Evaluation of the influence of large temperature variations on the grey level content of B-mode images

In this work, the variation of the grey-level content of B-Mode images is assessed, when the medium is subjected to large temperature variations. The goal is to understand how the features obtained from the grey-level pattern can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). Herein, B-Mode images were collected from a tissue mimic phantom heated in a water bath. Entropy was extracted from image Grey-Level Co-occurrence Matrix, and then assessed for non-invasive temperature estimation. During the heating period, the average temperature varies from 27 ∘C to 44 ∘C, and entropy values were capable of identifying variations of 2.0 ∘C. Besides, it was possible to quantify variations in the range from normal human body temperature (37 ∘C) to critical values, as 41 ∘C. Results are promising and encourage us to study the uncertainty associated to the experiment trying to improve the parameter sensibility.