Automated feature extraction and classification of breast lesions in magnetic resonance images

Kenneth G. A. Gilhuijs; Maryellen Lissak Giger; Ulrich Bick

doi:10.1117/12.310904

24 June 1998 Automated feature extraction and classification of breast lesions in magnetic resonance images

Kenneth G. A. Gilhuijs, Maryellen Lissak Giger, Ulrich Bick

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310904
Event: Medical Imaging '98, 1998, San Diego, CA, United States

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 A_z 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.

Citation Download Citation

Kenneth G. A. Gilhuijs, Maryellen Lissak Giger, and Ulrich Bick "Automated feature extraction and classification of breast lesions in magnetic resonance images", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310904

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