Browsing by Author "Kurrant, Douglas"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Regional Estimation of the Geometric and Dielectric Properties of Inhomogeneous Objects using Near-field Reflection Data
    (2013-04-30) Kurrant, Douglas; Fear, Elise
    An inversion strategy is presented that integrates a radar-based method with microwave tomography (MWT). The inversion technique is carried out in two steps. First, a reconstruction model indicating the locations and spatial features of regions of interest is constructed efficiently and quickly using ultrawideband (UWB) reflection data. The object-specific reconstruction model is incorporated into the second step of the procedure which estimates the mean dielectric properties over each region using MWT methods. Segmenting the internal structure of the object into regions provides prior information about an object's internal geometry and significantly simplifies the parameter space structure so that the inverse scattering problem solved with MWT is not as ill-posed as those typically encountered. The aim is to provide information about the basic structure of an object, including the geometric and mean dielectric properties of regions predominantly composed of a given material, rather than to reconstruct a detailed image.
  • Thumbnail Image
    ItemOpen Access
    Surface Estimation for Microwave Imaging
    (MDPI, 2017-07-19) Kurrant, Douglas; Bourqui, Jeremie; Fear, Elise
    Biomedical imaging and sensing applications in many scenarios demand accurate surface estimation from a sparse set of noisy measurements. These measurements may arise from a variety of sensing modalities, including laser or electromagnetic samples of an object's surface. We describe a state-of-the-art microwave imaging prototype that has sensors to acquire both microwave and laser measurements. The approach developed to translate sparse samples of the breast surface into an accurate estimate of the region of interest is detailed. To evaluate the efficacy of the method, laser and electromagnetic samples are acquired by sensors from three realistic breast models with varying sizes and shapes. A set of metrics is developed to assist with the investigation and demonstrate that the algorithm is able to accurately estimate the shape of a realistic breast phantom when only a sparse set of data are available. Moreover, the algorithm is robust to the presence of measurement noise, and is effective when applied to measurement scans of patients acquired with the prototype.