Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images

Human Epithelial type 2 (HEp-2) images are important in detecting the antinuclear autoantibody (ANA) in diagnosis of autoimmune disease in human body. Generally, HEp-2 cells can be classified into six main patterns, namely Centromere, Nucleolar, Homogeneous, Cytoplasmic, Fine Speckled and Coarse...

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Main Author:	Khaw, Wil Bond
Format:	Thesis
Language:	English
Published:	2017
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/39407/1/Khaw_Wil_Bond_24_Pages.pdf
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spelling	my-usm-ep.394072019-04-12T05:25:06Z Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images 2017 Khaw, Wil Bond TK1-9971 Electrical engineering. Electronics. Nuclear engineering Human Epithelial type 2 (HEp-2) images are important in detecting the antinuclear autoantibody (ANA) in diagnosis of autoimmune disease in human body. Generally, HEp-2 cells can be classified into six main patterns, namely Centromere, Nucleolar, Homogeneous, Cytoplasmic, Fine Speckled and Coarse Speckled. However, in current technology, HEp-2 images can only be analysed manually by indirect immunofluorescence (IIF) test. The result of IIF test has very high variability and very dependent on the experience of physicists. Therefore, digitalize the IIF test becomes the new interest to researchers as well as in this research, where segmentation and features extraction of HEp-2 images will be focused. In segmentation of HEp-2 images, the current state-of-the-art techniques failed to provide a satisfied segmented result. Therefore, a combination of two conventional methods (i.e. Fuzzy C-Means (FCM) clustering and thresholding) has been proposed in this study. From the result, the segmented images are smoother, more consistent and with lesser noises compared to other state-of-the-art methods. In feature extraction stage, this study proposes to extract five features, which are Contrast, Energy, Correlation, Homogeneity, and Entropy. Based on the results obtained, the five proposed features can successfully differentiate the staining patterns of HEp-2 cells. In short, the proposed methods in this research have high capability to be introduced in hospital for detection of HEp-2 images for xix autoimmune disease. The proposed method has been proven with higher accuracy which can reduce the shortcoming of the existing IIF test. 2017 Thesis http://eprints.usm.my/39407/ http://eprints.usm.my/39407/1/Khaw_Wil_Bond_24_Pages.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Khaw, Wil Bond Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images
description	Human Epithelial type 2 (HEp-2) images are important in detecting the antinuclear autoantibody (ANA) in diagnosis of autoimmune disease in human body. Generally, HEp-2 cells can be classified into six main patterns, namely Centromere, Nucleolar, Homogeneous, Cytoplasmic, Fine Speckled and Coarse Speckled. However, in current technology, HEp-2 images can only be analysed manually by indirect immunofluorescence (IIF) test. The result of IIF test has very high variability and very dependent on the experience of physicists. Therefore, digitalize the IIF test becomes the new interest to researchers as well as in this research, where segmentation and features extraction of HEp-2 images will be focused. In segmentation of HEp-2 images, the current state-of-the-art techniques failed to provide a satisfied segmented result. Therefore, a combination of two conventional methods (i.e. Fuzzy C-Means (FCM) clustering and thresholding) has been proposed in this study. From the result, the segmented images are smoother, more consistent and with lesser noises compared to other state-of-the-art methods. In feature extraction stage, this study proposes to extract five features, which are Contrast, Energy, Correlation, Homogeneity, and Entropy. Based on the results obtained, the five proposed features can successfully differentiate the staining patterns of HEp-2 cells. In short, the proposed methods in this research have high capability to be introduced in hospital for detection of HEp-2 images for xix autoimmune disease. The proposed method has been proven with higher accuracy which can reduce the shortcoming of the existing IIF test.
format	Thesis
qualification_level	Master's degree
author	Khaw, Wil Bond
author_facet	Khaw, Wil Bond
author_sort	Khaw, Wil Bond
title	Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images
title_short	Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images
title_full	Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images
title_fullStr	Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images
title_full_unstemmed	Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images
title_sort	segmentation of region of interest and extraction of significant features for hep-2 images
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2017
url	http://eprints.usm.my/39407/1/Khaw_Wil_Bond_24_Pages.pdf
_version_	1747820748864487424

Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images

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