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Simulation of breast cancer imaging using magnetic induction tomography

Master of Science in Biomedical Electronic Engineering

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Bibliographic Details
Main Author: Balasena, Gowry
Other Authors: Shahriman, Abu Bakar, Assoc. Prof. Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Magnetic induction
Tomography
Image reconstruction
Breast > Cancer
Cancer
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78038
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spelling my.unimap-780382023-03-07T02:20:28Z Simulation of breast cancer imaging using magnetic induction tomography Balasena, Gowry Shahriman, Abu Bakar, Assoc. Prof. Dr. Magnetic induction Tomography Image reconstruction Breast -- Cancer Cancer Master of Science in Biomedical Electronic Engineering In order to reduce the physical trauma caused by breast compressions, exposure to radiations and the high price of diagnostic tests, a new cost effective magnetic induction tomography (MIT) system is proposed to identify and locate tumors among the heterogeneous breast tissues. This technique operates in a non-invasive and contactless manner with the breasts. The numerical simulation imaging system consists of 16 sensor coils with 1 coil acting as the transmitter and the rest as receivers at a single time period, leading to a total of 240 receiver readings. The receiver readings and 240 generated sensitivity matrices were then used to reconstruct the images of the breast using linear back projection (LBP) algorithm after a careful comparison has been made on the algorithm with newton one-step error reconstruction (NOSER) and truncated singular value decomposition (TSVD) algorithms. The reconstructed images were assessed in terms of three essential error metrics which are the resolution (RES), magnification (MAG), and the position error (PE). The average errors are 0.004728, 13.7793, and 45.1929 for the RES, MAG and PE metrics respectively. Nonetheless, the average error metric values for the images of tumors located deepest, at the origin (0,0), show better results in terms of PE, that is -2.5356. A strong correlation between the MIT sensor readings and the size of simulated breast tumor was also observed from the adjusted R square value which is 0.998, indicating that the data fitted are very close to the regression line. The obtained results verify that the proposed MIT design and image reconstruction algorithm provide a promising alternative for breast cancer imaging although further studies are required to validate the simulation MIT data. 2017 2023-03-07T02:20:28Z 2023-03-07T02:20:28Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78038 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Magnetic induction
Tomography
Image reconstruction
Breast -- Cancer
Cancer
spellingShingle Magnetic induction
Tomography
Image reconstruction
Breast -- Cancer
Cancer
Balasena, Gowry
Simulation of breast cancer imaging using magnetic induction tomography
description Master of Science in Biomedical Electronic Engineering
author2 Shahriman, Abu Bakar, Assoc. Prof. Dr.
author_facet Shahriman, Abu Bakar, Assoc. Prof. Dr.
Balasena, Gowry
format Thesis
author Balasena, Gowry
author_sort Balasena, Gowry
title Simulation of breast cancer imaging using magnetic induction tomography
title_short Simulation of breast cancer imaging using magnetic induction tomography
title_full Simulation of breast cancer imaging using magnetic induction tomography
title_fullStr Simulation of breast cancer imaging using magnetic induction tomography
title_full_unstemmed Simulation of breast cancer imaging using magnetic induction tomography
title_sort simulation of breast cancer imaging using magnetic induction tomography
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78038
_version_ 1772813092378378240
score 13.214268

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