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Proceedings Paper

Use of joint two-view information for computerized lesion detection on mammograms: improvement of microcalcification detection accuracy
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Paper Abstract

We are developing new techniques to improve the accuracy of computerized microcalcification detection by using the joint two-view information on craniocaudal (CC) and mediolateral-oblique (MLO) views. After cluster candidates were detected using a single-view detection technique, candidates on CC and MLO views were paired using their radial distances from the nipple. Object pairs were classified with a joint two-view classifier that used the similarity of objects in a pair. Each cluster candidate was also classified as a true microcalcification cluster or a false-positive (FP) using its single-view features. The outputs of these two classifiers were fused. A data set of 38 pairs of mammograms from our database was used to train the new detection technique. The independent test set consisted of 77 pairs of mammograms from the University of South Florida public database. At a per-film sensitivity of 70%, the FP rates were 0.17 and 0.27 with the fusion and single-view detection methods, respectively. Our results indicate that correspondence of cluster candidates on two different views provides valuable additional information for distinguishing false from true microcalcification clusters.

Paper Details

Date Published: 9 May 2002
PDF: 8 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467220
Show Author Affiliations
Berkman Sahiner, Univ. of Michigan (United States)
Metin Nafi Gurcan, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Nicholas Petrick, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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