| dc.description.abstract |
In this paper, a novel fast method for modeling mammograms
by deterministic fractal coding approach to detect
the presence of microcalcifications, which are early
signs of breast cancer, is presented. The modeled
mammogram obtained using fractal encoding method is
visually similar to the original image containing microcalcifications,
and therefore, when it is taken out from
the original mammogram, the presence of microcalcifications
can be enhanced. The limitation of fractal image
modeling is the tremendous time required for encoding.
In the present work, instead of searching for a matching
domain in the entire domain pool of the image, three
methods based on mean and variance, dynamic range of
the image blocks, and mass center features are used.
This reduced the encoding time by a factor of 3, 89, and
13, respectively, in the three methods with respect to
the conventional fractal image coding method with quad
tree partitioning. The mammograms obtained from The
Mammographic Image Analysis Society database
(ground truth available) gave a total detection score of
87.6%, 87.6%, 90.5%, and 87.6%, for the conventional
and the proposed three methods, respectively. |
en_US |