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Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers

The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Po...

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Main Authors: Muhammad 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) 2013
Subjects:
Biomedical image processing
Mycobacterium tuberculosis detection
Tissue section
K-nearest neighbour
Fuzzy k-nearest neighbour
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/29377
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spelling my.unimap-293772013-10-30T14:51:58Z Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers Muhammad Khusairi, Osman Mohd Yusoff, Mashor, Prof. Dr. Hasnan, Jaafar khusairi@ppinang.uitm.edu.my hasnan@kb.usm.my usoff@unimap.edu.my Biomedical image processing Mycobacterium tuberculosis detection Tissue section K-nearest neighbour Fuzzy k-nearest neighbour The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia. Early detection and treatment are the most promising way to increase a patient's chance of survival from TB disease, reduce the duration and cost of treatment, and prevent the disease from spreading. Currently, microscopic examination of clinical specimens by medical technologists is the most widely used for TB screening and diagnosis. Unfortunately, the process is tedious, timeconsuming and error-prone. This paper describes a method for automated TB detection from tissue sections using image processing techniques and artificial intelligence. The proposed work consists of three stages; image segmentation, features extraction and classification. Tissue slide images are acquired using a digital camera attached to a light microscope. Then, k-mean clustering and thresholding techniques are applied for image segmentation. The segmented regions are further classified into three classes; ‘TB’, ‘overlapped TB’ and ‘non-TB’. A set of six geometrical features; area, perimeter, shape factor, minimum and maximum distance of a pixel in the boundary from the centroid, and eccentricity, are calculated from the segmented regions to describe their shape properties. Finally, k-nearest neighbour (kNN) and fuzzy k-nearest neighbour (fuzzy kNN) classifiers are used to classify the feature vectors. The experimental results suggested that the kNN classifier performed slightly better than the fuzzy KNN in classifying the TB bacilli. 2013-10-30T14:51:58Z 2013-10-30T14:51:58Z 2012-06-18 Working Paper p. 528-534 978-967-5760-11-2 http://hdl.handle.net/123456789/29377 en Proceedings of The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012); Universiti Malaysia Perlis (UniMAP)
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 Biomedical image processing
Mycobacterium tuberculosis detection
Tissue section
K-nearest neighbour
Fuzzy k-nearest neighbour
spellingShingle Biomedical image processing
Mycobacterium tuberculosis detection
Tissue section
K-nearest neighbour
Fuzzy k-nearest neighbour
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
description The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia.
author2 khusairi@ppinang.uitm.edu.my
author_facet khusairi@ppinang.uitm.edu.my
Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
format Working Paper
author Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
author_sort Muhammad Khusairi, Osman
title Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
title_short Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
title_full Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
title_fullStr Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
title_full_unstemmed Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
title_sort detection of mycobacterium tuberculosis in tissue using k-nearest neighbour and fuzzy k-nearest neighbour classifiers
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/29377
_version_ 1643795577307136000
score 13.214268

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