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Proceedings Paper

Automated identification of temporal pattern with high initial enhancement in dynamic MR lesions using fuzzy c-means algorithm
Author(s): Weijie Chen; Maryellen Lissak Giger; Ulrich Bick
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Paper Abstract

In contrast-enhanced (CE) MRI of the breast, signal-intensity time curves have been proven useful in differentiating between benign and malignant lesions. Due to uptake heterogeneity in the breast lesion, however, the signal-intensity time curve obtained from a specific region in the lesion may outperform that from the entire lesion. In this study, we propose the use of fuzzy c-means (FCM) clustering algorithms to reveal different temporal patterns within the breast lesion. The algorithm finds fuzzy cluster centers (i.e., temporal patterns) and assigns membership values to each voxel. The temporal pattern with maximum initial enhancement is selected as the representative curve of the lesion and the thresholded membership map is the identified region of fast enhancement. The approach was applied to the analysis of 121 lesions (77 malignant and 44 benign). The resulting representative curves were classified with linear discriminant analysis (LDA). The differentiation performance of LDA output in leave-one-out cross evaluation was assessed using receiver operating characteristic (ROC) analysis. Our results show that the use of FCM significantly improved the performance of signal-intensity time curves in the task of distinguishing between malignant and benign lesions.

Paper Details

Date Published: 12 May 2004
PDF: 5 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535619
Show Author Affiliations
Weijie Chen, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Ulrich Bick, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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