Statistical Segmentation of Mammograms
We proposed a new algorithm for extracting abnormalities in mammograms
using statistical methods. Since lesions in mammograms are disruptions
of the normal patterns, it is desirable to partition the image into texture
regions. Our algorithm assigns each pixel in the mammogram membership to
one of a finite number of classes depending on statistical properties of
a pixel and its neighbors.
Both the original mammogram and its class labels are modeled as discrete
parameter random fields. We estimate the pixel classes by
minimizing the expected value of the number of misclassified pixels. This is
known as the "maximizer of the posterior marginals" (MPM) estimate. Unlike
the other MPM algorithms, our algorithm does not require that the values of
all parameters of the marginal conditional probability mass functions of the
pixel classes be known a priori .
It combines the expectation-maximization (EM)
algorithm for parameter estimation with the MPM algorithm for segmentation.
In addition to segmenting abnormalities, our algorithm can also indicate the
reliability of each classified pixel.
The following is a poster the authors presented at the 3rd International
Workshop on Digital Mammography, 1996. To have a close look at the poster,
please click on the area that interests you.
More detailed information may be found in:
Notice: the copyright to the following paper is held by the publishers. The
attached PostScript file is a preprint. Please treat this material in a way
consistent with the "fair use" provisions of appropriate copyright law.
M. L. Comer, S. Liu, E. J. Delp, "Statistical Segmentation of Mammograms,"
Proceedings of the 3nd International Workshop on Digital Mammography,
June 9-12,1996, Chicago, pp. 475-478.
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