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 degrees C to 44 degrees C, and entropy values were capable of identifying variations of 2.0 degrees C. Besides, it was possible to quantify variations in the range from normal human body temperature (37 degrees C) to critical values, as 41 degrees C. Results are promising and encourage us to study the uncertainty associated to the experiment trying to improve the parameter sensibility.