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Proceedings Paper

Automated feature extraction and classification of breast lesions in magnetic resonance images
Author(s): Kenneth G. A. Gilhuijs; Maryellen Lissak Giger; Ulrich Bick
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Paper Abstract

We are developing computerized methods to distinguish between malignant and benign lesions in contrast-enhanced magnetic resonance images of the breast. In this study, we compare 2D spatial analysis of lesions with 3D spatial analysis. Our database consists of 28 lesions: 15 malignant and 13 benign. At 90 s intervals, 4 to 6 scans are obtained, and the spatial uptake of contrast agent is analyzed. Computer-extracted features quantify the inhomogeneity of uptake, sharpness of the margins, and shape of the lesion. Stepwise multiple regression is employed to obtain a subset of features, followed by linear discriminant analysis to estimate the likelihood of malignancy. Cross-validation and ROC analysis are used to evaluate the performance of the method in distinguishing between benign and malignant lesions. The procedures are performed in 3D, and in 2D from single and multiple slices. Shape and sharpness of the lesion were the most effective features. ROC analysis yielded an Az value of 0.96 for 3D features, between 0.67 and 0.92 for single slices, and 0.88 for 2D features from multiple slices. The performance of 2D analysis on single slices depends strongly on the selected plane and may be significantly lower than the accuracy of full 3D analysis.

Paper Details

Date Published: 24 June 1998
PDF: 7 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310904
Show Author Affiliations
Kenneth G. A. Gilhuijs, Univ. of Chicago and Netherlands Cancer Research Institiute (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Ulrich Bick, Univ. of Muenster (United States)


Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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