A new approach to mammograms
speeds up computer automated
"second opinion" interpretations of breast images. Just as a Google
search first returns a list of only those websites that it
determines to have the most important and useful information on the
words entered in the search, so medical researchers want to speed up
the computer search for suspicious-looking breast masses. Georgia
Tourassi (Georgia.email@example.com) and her colleagues at Duke
University employ such a Google-like approach that can retrieve
useful information from steadily growing mammogram-image databases
more rapidly than before.
Increasingly being used in clinical settings, knowledge-based
computer-assisted detection (KB-CAD) systems compare a mammogram
image to those of known cases of breast cancer in order to aid
radiologists in their diagnosis. When a new, unknown case is
presented for analysis, the KB-CAD system compares the case to
mammography images in the database. If the unknown case is visually
similar enough to a known case of breast cancer, then this would
suggest the presence of cancer.
Traditionally, KC-CAD systems compare the mammogram image under
investigation to every image of breast cancer in a computer
database. Although diagnostically accurate, this practice becomes
inefficient as image databases increase in size. A larger wealth
of images provides more information from which the systems can draw
upon for analysis, but comparing the mammogram in question to every
image becomes inefficient. Therefore, the Duke researchers
incorporate an additional, "Google," approach. They compare the
mammogram only to selected images that are most highly ranked for
their information content.
The selection of the most informative mammogram cases is performed
using a strategy based on the concept of "image entropy." Image
entropy represents a measure of the disorder or complexity in the
image. An image that is all black or white has zero entropy. An
image of a checkerboard has low entropy--it consists of an equal
number of light and dark pixels. Complex images with more uniform
distributions of many pixel intensity levels have higher entropy and
are considered more informative in the context of the Duke system.
However, normal breast tissue can be as complex as a tumor.
According to Tourassi, who reported her results at this week's
meeting of the American Association of Physicists in Medicine in
Orlando, this is precisely the reason mammographic diagnosis is such
a challenging task. The Duke approach includes normal cases as well
in the decision-making process.
Applied to an existing database of 2,300 mammography images, the
Duke method compared a sample mammogram to 600 images it ranked as
most informative. This cut down the time the CAD system took to
analyze the mammogram by one-fourth, to less than 3 seconds per
query. In the next year, the researchers expect to follow up their
pilot study with a larger investigation to evaluate the clinical
impact of this new approach.
Meeting Paper: TU-D-330A-8
AAPM meeting virtual pressroom
Contact Georgia Tourassi, Duke University