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Proceedings Paper

Improving CAD performance by fusion of the bilateral mammographic tissue asymmetry information
Author(s): Xingwei Wang; Lihua Li; Wei Liu; Weidong Xu; Dror Lederman; Bin Zheng
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Paper Abstract

Bilateral mammographic tissue density asymmetry could be an important factor in assessing risk of developing breast cancer and improving the detection of the suspicious lesions. This study aims to assess whether fusion of the bilateral mammographic density asymmetrical information into a computer-aided detection (CAD) scheme could improve CAD performance in detecting mass-like breast cancers. A testing dataset involving 1352 full-field digital mammograms (FFDM) acquired from 338 cases was used. In this dataset, half (169) cases are positive containing malignant masses and half are negative. Two computerized schemes were first independently applied to process FFDM images of each case. The first single-image based CAD scheme detected suspicious mass regions on each image. The second scheme detected and computed the bilateral mammographic tissue density asymmetry for each case. A fusion method was then applied to combine the output scores of the two schemes. The CAD performance levels using the original CAD-generated detection scores and the new fusion scores were evaluated and compared using a free-response receiver operating characteristic (FROC) type data analysis method. By fusion with the bilateral mammographic density asymmetrical scores, the case-based CAD sensitivity was increased from 79.2% to 84.6% at a false-positive rate of 0.3 per image. CAD also cued more "difficult" masses with lower CAD-generated detection scores while discarded some "easy" cases. The study indicated that fusion between the scores generated by a single-image based CAD scheme and the computed bilateral mammographic density asymmetry scores enabled to increase mass detection sensitivity in particular to detect more subtle masses.

Paper Details

Date Published: 23 February 2012
PDF: 7 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831508 (23 February 2012); doi: 10.1117/12.910531
Show Author Affiliations
Xingwei Wang, Univ. of Pittsburgh Medical Ctr. (United States)
Lihua Li, Hangzhou Dianzi Univ. (China)
Wei Liu, Hangzhou Dianzi Univ. (China)
Weidong Xu, Hangzhou Dianzi Univ. (China)
Dror Lederman, Univ. of Pittsburgh Medical Ctr. (United States)
Bin Zheng, Univ. of Pittsburgh Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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