Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network

Tuberculosis (TB) is a disease cause by infection with bacteria called Mycobacterium tuberculosis. The bacteria usually attack the lungs, resulting in pulmonary TB (PTB), but they can also affect other parts of the human body, referred as extra-pulmonary TB (EPTB). The disease can be cured in mos...

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Main Author:	Muhammad Khusairi, Osman
Format:	Thesis
Language:	English
Subjects:	Tuberculosis diagnosis Tuberculosis (TB) Hybrid multilayered perceptron (HMLP) network Image processing
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/2/full%20text.pdf
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id	my-unimap-44203
record_format	uketd_dc
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Prof. Dr. Mohdd Yusoff Mashor
topic	Tuberculosis diagnosis Tuberculosis (TB) Hybrid multilayered perceptron (HMLP) network Image processing
spellingShingle	Tuberculosis diagnosis Tuberculosis (TB) Hybrid multilayered perceptron (HMLP) network Image processing Muhammad Khusairi, Osman Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network
description	Tuberculosis (TB) is a disease cause by infection with bacteria called Mycobacterium tuberculosis. The bacteria usually attack the lungs, resulting in pulmonary TB (PTB), but they can also affect other parts of the human body, referred as extra-pulmonary TB (EPTB). The disease can be cured in most people with appropriate antibiotic treatment. Unfortunately, it remains a major public health problem and the second leading cause of death worldwide among the infectious diseases. Early diagnosis of the disease is essential for effective patient treatment and preventing TB transmission in the community. Direct microscopy of sputum smear and tissue sections are the most widely used test for the PTB and EPTB diagnosis respectively. However, the diagnosis method is time-consuming, tedious, labour-intensive and has low sensitivity. Research on computer-aided detection and diagnosis of PTB that aims to overcome the problems of manual screening has attracted a lot of attentions recently. However, very little attention has been focused on the EPTB disease. Thus, this research proposes a method for automatic detection of TB bacilli in tissue sections for EPTB diagnosis. A procedure of image segmentation is proposed to segment Ziehl-Neelsen (ZN) stained tissue images into TB bacilli and background regions. The procedure combines the thresholding and clustering algorithms, and utilizes the C-Y colour model to perform the segmentation process. A local adaptive thresholding is also proposed to improve the results of segmentation. In the feature extraction stage, the research considers three types of features namely simple shape descriptors, colour information and region-based moment invariants to represent the segmented objects and used in classifying the objects. In addition, this research also proposes the skeleton-based moment invariants to improve the classification performance of TB bacilli using the conventional region-based moment invariants. Three types of skeleton-based moment invariants namely skeletonbased Hu’s, Zernike’s and affine moment invariants are formulated. In the classification stage, this research proposes to use HMLP network for detection and classification of TB bacilli. A multiclass HMLP network is designed for simplicity of training process and replacing the hierarchical HMLP (HP 2 PMLP) network. The research also rederives three activation functions; hyperbolic tangent, adaptive sigmoid and adaptive hyperbolic tangent activation function to suit for training HMLP network and replace the commonly used sigmoid functions. In addition, three types of training algorithms namely Levenberg-Marquardt (LM), modified extreme learning machine (MELM) and hybrid modified prediction error-modified extreme learning machine (MRPE-MELM) algorithms are rederived for HMLP network to improve the network performance. Finally, a software that utilized the best of above mentioned methods is proposed to perform the automated TB bacilli detection. Overall, the software provides good ability in detecting and classifying TB bacilli. The software manages to achieve promising accuracy, sensitivity and specificity of 84.25%, 85.64% and 86.98% respectively. The software also showed a high sensitivity on TB-positive images when evaluated using the image-based analysis. These findings suggest the potential use of this software in diagnosing EPTB disease, especially for patients with highly suspected EPTB infection.
format	Thesis
author	Muhammad Khusairi, Osman
author_facet	Muhammad Khusairi, Osman
author_sort	Muhammad Khusairi, Osman
title	Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network
title_short	Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network
title_full	Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network
title_fullStr	Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network
title_full_unstemmed	Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network
title_sort	computer-aided extra-pulmonary tuberculosis diagnosis using image processing and hmlp network
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/1/p.1-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/2/full%20text.pdf
_version_	1747836829935075328
spelling	my-unimap-442032016-11-29T08:54:39Z Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network Muhammad Khusairi, Osman Prof. Dr. Mohdd Yusoff Mashor Tuberculosis (TB) is a disease cause by infection with bacteria called Mycobacterium tuberculosis. The bacteria usually attack the lungs, resulting in pulmonary TB (PTB), but they can also affect other parts of the human body, referred as extra-pulmonary TB (EPTB). The disease can be cured in most people with appropriate antibiotic treatment. Unfortunately, it remains a major public health problem and the second leading cause of death worldwide among the infectious diseases. Early diagnosis of the disease is essential for effective patient treatment and preventing TB transmission in the community. Direct microscopy of sputum smear and tissue sections are the most widely used test for the PTB and EPTB diagnosis respectively. However, the diagnosis method is time-consuming, tedious, labour-intensive and has low sensitivity. Research on computer-aided detection and diagnosis of PTB that aims to overcome the problems of manual screening has attracted a lot of attentions recently. However, very little attention has been focused on the EPTB disease. Thus, this research proposes a method for automatic detection of TB bacilli in tissue sections for EPTB diagnosis. A procedure of image segmentation is proposed to segment Ziehl-Neelsen (ZN) stained tissue images into TB bacilli and background regions. The procedure combines the thresholding and clustering algorithms, and utilizes the C-Y colour model to perform the segmentation process. A local adaptive thresholding is also proposed to improve the results of segmentation. In the feature extraction stage, the research considers three types of features namely simple shape descriptors, colour information and region-based moment invariants to represent the segmented objects and used in classifying the objects. In addition, this research also proposes the skeleton-based moment invariants to improve the classification performance of TB bacilli using the conventional region-based moment invariants. Three types of skeleton-based moment invariants namely skeletonbased Hu’s, Zernike’s and affine moment invariants are formulated. In the classification stage, this research proposes to use HMLP network for detection and classification of TB bacilli. A multiclass HMLP network is designed for simplicity of training process and replacing the hierarchical HMLP (HP 2 PMLP) network. The research also rederives three activation functions; hyperbolic tangent, adaptive sigmoid and adaptive hyperbolic tangent activation function to suit for training HMLP network and replace the commonly used sigmoid functions. In addition, three types of training algorithms namely Levenberg-Marquardt (LM), modified extreme learning machine (MELM) and hybrid modified prediction error-modified extreme learning machine (MRPE-MELM) algorithms are rederived for HMLP network to improve the network performance. Finally, a software that utilized the best of above mentioned methods is proposed to perform the automated TB bacilli detection. Overall, the software provides good ability in detecting and classifying TB bacilli. The software manages to achieve promising accuracy, sensitivity and specificity of 84.25%, 85.64% and 86.98% respectively. The software also showed a high sensitivity on TB-positive images when evaluated using the image-based analysis. These findings suggest the potential use of this software in diagnosing EPTB disease, especially for patients with highly suspected EPTB infection. Universiti Malaysia Perlis (UniMAP) 2014-04-09 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/44203 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/1/p.1-24.pdf 9539d7012f26503f8a756650cb63dc72 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/2/full%20text.pdf 8bf4269e25a0c9cceb90430f983d416e http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/44203/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Tuberculosis diagnosis Tuberculosis (TB) Hybrid multilayered perceptron (HMLP) network Image processing School of Mechatronic Engineering

Computer-aided extra-pulmonary tuberculosis diagnosis using image processing and HMLP network

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