs of images were prepared. Ten breast radiologists provided the subjective similarity ratings for each image pair in terms of the overall impression for diagnosis. The objective similarity measures based on cross-correlation of the images, differences in feature values, and psychophysical measures by use of an artificial neural network were determined. The objective measures based on the cross-correlation were found to be not correlated with the subjective similarity ratings (r < 0.1). The differences in the features characterizing the margin were relatively strong indicators of the similarity (r > 0.40). When several image features were used, the differences-based objective measure was moderately correlated (r = 0.59) with the subjective ratings. The relatively high correlation coefficient (r = 0.74) was obtained for the psychophysical similarity measure. The similar images selected by use of the psychophysical measure can be useful to radiologists in the diagnosis of breast cancer.

Paper Details

Date Published: 30 March 2007
PDF: 9 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65141I (30 March 2007); doi: 10.1117/12.713785
Show Author Affiliations
Chisako Muramatsu, The Univ. of Chicago (United States)
Qiang Li, The Univ. of Chicago (United States)
Robert A. Schmidt, The Univ. of Chicago (United States)
Junji Shiraishi, The Univ. of Chicago (United States)
Kenji Suzuki, The Univ. of Chicago (United States)
Gillian M. Newstead, The Univ. of Chicago (United States)
Kunio Doi, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

© SPIE. Terms of Use
Back to Top