A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories

In March 2003, a severe respiratory disease have broke out in Singapore causing a total of 238 infected and 33 died, implying a fatality rate of 14%. One of the measures put in place to prevent the spread of the disease in Singapore is the employment of thermograph cameras at access points of Singap...

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Main Author: Wong, Kenneth Yang Fei.
Other Authors: Lau Chiew Tong
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/36269
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-362692023-03-03T20:35:57Z A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories Wong, Kenneth Yang Fei. Lau Chiew Tong School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences In March 2003, a severe respiratory disease have broke out in Singapore causing a total of 238 infected and 33 died, implying a fatality rate of 14%. One of the measures put in place to prevent the spread of the disease in Singapore is the employment of thermograph cameras at access points of Singapore to screen people going in and out of Singapore for fever conditions. This report looks into the technology of thermography from its basis to how it is widely used in medical fields to aid in early diagnosis of diseases like breast cancer. The objective of this final year project is to look into the possibility of creating an automated fever detection system that can aid medical staffs in times of emergency where false-negative detection of fever cases can result in very dire consequences. As we are given a set of data in the form of thermographs, image pre-processing has to be done on the image to extract the features of interest. Image segmentation methods such as Histogram-Based, Edge Detection, Template matching and Region Growing and feature extraction methods such as Gabor Feature and Scale Invariant Feature Transform will be discussed. A model of Template matching followed by Gabor Feature is proposed and implemented for the pre-processing of the images. To automate the process, we look into incorporating neural networks into the system as neural networks functions in a way that resembles the human brain. 3 neural networks were discussed in detail, namely Multilayer Perceptron, Radial basis function networks and Self-Organizing map. The proposed model is to use Self-Organizing Map to organized the input into clusters and uses it as an unsupervised tool to achieve our objective. Bachelor of Engineering (Computer Engineering) 2010-04-30T01:29:35Z 2010-04-30T01:29:35Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/36269 en Nanyang Technological University 49 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
spellingShingle DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Wong, Kenneth Yang Fei.
A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
description In March 2003, a severe respiratory disease have broke out in Singapore causing a total of 238 infected and 33 died, implying a fatality rate of 14%. One of the measures put in place to prevent the spread of the disease in Singapore is the employment of thermograph cameras at access points of Singapore to screen people going in and out of Singapore for fever conditions. This report looks into the technology of thermography from its basis to how it is widely used in medical fields to aid in early diagnosis of diseases like breast cancer. The objective of this final year project is to look into the possibility of creating an automated fever detection system that can aid medical staffs in times of emergency where false-negative detection of fever cases can result in very dire consequences. As we are given a set of data in the form of thermographs, image pre-processing has to be done on the image to extract the features of interest. Image segmentation methods such as Histogram-Based, Edge Detection, Template matching and Region Growing and feature extraction methods such as Gabor Feature and Scale Invariant Feature Transform will be discussed. A model of Template matching followed by Gabor Feature is proposed and implemented for the pre-processing of the images. To automate the process, we look into incorporating neural networks into the system as neural networks functions in a way that resembles the human brain. 3 neural networks were discussed in detail, namely Multilayer Perceptron, Radial basis function networks and Self-Organizing map. The proposed model is to use Self-Organizing Map to organized the input into clusters and uses it as an unsupervised tool to achieve our objective.
author2 Lau Chiew Tong
author_facet Lau Chiew Tong
Wong, Kenneth Yang Fei.
format Final Year Project
author Wong, Kenneth Yang Fei.
author_sort Wong, Kenneth Yang Fei.
title A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
title_short A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
title_full A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
title_fullStr A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
title_full_unstemmed A cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
title_sort cognitive interpretation of thermograhic images using novel fuzzy learning semantic memories
publishDate 2010
url http://hdl.handle.net/10356/36269
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