2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (2016)
Sao Paulo, Brazil
Oct. 4, 2016 to Oct. 7, 2016
Mammographic Computer-Aided Diagnosis systems are applications designed to assist radiologists in diagnosis of malignancy in mammographic findings. Most methods described in the literature do not perform a proper preprocessing step in mammographic images prior to classification, which can generate inconsistent results due to the potentially large amount of noise in medical images. This paper proposes a new method based on Information Theory and Data Compression for detection of random noise in image bit planes. In order to validate the efficiency of the proposed noise removal method, we used Machine Learning algorithms to classify mammographic findings from the Digital Database for Screening Mammography. Results using texture features indicate that a reduction in the radiometric resolution of 4 or 5 bit planes in digitized screen film mammographic images result in a better classification performance.
Breast cancer, Mammography, Delta-sigma modulation, Entropy, Noise measurement, Context
H. N. Oliveira, J. A. Santos, M. C. Melo, T. G. Rego and L. V. Batista, "Information Theory-Based Detection of Noisy Bit Planes in Medical Images," 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images(SIBGRAPI), Sao Paulo, Brazil, 2016, pp. 32-39.