Statistical methods for computer aided diagnosis in chest radiography / Hossein Ebrahimian
This study was done to create a semi automatic procedure for the discrimination of three different lung diseases using chest radiograph. The statistical discrimination procedure make use of phase congruency and texture measures as features for discrimination. Initially, a literature review was carr...
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| Format: | Thesis |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/4382/1/MsC_Dissertation_Hossein_Ebrahimian.pdf http://studentsrepo.um.edu.my/4382/ |
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| Institution: | Universiti Malaya |
| Summary: | This study was done to create a semi automatic procedure for the discrimination of three different lung diseases using chest radiograph. The statistical discrimination procedure make use of phase congruency and texture measures as features for discrimination.
Initially, a literature review was carried out which showed that phase congruency in the proposed discrimination procedure has not been attempted before. The cases studied are chest X-ray films collected from the Institute of Respiratory Medicine, Kuala Lumpur, which were digitized into DICOM format before extracting the relevant imaging data.
This study continues in three independent parts. Firstly, the region of infection (ROI) for all four cases including normal lung (NL), lung cancer (LC), lobar pneumonia (PNEU) and pulmonary tuberculosis (PTB) was detected in a novel application of statistical moments. Secondly, the ROI in the original pixel form were transformed to the corresponding phase congruency value. The ability of phase congruency as a feature for discrimination was then investigated. The texture measures of phase congruency values that were shown to have univariate normal distributions were used as a global feature for discrimination.
The final choice of features for discrimination was decided after a Receiver Operating Characteristics (ROC) analysis. Energy, contrast and homogeneity were shown to be suitable candidate for feature vectors.
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Consequently, the semi automatic procedure to find the ROI and the corresponding discrimination procedure are combined to develop a prototype computer aided diagnosis (CAD) system. The construction of this CAD system will allow the methods and procedures in this study to be verified by the radiologists and medical practitioner. |
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