Abstract
To investigate the efficacy of an automated method of shape measurement for improving the discrimination of benign and malignant breast lesions. A total of 47 breast lesions (32 malignant and 15 benign) were examined using a 1.5 Tesla system. Regions of interest (ROIs) were manually drawn and extracted from high-resolution, fat-suppressed, postcontrast images, or were extracted with the use of a semiautomated computer algorithm. Shape parameters (i.e., complexity, convexity, circularity, and degree of elongation) were determined to assess whether they could be used to discriminate breast lesions. Convexity differed significantly between the benign and malignant groups for both ROI methods. In addition, the semiautomated method demonstrated significantly different values of complexity. This work demonstrates the usefulness of several shape descriptors for characterizing breast lesions, and shows that the automated method of analysis improves the discrimination and standardization of data.
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