Academic Radiology
Volume 13, Issue 1 , Pages 63-72, January 2006

A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1

University of Chicago, Radiology, 584 South Maryland, MC Chicago, IL 60637 (W.C.), Charite University Medicine, Department of Radiology, Campus Mitte, Berlin, Germany

Received 25 July 2005; received in revised form 25 August 2005; accepted 27 August 2005.

Rationale and Objectives

Accurate quantification of the shape and extent of breast tumors has a vital role in nearly all applications of breast magnetic resonance (MR) imaging (MRI). Specifically, tumor segmentation is a key component in the computerized assessment of likelihood of malignancy. However, manual delineation of lesions in four-dimensional MR images is labor intensive and subject to interobserver and intraobserver variations. We developed a computerized lesion segmentation method that has the advantage of being automatic, efficient, and objective.

Materials and Methods

We present a fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images. The proposed lesion segmentation algorithm consists of six consecutive stages: region of interest (ROI) selection by a human operator, lesion enhancement within the selected ROI, application of FCM on the enhanced ROI, binarization of the lesion membership map, connected-component labeling and object selection, and hole-filling on the selected object. We applied the algorithm to a clinical MR database consisting of 121 primary mass lesions. Manual segmentation of the lesions by an expert MR radiologist served as a reference in the evaluation of the computerized segmentation method. We also compared the proposed algorithm with a previously developed volume-growing (VG) method.

Results

For the 121 mass lesions in our database, 97% of lesions were segmented correctly by means of the proposed FCM-based method at an overlap threshold of 0.4, whereas 84% of lesions were correctly segmented by means of the VG method.

Conclusion

Our proposed algorithm for breast-lesion segmentation in dynamic contrast-enhanced MRI was shown to be effective and efficient.

Key Words:  Tumor segmentation , dynamic contrast-enhanced magnetic resonance imaging , fuzzy c-means , computer-aided diagnosis , breast cancer

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 Supported in part by Breast Cancer Research Program grant no. DAMD17-03-1-0245 from the Department of Defense and grant no. CA89452 from the US Public Health Service. M.L.G. is a shareholder in R2 Technology, Sunnyvale, CA. It is the policy of the University of Chicago that investigators disclose publicly actual or potential significant financial interests that may appear to be affected by the research activities.

PII: S1076-6332(05)00803-2

doi:10.1016/j.acra.2005.08.035

Academic Radiology
Volume 13, Issue 1 , Pages 63-72, January 2006

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