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|Title: ||Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network|
|Authors: ||Mohammad Khusairi, Osman|
Mohd Yusoff, Mashor, Prof. Dr.
|Keywords: ||Tuberculosis (TB);Tubercle bacilli;Ziehl-Neelsen stain|
|Issue Date: ||16-Oct-2010|
|Publisher: ||Universiti Malaysia Perlis (UniMAP)|
|???metadata.dc.publisher.department???: ||Centre for Graduate Studies|
|Series/Report no.: ||Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010)|
|Abstract: ||Early detection of tuberculosis infection is the key to
successful treatment and control of the disease.
Manual screening by light microscopy is the most
widely used for tubercle bacilli detection but it is time consuming
and labour-intensive process. This paper
describes a method using image processing and neural
network for automated tubercle bacilli detection in
tissues. The proposed work consists of three main
stages: image segmentation, feature extraction and
identification. First, images of Ziehl-Neelsen stained
tissue slides are acquired using a digital camera
attached to a light microscope. To isolate tubercle
bacilli from its background, moving k-mean clustering
that uses C-Y colour information is used. Then, seven
Hu’s moment invariants are extracted as features to
represent the bacilli. Finally, based on the input
features, multilayer perceptron network is used to
classify into two classes: ‘true TB’ and ‘possible TB’.
Six types of training algorithms are used to evaluate
the network performance. Experimental results
demonstrated that the MLP network trained by
Levenberg-Marquardt has achieved the highest
accuracy with percentage of 88.57%.|
|Description: ||International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.|
|Appears in Collections:||Conference Papers|
Mohd Yusoff Mashor, Prof. Dr.
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