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: | |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| 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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| Summary: | 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. |
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