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Proceedings Paper

Statistical fractal border features for mammographic breast mass analysis
Author(s): Alan I. Penn; Scott F. Thompson; Murray H. Loew; Radhika Sivaramakrishna; Kimerly Powell
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Paper Abstract

We present preliminary results of a study in which Fractal Interpolation Function Models (FIFM) are used to generate a fractal dimension (fd) feature to discriminate between benign and malignant masses on digitized mammograms. The FIFM method identifies boundary segments that are approximately self-affine and can be accurately modeled with multiple fractal interpolation functions (FIF). The fd of a segment is estimated to be the mean of the fds from the FIF models of that segment. An overall fd feature is computed as the mean of multiple segment fds. The statistical approach provides a stability to the overall fd feature. The FIFM feature may be useful in improving the performance of computer-assisted-diagnosis systems.

Paper Details

Date Published: 21 May 1999
PDF: 8 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348609
Show Author Affiliations
Alan I. Penn, Alan Penn and Associates, George Washington Univ., and Univ. of Pennsylvania (United States)
Scott F. Thompson, Alan Penn and Associates (United States)
Murray H. Loew, George Washington Univ. (United States)
Radhika Sivaramakrishna, Cleveland Clinic Foundation (United States)
Kimerly Powell, Cleveland Clinic Foundation (United States)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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