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Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network

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.

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Bibliographic Details
Main Authors: Mohammad Khusairi, Osman, Mohd Yusoff, Mashor, Prof. Dr., Hasnan, Jaafar
Other Authors: khusairi@ppinang.uitm.edu.my
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2012
Subjects:
Tuberculosis (TB)
Tubercle bacilli
Ziehl-Neelsen stain
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21726
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spelling my.unimap-217262012-11-10T04:52:55Z Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network Mohammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. Hasnan, Jaafar khusairi@ppinang.uitm.edu.my hasnan@kb.usm.my Tuberculosis (TB) Tubercle bacilli Ziehl-Neelsen stain 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. 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%. 2012-11-10T04:52:55Z 2012-11-10T04:52:55Z 2010-10-16 Working Paper 978-967-5760-03-7 http://hdl.handle.net/123456789/21726 en Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010) Universiti Malaysia Perlis (UniMAP) Centre for Graduate Studies
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Tuberculosis (TB)
Tubercle bacilli
Ziehl-Neelsen stain
spellingShingle Tuberculosis (TB)
Tubercle bacilli
Ziehl-Neelsen stain
Mohammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network
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.
author2 khusairi@ppinang.uitm.edu.my
author_facet khusairi@ppinang.uitm.edu.my
Mohammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
format Working Paper
author Mohammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
author_sort Mohammad Khusairi, Osman
title Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network
title_short Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network
title_full Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network
title_fullStr Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network
title_full_unstemmed Detection of tubercle bacilli in Ziehl-Neelsen stained tissue slide images using Hu’s moment invariants and artificial neural network
title_sort detection of tubercle bacilli in ziehl-neelsen stained tissue slide images using hu’s moment invariants and artificial neural network
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21726
_version_ 1643793419968970752
score 13.160551

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