An automatic segmentation and classification framework for anti-nuclear antibody images

Autoimmune disease is a disorder of immune system due to the over-reaction of lymphocytes against one's own body tissues. Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self body tissues or cells, which plays an important role in the diagnosis...

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Main Authors:	Cheng, Chung-Chuan, Hsieh, Tsu-Yi, Taur, Jin-Shiuh, Chen, Yung-Fu
Format:	Online
Language:	English
Published:	BioMed Central 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029490/

id	pubmed-4029490
recordtype	oai_dc
spelling	pubmed-40294902014-06-17 An automatic segmentation and classification framework for anti-nuclear antibody images Cheng, Chung-Chuan Hsieh, Tsu-Yi Taur, Jin-Shiuh Chen, Yung-Fu Research Autoimmune disease is a disorder of immune system due to the over-reaction of lymphocytes against one's own body tissues. Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self body tissues or cells, which plays an important role in the diagnosis of autoimmune diseases. Indirect ImmunoFluorescence (IIF) method with HEp-2 cells provides the major screening method to detect ANA for the diagnosis of autoimmune diseases. Fluorescence patterns at present are usually examined laboriously by experienced physicians through manually inspecting the slides with the help of a microscope, which usually suffers from inter-observer variability that limits its reproducibility. Previous researches only provided simple segmentation methods and criterions for cell segmentation and recognition, but a fully automatic framework for the segmentation and recognition of HEp-2 cells had never been reported before. This study proposes a method based on the watershed algorithm to automatically detect the HEp-2 cells with different patterns. The experimental results show that the segmentation performance of the proposed method is satisfactory when evaluated with percent volume overlap (PVO: 89%). The classification performance using a SVM classifier designed based on the features calculated from the segmented cells achieves an average accuracy of 96.90%, which outperforms other methods presented in previous studies. The proposed method can be used to develop a computer-aided system to assist the physicians in the diagnosis of auto-immune diseases. BioMed Central 2013-12-09 /pmc/articles/PMC4029490/ /pubmed/24565042 http://dx.doi.org/10.1186/1475-925X-12-S1-S5 Text en Copyright © 2013 Cheng et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Cheng, Chung-Chuan Hsieh, Tsu-Yi Taur, Jin-Shiuh Chen, Yung-Fu
spellingShingle	Cheng, Chung-Chuan Hsieh, Tsu-Yi Taur, Jin-Shiuh Chen, Yung-Fu An automatic segmentation and classification framework for anti-nuclear antibody images
author_facet	Cheng, Chung-Chuan Hsieh, Tsu-Yi Taur, Jin-Shiuh Chen, Yung-Fu
author_sort	Cheng, Chung-Chuan
title	An automatic segmentation and classification framework for anti-nuclear antibody images
title_short	An automatic segmentation and classification framework for anti-nuclear antibody images
title_full	An automatic segmentation and classification framework for anti-nuclear antibody images
title_fullStr	An automatic segmentation and classification framework for anti-nuclear antibody images
title_full_unstemmed	An automatic segmentation and classification framework for anti-nuclear antibody images
title_sort	automatic segmentation and classification framework for anti-nuclear antibody images
description	Autoimmune disease is a disorder of immune system due to the over-reaction of lymphocytes against one's own body tissues. Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self body tissues or cells, which plays an important role in the diagnosis of autoimmune diseases. Indirect ImmunoFluorescence (IIF) method with HEp-2 cells provides the major screening method to detect ANA for the diagnosis of autoimmune diseases. Fluorescence patterns at present are usually examined laboriously by experienced physicians through manually inspecting the slides with the help of a microscope, which usually suffers from inter-observer variability that limits its reproducibility. Previous researches only provided simple segmentation methods and criterions for cell segmentation and recognition, but a fully automatic framework for the segmentation and recognition of HEp-2 cells had never been reported before. This study proposes a method based on the watershed algorithm to automatically detect the HEp-2 cells with different patterns. The experimental results show that the segmentation performance of the proposed method is satisfactory when evaluated with percent volume overlap (PVO: 89%). The classification performance using a SVM classifier designed based on the features calculated from the segmented cells achieves an average accuracy of 96.90%, which outperforms other methods presented in previous studies. The proposed method can be used to develop a computer-aided system to assist the physicians in the diagnosis of auto-immune diseases.
publisher	BioMed Central
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4029490/
_version_	1612091701883568128

An automatic segmentation and classification framework for anti-nuclear antibody images

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