Microwave Resonant Imaging for Subcutaneous Tissue Detection Using Image Analysis

This work introduces a refined image-processing method explicitly designed to amalgamate raster-scanned images obtained with a compact planar microwave resonator. The resonator tuned for use on the skin provides a deeper field penetration into the dermis layer, compared to the skin impedance measurements. The resonance of the sensor is sensitive to the dielectric properties of the tissues underneath the skin. Healthy and abnormal tissues exhibit different dielectric characteristics. Pixelated images can leverage resonant frequencies and reflection coefficients to enhance contrasts and boundaries to identify the abnormal tissues. We proposed using analytical tools like the structural similarity index to correlate frequency shifts with reflection coefficient magnitudes precisely. Preliminary results suggested the adeptness of the technique at classifying whether the tissue depth surpasses 15 mm and detecting tumor locations with a high accuracy when the image depth does not exceed 15 mm. A compact neural network model was implemented for processing the microwave images when the tumor depth exceeded 15 mm and achieved a high Dice coefficient. The work incorporated simulations to substantiate the novel concept and demonstrated its potential for noninvasive, efficient, and cost-effective subcutaneous imaging in medical applications.