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Permanent link (DOI): https://doi.org/10.7939/R3BD6C

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Intelligent CAD System for Infectious TB Detection on Chest Radiographs Open Access

Descriptions

Other title
Subject/Keyword
Chest radiograph (CXR)
Tuberculosis (TB)
Computer-aided detection (CAD)
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Xu, Tao
Supervisor and department
Cheng, Irene (Computing Science)
Mandal, Mrinal (Electrical and Computer Engineering)
Examining committee member and department
Bajic, Ivan (School of Engineering Science at Simon Fraser University)
Cheng, Irene (Computing Science)
Zemp, Roger (Electrical and Computer Engineering)
Zhao, Vicky (Electrical and Computer Engineering)
Mandal, Mrinal (Electrical and Computer Engineering)
Evoy, Stephane (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Biomedical Engineering
Date accepted
2013-05-09T16:05:45Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Computer aided detection (CAD) or diagnosis (CADx) is rapidly entering the radiology mainstream due to the conversion from film-based to digital radiographic systems and the advances in computerized image analysis techniques over the past decades. However, little CAD work in chest radiology has been done beyond lung nodules. Our research focuses on developing an intelligent CAD system for automated detection of infectious tuberculosis (TB), which has typical radiographic features such as cavity and acinar shadows. In this thesis, I first present a general conceptual framework of the CAD system consisting of several steps, such as image preprocessing, feature extraction and classification, and final decision analysis. I then propose an efficient technique for automatic lung field segmentation using edge-region force guided active shape model (ERF-ASM) which is an important preprocessing step in the CAD system. A coarse-to-fine dual scale (CFDS) feature classification technique is then proposed for TB cavity detection. In this technique, Gaussian-model-based template matching (GTM), local binary pattern (LBP) and histogram of oriented gradients (HOG) based features are applied at the coarse scale; while circularity, gradient inverse coefficient of variation (GICOV) and Kullback- Leibler divergence (KLD) measures are applied at the fine scale. Finally, a hybrid system using combined LBP, HOG and grey level co-occurrence matrix (GLCM) based features is proposed for acinar shadows detection. Experiments over 300 chest radiographs show promising results of the proposed techniques.
Language
English
DOI
doi:10.7939/R3BD6C
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
T. Xu, M. Mandal, R. Long, et al, “An edge-region force guided active shape approach for automatic lung field detection in chest radiographs,” Computerized Medical Imaging and Graphics, 2012, 36(6):452-463.T. Xu, I. Cheng, R. Long, et al, “Novel coarse-to-fine dual scale technique for tuberculosis cavity detection in chest radiographs,” EURASIP Journal on Image and Video Processing, 2013, 2013:3.

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