TY - JOUR
T1 - Design of Microwave-based Brain Tumor Detection Framework with the Development of Sparse and Low-Rank Compressive Sensing Image Reconstruction
AU - Sholeh, Hermawan Rahman
AU - Rizkinia, Mia
AU - Basari, Basari
N1 - Funding Information:
The authors acknowledge Universitas Indonesia’s support through the Q3 Research Grant 2020 and Q1Q2 Research Grant 2019 under contract number NKB ? ? ? ??/UN ?.R ?. ?/HKP. ? ?. ? ?/ ? ?? ?.
Publisher Copyright:
© 2020. All Rights Reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/20
Y1 - 2020/11/20
N2 - Cancer is one of the leading causes of death, and the brain is one of the body's cancer-prone organs. The early detection of brain tumors can reduce cancer risk, which is practically assisted and conducted using scanners such as computed tomography (CT) and magnetic resonance imaging (MRI). However, those modalities are high-cost and large-sized, and they have a side effect risk to health. Alternatively, microwave imaging offers a novel cancer scanning method for early detection with low cost, small size and low health risk. Consequently, this research designs and creates a framework with a novel microwave image reconstruction algorithm inside. The framework is a component of the controller and image reconstructor for a portable microwave-based brain tumor detector that is open source and multi-platform. For the novel algorithm, this research proposes a CS-based imaging algorithm by exploiting the data's sparse and low-rank properties. The experiment shows that the proposed algorithm can give better qualitative and quantitative reconstruction results compared to a full-sampling-based as well as CS-based algorithm.
AB - Cancer is one of the leading causes of death, and the brain is one of the body's cancer-prone organs. The early detection of brain tumors can reduce cancer risk, which is practically assisted and conducted using scanners such as computed tomography (CT) and magnetic resonance imaging (MRI). However, those modalities are high-cost and large-sized, and they have a side effect risk to health. Alternatively, microwave imaging offers a novel cancer scanning method for early detection with low cost, small size and low health risk. Consequently, this research designs and creates a framework with a novel microwave image reconstruction algorithm inside. The framework is a component of the controller and image reconstructor for a portable microwave-based brain tumor detector that is open source and multi-platform. For the novel algorithm, this research proposes a CS-based imaging algorithm by exploiting the data's sparse and low-rank properties. The experiment shows that the proposed algorithm can give better qualitative and quantitative reconstruction results compared to a full-sampling-based as well as CS-based algorithm.
KW - Compressive sensing
KW - Framework
KW - Image reconstruction
KW - Low-rank
KW - Microwave imaging
KW - Sparse
UR - http://www.scopus.com/inward/record.url?scp=85097676366&partnerID=8YFLogxK
U2 - 10.14716/ijtech.v11i5.4329
DO - 10.14716/ijtech.v11i5.4329
M3 - Article
AN - SCOPUS:85097676366
SN - 2086-9614
VL - 11
SP - 984
EP - 994
JO - International Journal of Technology
JF - International Journal of Technology
IS - 5
ER -