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Comparison of image reconstruction on microwave tomography using filtered back projection
Amri N.A.a, Oktafiani F.a, Hamid E.Y.a, Munir A.a
a Radio Télécommunication and Microwave Laboratory School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.Back projection has been commonly used as a method to reconstruct the image of object for tomography. The selection of reconstruction method is very influential on the results. In order to refine the results, a filter is sometimes added to the algorithm of back projection. In this paper, filtered back projection (FBP) is applied to reconstruct the image of object from the measurement data which were collected hy scanning the object using a method of parallel beam projection. The reconstructed image is then filtered using FBP algorithm to investigate the effect of filtering process and the quality of image for each filter. The filters used here are Ramp filter, Shepp-Logan filter, Cosine filter, Hamming filter, and Harming filter. The filtering results show that the Hamming filter is relatively better than other filters with Mean Squared Error (MSE) value of 0.1294 and Structural Similarity Index Method (SSIM) value of 0.4102. In addition, interpolations are also added into the algorithm namely Linear interpolation, Nearest-Neighbor interpolation, and Cubic interpolation. The process demonstrates that the combination of Cubic interpolation with Hamming filter have the best performance.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Cubic interpolation,Filtered back projection,Linear Interpolation,Microwave tomography,Nearest neighbor interpolation,Reconstructed image,Reconstruction method,Structural similarity indices[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Back Projection algorithm,Filtered Back Projection (FBP),Image reconstruction,Interpolation,Microwave tomography,Parallel beam projection[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICT49546.2020.09239487[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]