NDLI: Tuberculosis bacilli detection in Ziehl-Neelsen-stained tissue using affine moment invariants and Extreme Learning Machine

Content Provider	IEEE Xplore Digital Library
Author	Osman, M.K. Mashor, M.Y. Jaafar, H.
Copyright Year	2011
Description	Author affiliation: Department of Pathology, School of Medical Science, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia (Jaafar, H.) \|\| Electronic & Biomedical Intelligent Systems (EBItS), Research Group, School of Mechatronic Engineering, Universiti Malaysia Perlis, Ulu Pauh Main Campus, 02600 Arau, Malaysia (Mashor, M.Y.) \|\| Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM) Malaysia (Osman, M.K.)
Abstract	This paper describes an approach to automate the detection and classification of tuberculosis (TB) bacilli in tissue section using image processing technique and feedforward neural network trained by Extreme Learning Machine. It aims to assist pathologists in TB diagnosis and give an alternative to the conventional manual screening process, which is time-consuming and labour-intensive. Images are captured from Ziehl-Neelsen (ZN) stained tissue slides using light microscope as it is commonly used approach for diagnosis of TB. Then colour image segmentation is used to locate the regions correspond to the bacilli. After that, affine moment invariants are extracted to represent the segmented regions. These features are invariant under rotation, scale and translation, thus useful to represent the bacilli. Finally, a single layer feedforward neural network (SLFNN) trained by Extreme Learning Machine (ELM) is used to detect and classify the features into three classes: ‘TB’, ‘overlapped TB’ and ‘non-TB’. The results indicate that the ELM gives acceptable classification performance with shorter training period compared to the standard backpropagation training algorithms.
Starting Page	232
Ending Page	236
File Size	879610
Page Count	5
File Format	PDF
ISBN	9781612844145
e-ISBN	9781612844138
DOI	10.1109/CSPA.2011.5759878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-04
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image segmentation Image color analysis tissue sections Extreme Learning Machine Microscopy Tuberculosis bacilli detection Signal processing algorithms Machine learning affine moment invariants Classification algorithms neural network
Content Type	Text
Resource Type	Article

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