Application of Continuous Wavelet Transform to Raw Magnetic Resonance Signals to Differentiate Tissue Features

It is quite useful to diagnose different health conditions such as cancer, osteolysis, amongst others, by the use of images for clinical practice or research. The study and to interpret, anatomical areas of interest has always been a core subject of imaging systems. Technological development in this area has produce had focuses on enhance temporal and spatial resolutions. Although a lot of research has been done to improve images characteristics, the final diagnostic relies on the judgment and experience of the medical specialist since there is not a numerical relationship to the mechanical properties of the human tissues. Based on the former, this work is aimed to develop a procedure to identify the characteristics of tissue and relate them to mechanical properties as an aid to medical diagnosis. Here, the continuous wavelet transform is applied as a passband filter tool to raw magnetic resonance signals. A relation between frequency content and tissues present in the image was identified. From here,it was found that tissue regions exhibiting higher stiffness and toughness emit signals with low frequency, while tissues with lower stiffness and toughness emit signals with high frequency. The method proposed allows to extract information that can help to generate parameters for classification, detection and mechanical properties of human tissue, for the tissue disease diagnosis.