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Proceedings Paper

Unitary ranking in the automated detection of mammographic masses
Author(s): Nicholas Petrick; Heang-Ping Chan; Berkman Sahiner; Mark A. Helvie M.D.; Mitchell M. Goodsitt
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Paper Abstract

We are investigating the utility of a unitary ranking method for the classification of masses and false-positives (FPs) in an automated detection algorithm. In unitary ranking, the scores within individual images are ordered from maximum to minimum. A threshold is then applied to this ordering, or ranking, to determine a final set of potential mass regions. A more commonly used approach is to rank the scores from all the images together and then apply a single threshold to the entire group. This method will be referred to as group ranking. In this study, we compared the free-response receiver operating characteristic (FROC) performance of unitary ranking with group ranking. The results are based on the classification of mammographic regions automatically extracted from 255 digitized mammograms. They indicate that unitary ranking reduces the number of false positive (FP) detections over the group ranking method. In particular, unitary ranking for a 95% true positive detection fraction reduced the FPs by 1% (from 10.1 FPs per image to 10.0), 11% (from 6.3 to 5.6) and 26% (from 3.1 to 2.3) for sets of regions having FP to true positive (TP) ratios of 24:1, 16:1 and 8:1, respectively. This preliminary study indicates that the unitary ranking may be a useful scoring technique in the classification of regions on digital mammograms.

Paper Details

Date Published: 25 April 1997
PDF: 9 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274139
Show Author Affiliations
Nicholas Petrick, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Mark A. Helvie M.D., Univ. of Michigan (United States)
Mitchell M. Goodsitt, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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