Statistical fractal border features for mammographic breast mass analysis

Alan I. Penn; Scott F. Thompson; Murray H. Loew; Radhika Sivaramakrishna; Kimerly Powell

doi:10.1117/12.348609

21 May 1999 Statistical fractal border features for mammographic breast mass analysis

Alan I. Penn, Scott F. Thompson, Murray H. Loew, Radhika Sivaramakrishna, Kimerly Powell

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348609
Event: Medical Imaging '99, 1999, San Diego, CA, United States

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.

Citation Download Citation

Alan I. Penn, Scott F. Thompson, Murray H. Loew, Radhika Sivaramakrishna, and Kimerly Powell "Statistical fractal border features for mammographic breast mass analysis", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348609

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