| dc.description.abstract | 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. | en_US |
