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Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue
| Title: | Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue. |
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| Name(s): |
Rao, Manasa Pandya, Abhijit S., Thesis advisor Florida Atlantic University, Degree grantor College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2008 | |
| Date Issued: | 2008 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 91 p. | |
| Language(s): | English | |
| Summary: | This research aims at proposing the use of Electrical Impedance Tomography (EIT), a non-invasive technique that makes it possible to measure two or three dimensional impedance of living local tissue in a human body which is applied for medical diagnosis of diseases. In order to achieve this, electrodes are attached to the part of human body and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. In this thesis we have worked towards alleviating drawbacks of EIT such as estimating parameters by incorporating it in an electrode structure and determining a solution to spatial distribution of bio-impedance to a close proximity. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called "divided electrode" for measurement of bio-impedance in a cross section of a local tissue. Its capability is examined by computer simulations, where a distributed equivalent circuit is utilized as a model for the cross section tissue. Further, a novel hybrid model is derived which is a combination of artificial intelligence based gradient free optimization technique and numerical integration in order to estimate parameters. This arne! iorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy. | |
| Identifier: | FA00012544 (IID) | |
| Degree granted: | Thesis (M.S.)--Florida Atlantic University, 2008. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | College of Engineering and Computer Science | |
| Subject(s): |
Electrical impedance tomography Diagnostic imaging--Data processing Computational intelligence |
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| Held by: | Florida Atlantic University Libraries | |
| Sublocation: | Digital Library | |
| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00012544 | |
| Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU | |
| Is Part of Series: | Florida Atlantic University Digital Library Collections. |
