Discriminant functions and multi-resolution analysis (MRA) for disease detection

Problems associated with the detection of diseases in their early stage are well known when using chest radiograph images. A graphical method involving wavelet coefficients as the feature vector (WFV) has been proposed for the detection and discrimination of Mycobacterium Tuberculosis (MTB) and lung...

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Main Authors: Rijal, Omar Mohd, Noor, Norliza Mohd, Hussin, A, Ling, Ong E
Format: Article
Published: WSEAS 2006
Subjects:
Online Access:http://eprints.utm.my/7686/
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author Rijal, Omar Mohd
Noor, Norliza Mohd
Hussin, A
Ling, Ong E
author_facet Rijal, Omar Mohd
Noor, Norliza Mohd
Hussin, A
Ling, Ong E
author_sort Rijal, Omar Mohd
building UTeM Institutional Repository
collection Online Access
description Problems associated with the detection of diseases in their early stage are well known when using chest radiograph images. A graphical method involving wavelet coefficients as the feature vector (WFV) has been proposed for the detection and discrimination of Mycobacterium Tuberculosis (MTB) and lung cancer (LC). In a pilot study confirmed cases showing no complications (for example a section of the lung filled with water) were studied. Further, in the pilot study the feature vector were compressed for simpler data management. However discrimination using the compressed WFV was developed to handle small sample situations. In this paper, an alternative method for larger samples sizes was employed. A control group was used to calculate the parameters of the Linear Discriminant Function (LDF(x)) and the Quadratic Discriminant Function, (QDF(x)). A separate test group was then used to calculate misclassification probabilities. The feature vector selected, that is vector x, were the average and detail vectors from the MRA of the WFV. The technique developed here allows misclassified cases to be reclassified correctly, a facility not provided for in earlier techniques.
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institution Universiti Teknologi Malaysia
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spelling utm-76862017-10-23T04:48:37Z http://eprints.utm.my/7686/ Discriminant functions and multi-resolution analysis (MRA) for disease detection Rijal, Omar Mohd Noor, Norliza Mohd Hussin, A Ling, Ong E QA Mathematics Problems associated with the detection of diseases in their early stage are well known when using chest radiograph images. A graphical method involving wavelet coefficients as the feature vector (WFV) has been proposed for the detection and discrimination of Mycobacterium Tuberculosis (MTB) and lung cancer (LC). In a pilot study confirmed cases showing no complications (for example a section of the lung filled with water) were studied. Further, in the pilot study the feature vector were compressed for simpler data management. However discrimination using the compressed WFV was developed to handle small sample situations. In this paper, an alternative method for larger samples sizes was employed. A control group was used to calculate the parameters of the Linear Discriminant Function (LDF(x)) and the Quadratic Discriminant Function, (QDF(x)). A separate test group was then used to calculate misclassification probabilities. The feature vector selected, that is vector x, were the average and detail vectors from the MRA of the WFV. The technique developed here allows misclassified cases to be reclassified correctly, a facility not provided for in earlier techniques. WSEAS 2006-05 Article PeerReviewed Rijal, Omar Mohd and Noor, Norliza Mohd and Hussin, A and Ling, Ong E (2006) Discriminant functions and multi-resolution analysis (MRA) for disease detection. WSEAS Transaction on Mathematics, 5 (5). pp. 605-608. http://www.academia.edu/12430829/Discriminant_functions_and_multi-resolution_analysis_MRA_for_disease_detection
spellingShingle QA Mathematics
Rijal, Omar Mohd
Noor, Norliza Mohd
Hussin, A
Ling, Ong E
Discriminant functions and multi-resolution analysis (MRA) for disease detection
title Discriminant functions and multi-resolution analysis (MRA) for disease detection
title_full Discriminant functions and multi-resolution analysis (MRA) for disease detection
title_fullStr Discriminant functions and multi-resolution analysis (MRA) for disease detection
title_full_unstemmed Discriminant functions and multi-resolution analysis (MRA) for disease detection
title_short Discriminant functions and multi-resolution analysis (MRA) for disease detection
title_sort discriminant functions and multi-resolution analysis (mra) for disease detection
topic QA Mathematics
url http://eprints.utm.my/7686/
http://eprints.utm.my/7686/