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Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue
Title
Artificial Intelligence Based Electrical Impedance Tomography for Local Tissue.
Creator
Rao, Manasa, Pandya, Abhijit S., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
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...
Show moreThis 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.
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Date Issued
2008
PURL
http://purl.flvc.org/fau/fd/FA00012544
Subject Headings
Electrical impedance tomography, Diagnostic imaging--Data processing, Computational intelligence
Format
Document (PDF)
Bioinformatics-inspired binary image correlation
Title
Bioinformatics-inspired binary image correlation: application to bio-/medical-images, microsarrays, finger-prints and signature classifications.
Creator
Pappusetty, Deepti, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image...
Show moreThe efforts addressed in this thesis refer to assaying the extent of local features in 2D-images for the purpose of recognition and classification. It is based on comparing a test-image against a template in binary format. It is a bioinformatics-inspired approach pursued and presented as deliverables of this thesis as summarized below: 1. By applying the so-called 'Smith-Waterman (SW) local alignment' and 'Needleman-Wunsch (NW) global alignment' approaches of bioinformatics, a test 2D-image in binary format is compared against a reference image so as to recognize the differential features that reside locally in the images being compared 2. SW and NW algorithms based binary comparison involves conversion of one-dimensional sequence alignment procedure (indicated traditionally for molecular sequence comparison adopted in bioinformatics) to 2D-image matrix 3. Relevant algorithms specific to computations are implemented as MatLabTM codes 4. Test-images considered are: Real-world bio-/medical-images, synthetic images, microarrays, biometric finger prints (thumb-impressions) and handwritten signatures. Based on the results, conclusions are enumerated and inferences are made with directions for future studies.
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Date Issued
2011
PURL
http://purl.flvc.org/FAU/3333052
Subject Headings
Bioinformatics, Statistical methods, Diagnostic imaging, Digital techniques, Image processing, Digital techniques, Pattern perception, Data processing, DNA microarrays
Format
Document (PDF)
Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications
Title
Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications.
Creator
Lewkowitz, Stephanie, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient...
Show moreEvery scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and diā†µerent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004721, http://purl.flvc.org/fau/fd/FA00004721
Subject Headings
Computer vision in medicine, Diagnostic imaging -- Data processing, Mathematical models, Medical errors -- Prevention, Medical physics, Sampling (Statistics)
Format
Document (PDF)