eprintid: 8367
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/83/67
datestamp: 2023-11-09 16:20:16
lastmod: 2023-11-09 16:20:16
status_changed: 2023-11-09 16:12:28
type: conference_item
metadata_visibility: show
creators_name: Rosdi, N.T.A.M.
creators_name: May, Z.
creators_name: Faye, I.
creators_name: Nasir, M.H.
title: Hep-2 cell feature extraction using wavelet and independent component analysis
ispublished: pub
keywords: Antibodies; Cells; Classification (of information); Cytology; Discrete wavelet transforms; Extraction; Feature extraction; Graphical user interfaces; Image processing; Industrial electronics; MATLAB; Support vector machines; User interfaces; Wavelet transforms, 2-d discrete wavelet transforms; Anti-nuclear Antibody (ANA); Graphical user interfaces (GUI); Hep-2 cells; Independent component analysis(ICA); Indirect immunofluorescence; Matlab- software; Wavelet, Independent component analysis
note: cited By 1; Conference of 2014 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2014 ; Conference Date: 28 September 2014 Through 1 October 2014; Conference Code:130936
abstract: Human antibodies work to attack any diseases or bacteria that presented inside the body. However, there is an act when human antibodies tend to attack own body cells or tissues which is called as Anti-nuclear Antibodies (ANA). ANA consist of many different types that can be recognized by its nucleus size and shape. Common method of classifying ANA is by performing Indirect Immunofluorescences (IIF) with HEp-2 cell and observed the pattern under the microscope by naked eye which said to be inaccurate, takes time and subjective. Thus, this project will study on the technique to identify and classify the pattern of ANA automatically. Algorithms are created using MATLAB software and a Graphical User Interface (GUI) is generated for the algorithm to be easily used. This work will focus more on feature extraction using Wavelet and Independent Component Analysis (ICA). The type of Wavelet Transform that will be used is the 2D Discrete Wavelet Transform (2D DWT) and Fast ICA for Independent Component Analysis. Then Support Vector Machine (SVM) is used to perform the classifications parts using the features extracted from both methods. Different features obtained are tested in SVM and the performance of both methods is compared. From the result, it shows that by using the same classifier, Wavelet can provide better features for classification compared to ICA. © 2014 IEEE.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032829878&doi=10.1109%2fISIEA.2014.8049868&partnerID=40&md5=4be55d8a508a4b5734b7f14f3accfa43
id_number: 10.1109/ISIEA.2014.8049868
full_text_status: none
publication: ISIEA 2014 - 2014 IEEE Symposium on Industrial Electronics and Applications
pagerange: 36-41
refereed: TRUE
isbn: 9781479955909
citation:   Rosdi, N.T.A.M. and May, Z. and Faye, I. and Nasir, M.H.  (2017) Hep-2 cell feature extraction using wavelet and independent component analysis.  In: UNSPECIFIED.