Simulation of breast cancer imaging using magnetic induction tomography

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 ope...

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Format:	Thesis
Language:	English
Subjects:	Magnetic induction Tomography Image reconstruction Breast > Cancer Cancer
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/3/Gowry.pdf
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record_format	uketd_dc
spelling	my-unimap-780382023-03-07T02:20:28Z Simulation of breast cancer imaging using magnetic induction tomography Shahriman, Abu Bakar, Assoc. Prof. Dr. 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. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78038 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/4/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/1/Page%201-24.pdf 5586bfc391de44025131b036823af700 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/2/Full%20text.pdf 6ca27f9a6497fade0b7bdc009d39c715 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/3/Gowry.pdf 11acda1d9f5b2b26d7b8230a20a0f58e Universiti Malaysia Perlis (UniMAP) Magnetic induction Tomography Image reconstruction Breast -- Cancer Cancer School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Shahriman, Abu Bakar, Assoc. Prof. Dr.
topic	Magnetic induction Tomography Image reconstruction Breast -- Cancer Cancer
spellingShingle	Magnetic induction Tomography Image reconstruction Breast -- Cancer Cancer Simulation of breast cancer imaging using magnetic induction tomography
description	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.
format	Thesis
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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78038/3/Gowry.pdf
_version_	1776104265042886656

Simulation of breast cancer imaging using magnetic induction tomography

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