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Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans

Texture feature is an important feature analysis method in computer-aided diagnosis systems for disease diagnosis. However, texture feature itself could not provide an overall description of the diseases. In this paper, we propose Continuous Local Feature (CLH) to diagnose the Bronchiolitis Obl...

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Main Author: Saipullah, Khairul Muzzammil
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utem.edu.my/id/eprint/4098/1/Continuous_Local_Histogram_Descriptor_For_Diagnosis_of_Bronchiolitis_Obliterans.pdf
http://eprints.utem.edu.my/id/eprint/4098/
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6122138&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6112287%2F6122068%2F06122138.pdf%3Farnumber%3D6122138
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spelling my.utem.eprints.40982015-05-28T02:39:51Z http://eprints.utem.edu.my/id/eprint/4098/ Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans Saipullah, Khairul Muzzammil TA Engineering (General). Civil engineering (General) Texture feature is an important feature analysis method in computer-aided diagnosis systems for disease diagnosis. However, texture feature itself could not provide an overall description of the diseases. In this paper, we propose Continuous Local Feature (CLH) to diagnose the Bronchiolitis Obliterans (BO) lung diseases in the chest computer tomography images. CLH is based on the continuous combination of histograms of local texture feature, local shape feature, and the brightness feature. Because CLH extracts more information, it has high discriminating power and is able to classify between the BO lung disease and normal lung region effectively. The experimental results in classifying between BO and normal lung region show that CLH achieves 98.15% of average sensitivity whereas Local Binary Patterns and Gray Level Run Length Matrix achieve 73% and 75.8% of average sensitivities, respectively. In the receiver operating curve analysis, CLH archives 0.9 of area under curve (AUC) whereas LBP and GLRLM achieve 0.78 and 0.86 of AUCs. 2011-12-05 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/4098/1/Continuous_Local_Histogram_Descriptor_For_Diagnosis_of_Bronchiolitis_Obliterans.pdf Saipullah, Khairul Muzzammil (2011) Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans. In: IEEE Hybrid Intelligence System 2011 (HIS2011), 5-8 December 2011, Ayer keroh, Melaka. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6122138&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6112287%2F6122068%2F06122138.pdf%3Farnumber%3D6122138
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Saipullah, Khairul Muzzammil
Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans
description Texture feature is an important feature analysis method in computer-aided diagnosis systems for disease diagnosis. However, texture feature itself could not provide an overall description of the diseases. In this paper, we propose Continuous Local Feature (CLH) to diagnose the Bronchiolitis Obliterans (BO) lung diseases in the chest computer tomography images. CLH is based on the continuous combination of histograms of local texture feature, local shape feature, and the brightness feature. Because CLH extracts more information, it has high discriminating power and is able to classify between the BO lung disease and normal lung region effectively. The experimental results in classifying between BO and normal lung region show that CLH achieves 98.15% of average sensitivity whereas Local Binary Patterns and Gray Level Run Length Matrix achieve 73% and 75.8% of average sensitivities, respectively. In the receiver operating curve analysis, CLH archives 0.9 of area under curve (AUC) whereas LBP and GLRLM achieve 0.78 and 0.86 of AUCs.
format Conference or Workshop Item
author Saipullah, Khairul Muzzammil
author_facet Saipullah, Khairul Muzzammil
author_sort Saipullah, Khairul Muzzammil
title Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans
title_short Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans
title_full Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans
title_fullStr Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans
title_full_unstemmed Continuous Local Histogram Descriptor For Diagnosis of Bronchiolitis Obliterans
title_sort continuous local histogram descriptor for diagnosis of bronchiolitis obliterans
publishDate 2011
url http://eprints.utem.edu.my/id/eprint/4098/1/Continuous_Local_Histogram_Descriptor_For_Diagnosis_of_Bronchiolitis_Obliterans.pdf
http://eprints.utem.edu.my/id/eprint/4098/
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6122138&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F6112287%2F6122068%2F06122138.pdf%3Farnumber%3D6122138
_version_ 1665905270807592960
score 13.211869

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