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Proceedings Paper

Localized fractal dimension measurement in digital mammographic images
Author(s): Christine J. Burdett; Mita D. Desai
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Paper Abstract

This paper investigates a novel image processing tool for differentiating between malignant and benign lesions in digitized mammograms. The new technique makes use of localized measurements of fractal parameters, calculated through the use of Gabor filters, and provides a means of quantifying the intrinsic roughness of the intensity surface of the digitized mammographic data. Since benign lesions are usually smoothly marginated while malignant lesions are characterized by indistinct, rough, spiculated borders, the premise used is that a benign lesion will have a value of fractal dimension that is lower than that of a malignant lesion. The technique allows spatio-spectrally accurate fractal parameter measurements to be made by making fractal measurements over different scales. This is done by decomposing the image into N bandpass channels. The local fractal dimension can then be measured from the spectral samples by finding the best linear fit (linear regression) to the data set, from which the fractal parameters of interest are computed. Conjoint resolution can be obtained by selecting the channels filters to be Gabor functions, or frequency-translated Gaussian functions. Results of this technique as applied to lesions in digitized mammograms are presented, using mammogram x-rays digitized to 12 bits of gray scale resolution.

Paper Details

Date Published: 22 October 1993
PDF: 11 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.157903
Show Author Affiliations
Christine J. Burdett, Univ. of Texas/San Antonio (United States)
Mita D. Desai, Univ. of Texas/San Antonio (United States)

Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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