Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/29377
Title: Detection of mycobacterium tuberculosis in tissue using k-Nearest neighbour and fuzzy k-Nearest neighbour classifiers
Authors: Muhammad Khusairi, Osman
Mohd Yusoff, Mashor, Prof. Dr.
Hasnan, Jaafar
khusairi@ppinang.uitm.edu.my
hasnan@kb.usm.my
usoff@unimap.edu.my
Keywords: Biomedical image processing
Mycobacterium tuberculosis detection
Tissue section
K-nearest neighbour
Fuzzy k-nearest neighbour
Issue Date: 18-Jun-2012
Publisher: Universiti Malaysia Perlis (UniMAP)
Citation: p. 528-534
Series/Report no.: Proceedings of The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012);
Abstract: 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.
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.
URI: http://dspace.unimap.edu.my/123456789/29377
ISBN: 978-967-5760-11-2
Appears in Collections:Conference Papers
Mohd Yusoff Mashor, Prof. Dr.

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