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Proceedings Paper

Optimizing feature selection across a multimodality database in computerized classification of breast lesions
Author(s): Karla Horsch; Alfredo Fredy Ceballos; Maryellen Lissak Giger; Ioana R. Bonta; Zhimin Huo; Carl J. Vyborny; Edward R. Hendrick; Li Lan
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Paper Abstract

Linear step-wise feature selection is performed for computerized analysis methods on a set of mammography features using a database of mammography cases, a set of ultrasound features using a database of ultrasound cases, and a set of mammography and sonography features using a multi- modality database of lesions with both mammograms and sonograms. The large mammography and sonography databases were randomly split 20 times into three subdatabases for feature selection, classifier training and independent validation. The average validation Az value over the 20 random splits for the mammography database was 0.82 +/- 0.04 and for the sonography database was 0.85 +/- 0.03. The average consistency feature selection Az value for the mammography and sonography databases were 0.87 +/- 0.02 and 0.88 +/- 0.02, respectively. For the multi-modality database, the consistency feature selection Az value was 0.93.

Paper Details

Date Published: 9 May 2002
PDF: 7 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467053
Show Author Affiliations
Karla Horsch, Univ. of Chicago (United States)
Alfredo Fredy Ceballos, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Ioana R. Bonta, Univ. of Chicago (United States)
Zhimin Huo, Univ. of Chicago (United States)
Carl J. Vyborny, Univ. of Chicago (United States)
Edward R. Hendrick, Northwestern Univ. (United States)
Li Lan, Univ. of Chicago (United States)

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

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