A novel technique for imaging based on ultrawideband (UWB) microwave signals is introduced. Specifically, the procedure deals with the Huygens Principle (HP); using HP to forward-propagate the waves removes the need to solve inverse problems and, consequently, no matrix generation/inversion is required. Together with its simplicity, the methodology permits the capture of contrast-the extent to which different media can be discriminated in the final image. Moreover, UWB allows all the information in the frequency domain to be utilized by combining the information from the individual frequencies to construct a consistent image. It follows that the methodology can identify the presence and location of significant scatterers inside a volume. Validation of the technique through both simulations and measurements on cylinder and spheres with inclusions has been performed. Application of the proposed technique to medical imaging is envisaged
Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for breast lesion detection is used to accumulate clinical data from subjects undergoing breast examinations at the Department of Diagnostic Imaging, Perugia Hospital, Perugia, Italy. This paper presents the first ever clinical demonstration and comparison of a microwave ultra-wideband (UWB) device augmented by machine learning with subjects who are simultaneously undergoing conventional breast examinations. Non-ionizing microwave signals are transmitted through the breast tissue and the scattering parameters (S-parameter) are received via a dedicated moving transmitting and receiving antenna set-up. The output of a parallel radiologist study for the same subjects, performed using conventional techniques, is taken to pre-process microwave data and create suitable data for the machine intelligence system. These data are used to train and investigate several suitable supervised machine learning algorithms nearest neighbour (NN), multi-layer perceptron (MLP) neural network, and support vector machine (SVM) to create an intelligent classification system towards supporting clinicians to recognise breasts with lesions. The results are rigorously analysed, validated through statistical measurements, and found the quadratic kernel of SVM can classify the breast data with 98% accuracy.
This paper presents preliminary results of an innovative microwave imaging apparatus for breast lesions detection. Specifically, a Huygens Principle based method is employed to process the microwave signals and to build the respective microwave images. The apparatus has been first tested on phantoms. Next, its performance has been verified through clinical examinations on 22 healthy breasts and on 29 breast having lesions, using as gold standard the output of the radiologist study review obtained using conventional techniques. Specifically, we introduce a metric, which is the ratio between maximum and average of the image intensity (MAX/AVG). We found that MAX/AVG of microwave images can be used for classifying breasts containing lesions. In addition, using MAX/AVG as classification parameter, receiver operating characteristic curves have been empirically determined. Furthermore, for one randomly selected breast having lesion, we have demonstrated that the localization of the inclusion acquired through microwave imaging is compatible with mammography images.
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