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- Evaluation of the influence of large temperature variations on the grey level content of B-mode imagesPublication . Alvarenga, A. V.; Teixeira, César; Ruano, M. Graça; Pereira, WagnerIn 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 27oC to 44oC, and entropy values were capable of identifying variations of 2.0oC. Besides, it was possible to quantify variations in the range from normal human body temperature (37oC) to critical values, as 41oC. Results are promising and encourage us to study the uncertainty associated to the experiment trying to improve the parameter sensibility.
- Influence of temperature variations on the entropy and correlation of the Grey-Level Co-occurrence Matrix from B-Mode imagesPublication . Alvarenga, A. V.; Teixeira, C. A.; Ruano, M. Graça; Pereira, W. C. A.In this work, the feasibility of texture parameters extracted from B-Mode images were explored in quantifying medium temperature variation. The goal is to understand how parameters obtained from the gray-level content can be used to improve the actual state-of-the-art methods for non-invasive temperature estimation (NITE). B-Mode images were collected from a tissue mimic phantom heated in a water bath. The phantom is a mixture of water, glycerin, agar-agar and graphite powder. This mixture aims to have similar acoustical properties to in vivo muscle. Images from the phantom were collected using an ultrasound system that has a mechanical sector transducer working at 3.5 MHz. Three temperature curves were collected, and variations between 27 and 44 degrees C during 60 min were allowed. Two parameters (correlation and entropy) were determined from Grey-Level Co-occurrence Matrix (GLCM) extracted from image, and then assessed for non-invasive temperature estimation. Entropy values were capable of identifying variations of 2.0 degrees C. Besides, it was possible to quantify variations from normal human body temperature (37 degrees C) to critical values, as 41 degrees C. In contrast, despite correlation parameter values (obtained from GLCM) presented a correlation coefficient of 0.84 with temperature variation, the high dispersion of values limited the temperature assessment. (C) 2009 Elsevier B.V. All rights reserved.