AI Article Synopsis

  • * These details could indicate potential health issues such as tissue degeneration, inflammation, or tumors, highlighting the need for automated analysis methods to assist physicians in accurate diagnoses.
  • * The study aims to showcase the effectiveness of fractal analysis in enhancing the evaluation of ultrasound images, specifically relating to submandibular diffuse pathology, and addressing the operator dependence of current clinical practices.

Article Abstract

The geometry of some medical images of tissues, obtained by elastography and ultrasonography, is characterized in terms of complexity parameters such as the fractal dimension (FD). It is well known that in any image there are very subtle details that are not easily detectable by the human eye. However, in many cases like medical imaging diagnosis, these details are very important since they might contain some hidden information about the possible existence of certain pathological lesions like tissue degeneration, inflammation, or tumors. Therefore, an automatic method of analysis could be an expedient tool for physicians to give a faultless diagnosis. The fractal analysis is of great importance in relation to a quantitative evaluation of "real-time" elastography, a procedure considered to be operator dependent in the current clinical practice. Mathematical analysis reveals significant discrepancies among normal and pathological image patterns. The main objective of our work is to demonstrate the clinical utility of this procedure on an ultrasound image corresponding to a submandibular diffuse pathology.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3671291PMC
http://dx.doi.org/10.1155/2013/347238DOI Listing

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