Abstract
“Power-law” characterization of breast tissues can be achieved in different ways, as can be found in literature. The outcomes of all such characterizations appear to be in line with early observations stating that the power-law exponent β has a value between two and four for mammography images. Ambiguous aspects of power-law characterization and their implementation are addressed in this paper, including data representation, filtering, frequency range and ROI size. It is shown how different implementations have an effect on computed β values, using three different datasets (mammography images, chest x-ray images, and non-medical images). It is found that differences in computed β value within the mammography image dataset can be even larger than the differences between the mammography image dataset, chest x-ray images, and the non-medical images. A clear description of the used methodology is therefore essential for the interpretation and relevance of any power-law characterization.
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Kierkels, J.J.M., Veldkamp, W.J.H., Bouwman, R.W., van Engen, R.E. (2012). Power-Law, Beta, and (Slight) Chaos in Automated Mammography Breast Structure Characterization. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_69
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DOI: https://doi.org/10.1007/978-3-642-31271-7_69
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