  Digital analysis of mammographic images is a complementary tool to clinical
evaluation, commonly used to identify tumors and/or microcalcifications in
mammograms. Recent mammographic equipment, can automatically classify them
using this methodology. The difficulty in finding and classifying such areas,
arise from different factors such as: image acquisition methodology, excess of
brightness, similar physiological and radiological properties of tissues, etc.
In this work it is proposed that the numerical computations of fractal
dimension and entropy are tools that could be used to automatically segment and
distinguish malignant tumors and/or microcalcifications in digital mammograms.
The study consisted in segment the image in two areas: background and confirmed
malignant tumor and/or microcalcification, to which the fractal dimension and
entropy values are calculated and it was found the correlation between them.
The analysis was performed on a grayscale re-digitized images from images
provided locally by hospital for microcalcifications and from public databases
for malignant tumors. From these re-digitized images, it was able to give the
exact coordinates as the diagnosis provides. For any image, the highest
intensity value was located in the tumor and/or microcalcification area,
resulting that the fractal dimension had a higher value than the rest of the
image, while the entropy value was lower, due to the uniformity of intensities
in these areas. To complete this study, it was performed a data analysis with
the set of intensities in each area. This allowed to distinguish between the
areas of interest according to the value of the intensities, having a value
greater than 3 {\sigma}. Finally, it is shown a technique to visually highlight
the malignant tumor and/or microcalcification.
