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

Tumor and microcalcification characterization using Entropy, Fractal Dimension and intensity values statistical analysis in mammography