Image Reconstruction Based on Compressive Sensing Using Total Variation Spatial Regulation for Microwave Imaging

Izra Halim Razzak, Mia Rizkinia, Basari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Medical Imaging is an essential tool in supporting medical diagnosis as well as early detection of a number of diseases, e.g., tumor and cancer. Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) are the modalities that are widely used for these purposes. Recently, Microwave Imaging has emerged as a modality that promotes tomography techniques with lower economical cost and smaller size than the previous technologies. For the sake of the image quality, however, this imaging system requires a large amount of data measurements in the reconstruction process. To overcome the drawback, this research proposes an algorithm to reconstruct the microwave images with lower number of measurements using Compressive Sensing (CS) approach. CS enables reconstructing a signal from a smaller number of measurements than which is required in the conventional sampling method. To meet this framework, in our proposed formulation, the acquisition scheme of scanning process is represented by a projection matrix for which a weight matrix of Discrete Radon Transform is used. In addition, in the data acquisition process, a number of translation and rotation positions are provided and varied in combinations to confirm the fewer measurement concept of CS. As the basic sparse reconstruction had been successfully proven for this task, this research contributes by adding spatial information using total variation (TV) and solving the proposed optimization problem using Alternating Direction Method of Multipliers (ADMM). As for the sparse dictionary matrix, Discrete Cosine Transform (DCT) is selected. The experiment shows that the proposed algorithm successfully outperforms the reconstruction which applied Filtered Back Projection (FBP), Simultaneous Algebraic Reconstruction Technique (SART) algorithm, and Basis Pursuit (BP) in terms of image quality and quantitative parameters.

Original languageEnglish
Title of host publication2019 PhotonIcs and Electromagnetics Research Symposium - Spring, PIERS-Spring 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2052-2057
Number of pages6
ISBN (Electronic)9781728134031
DOIs
Publication statusPublished - Jun 2019
Event2019 PhotonIcs and Electromagnetics Research Symposium - Spring, PIERS-Spring 2019 - Rome, Italy
Duration: 17 Jun 201920 Jun 2019

Publication series

NameProgress in Electromagnetics Research Symposium
Volume2019-June
ISSN (Print)1559-9450
ISSN (Electronic)1931-7360

Conference

Conference2019 PhotonIcs and Electromagnetics Research Symposium - Spring, PIERS-Spring 2019
CountryItaly
CityRome
Period17/06/1920/06/19

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