Optimizing feature selection across a multimodality database in computerized classification of breast lesions

Karla Horsch; Alfredo Fredy Ceballos; Maryellen Lissak Giger; Ioana R. Bonta; Zhimin Huo; Carl J. Vyborny; Edward R. Hendrick; Li Lan

doi:10.1117/12.467053

9 May 2002 Optimizing feature selection across a multimodality database in computerized classification of breast lesions

Karla Horsch, Alfredo Fredy Ceballos, Maryellen Lissak Giger, Ioana R. Bonta, Zhimin Huo, Carl J. Vyborny, Edward R. Hendrick, Li Lan

Author Affiliations +

Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002) https://doi.org/10.1117/12.467053
Event: Medical Imaging 2002, 2002, San Diego, California, United States

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 A_z 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 A_z value was 0.93.

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Karla Horsch, Alfredo Fredy Ceballos, Maryellen Lissak Giger, Ioana R. Bonta, Zhimin Huo, Carl J. Vyborny, Edward R. Hendrick, and Li Lan "Optimizing feature selection across a multimodality database in computerized classification of breast lesions", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); https://doi.org/10.1117/12.467053

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