Automated analysis of contrast enhancement in breast MRI lesions using mean shift clustering for ROI selection

✍ Scribed by Mark J. Stoutjesdijk; Jeroen Veltman; Henkjan Huisman; Nico Karssemeijer; Jelle O. Barentsz; Johan G. Blickman; Carla Boetes

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 779 KB
• Volume: 26
• Category: Article
• ISSN: 1053-1807
• DOI: 10.1002/jmri.21026

✦ Synopsis

Abstract

Purpose

To evaluate a new method for automated determination of a region of interest (ROI) for the analysis of contrast enhancement in breast MRI.

Materials and Methods

Mean shift multidimensional clustering (MS‐MDC) was employed to divide 92 lesions into several spatially contiguous clusters each, based on multiple enhancement parameters. The ROIs were defined as the clusters with the highest probability of malignancy. The performance of enhancement analysis within these ROIs was estimated using the area under the receiver operator characteristic curve (AUC), and compared against a radiologist's final assessment and a classifier using histogram analysis (HA). For HA, the first, second, and third quartiles were evaluated.

Results

MS‐MDC resulted in AUC = 0.88 with a 95% confidence interval (CI) of 0.81–0.95. The AUC for the radiologist's assessment was 0.93 (95%CI = 0.87–0.97). Best HA performance was found using the first quartile, with AUC = 0.79 (95%CI = 0.69–0.88). There was no significant difference between MS‐MDC and the radiologist (P = 0.40). The improvement of MS‐MDC over HA was significant (P = 0.018).

Conclusion

Mean shift clustering followed by automated selection of the most suspicious cluster resulted in accurate ROIs in breast MRI lesions. J. Magn. Reson. Imaging 2007;26:606–614. © 2007 Wiley‐Liss, Inc.