Automated Scaling Region of Interest with Iterative Edge Preserving in Forward-Backward Time-Stepping

The thesis reported on a deterministic inverse scattering method in time-domain to reconstruct dielectric profiles of an unknown embedded object within its peripheral region. Image reconstruction of geometrically simple objects taken after breast profiles and lung(s) model are solely done by simulation executed in single computing. In effort to alleviate the nonlinearity problem of inverse scattering, the inversion technique has been integrated with varied types of techniques to improve the inversion solution. The extended algorithm would only increase the computational cost, nevertheless would never eliminate the nonlinearity problem that degrade the inversion solution. The reconstruction process started in a coarse region which then rescaled down according to object geometry configuration. This is accomplished by combining an inversion technique of Forward-Backward Time-Stepping (FBTS) with an Automated Scaling Region of Interest (AS-ROI) method. Edge preserving techniques comprises of edge preserving regularization and anisotropic diffusion are integrated into the combined FBTS and AS-ROI to further increase the accuracy level in the profiles’ intensity. Accuracy of reconstructed object is validated by using mean squared error (MSE), relative error (RE) and Euclidean distance (ED) that measure the precision in terms of pixels’ intensity, size and localization. Results exhibited significant improvement in the accuracy level of reconstructed images with a combined method of FBTS and AS-ROI. AS-ROI has successfully increased the pixels precision in relative to FBTS about 10.33% for breast model and 25.17% for lung model. The accuracy increment by AS-ROI is due to better fields penetration as exterior pixels are replaced with background layer of low profiles. In the combined rescaled and regularized method in FBTS, edge preserving smoothing filter and regularization are alternately imposed on the improved reconstructed profiles by AS-ROI. Therefore, the accuracy is further increased to 18.58% and 40.68%, respectively for breast and lung model. In term of size estimation, average error in object’s radius is analogous to accuracy level measured in MSE. It indicates that efficiency of AS-ROI is highly relied upon the accuracy of FBTS estimation in reconstructing image profiles prior to rescaling process. Apart from that, accuracy in size estimation differ for varied shapes due to number of pixels at the boundary. Average RE is 61.94% for a circular shape in breast model, meanwhile attains RE of 4.17% for a U-shape object. The highest RE for a single circular tumour’s size in lung model is 62.5%. However, object localization by AS-ROI is 100% for a circular and U-shape object in breast model. Nevertheless, AS-ROI attains an average ED of 2.3 for lung model. Computational time decreases accordingly with the reduced number of pixels involved after the rescaling process. Reduction in the computational time is 13.06% for breast model, nonetheless 28.74% for lung model. Significant time reduction observed for lung model is benefited from considerable number of pixels that has been removed from the original image. The inclusion of edge preserving techniques into the combined AS-ROI with FBTS however has slightly increase the computational time about 5.87% and 4.69% for breast and lung model, respectively. Nevertheless, it compensates the computational cost for higher accuracy of image profiles’ intensities.