Abstract / truncated to 115 words (read the full abstract)

Goals and motivation: The PhD aimed at proposing new texture feature extraction algorithms based on information theory. More precisely, new entropy-based algorithms (EBA) were proposed to study the texture of biomedical datasets and, through the use of artificial intelligence techniques, to help diagnose pulmonary pathologies. Introduction: In 1948, Shannon introduced entropy to quantify a signal’s information content in his information theory proposal. Shannon’s entropy determination was accomplished by employing a logarithm-based metric to assess the quantity of information in a message contained within a communication system. This quantity is associated with an increase in disorder. The system had a higher disorder content when the entropy values were higher. Shannon’s entropy, or first-order entropy, can be ... toggle 6 keywords

entropy algorithms – image processing – information theory – machine learning – pulmonary diseases – texture analysis.

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