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To achieve a precise noninvasive temperature estimation, inside patient tissues, would open promising research fields, because its clinic results would provide early-diagnosis tools. In fact, detecting changes of thermal origin in ultrasonic echo spectra could be useful as an early complementary indicator of infections, inflammations, or cancer. But the effective clinic applications to diagnosis of thermometry ultrasonic techniques, proposed previously, require additional research. Before their implementations with ultrasonic probes and real-time electronic and processing systems, rigorous analyses must be still made over transient echotraces acquired from well-controlled biological and computational phantoms, to improve resolutions and evaluate clinic limitations. It must be based on computing improved signal-processing algorithms emulating tissues responses. Some related parameters in echo-traces reflected by semiregular scattering tissues must be carefully quantified to get a precise processing protocols definition. In this paper, approaches for non-invasive spectral ultrasonic detection are analyzed. Extensions of author's innovations for ultrasonic thermometry are shown and applied to computationally modeled echotraces from scattered biological phantoms, attaining high resolution (better than 0.1°C). Computer methods are provided for viability evaluation of thermal estimation from echoes with distinct noise levels, difficult to be interpreted, and its effectiveness is evaluated as possible diagnosis tool in scattered tissues like liver.

The resolution in images is a perceptible detail measure. If the resolution increases, perception of fine details, edges, clearness of the objects and image quality increases too. Video surveillance cameras usually have a standard resolution for video surveillance applications, commonly in VGA resolution (640 x 480 pixels). This video image in most of the cases does not provide enough information to identify a person or an object, the cameras with low resolution deliver poor data information and poor information in detailed images to maximize its size. If an area needs more resolution, it is necessary an algorithm that achieve this without the loss of inherent characteristics. We selected the fuzzy logic theory to solve these problems. This technique is used to improve image resolution. It helps in processes where ambiguity and vagueness in the data interpolation are present, this is due to the non-linearity of image information (edges, fine details, textures, etc.). The proposed Gaussian membership functions have non-linear characteristics, so they obtain good results in interpolation process.

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