An automatic HEp-2 specimen analysis system based on an active contours model and an SVM classification

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Abstract

The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. Themost commonmethods to determineANAare indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic systemable to characterize IIF images. The fluorescence intensity classificationwas obtained by performing an image preprocessing phase and implementing a Support VectorMachines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.
Lingua originaleEnglish
pagine (da-a)307-1-307-21
Numero di pagine21
RivistaAPPLIED SCIENCES
Volume9
Stato di pubblicazionePublished - 2019

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Antinuclear Antibodies
systems analysis
Antibodies
Classifiers
Fluorescence
Hough transforms
Antigens
classifiers
antibodies
Image analysis
Screening
cells
fluorescence
Monitoring
ellipses
antigens
staining
Substrates
preprocessing
image analysis

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cita questo

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title = "An automatic HEp-2 specimen analysis system based on an active contours model and an SVM classification",
abstract = "The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. Themost commonmethods to determineANAare indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic systemable to characterize IIF images. The fluorescence intensity classificationwas obtained by performing an image preprocessing phase and implementing a Support VectorMachines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.",
author = "Vincenzo Taormina and Giuseppe Raso and Donato Cascio",
year = "2019",
language = "English",
volume = "9",
pages = "307--1--307--21",
journal = "Applied Sciences (Switzerland)",
issn = "2076-3417",
publisher = "Multidisciplinary Digital Publishing Institute",

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TY - JOUR

T1 - An automatic HEp-2 specimen analysis system based on an active contours model and an SVM classification

AU - Taormina, Vincenzo

AU - Raso, Giuseppe

AU - Cascio, Donato

PY - 2019

Y1 - 2019

N2 - The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. Themost commonmethods to determineANAare indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic systemable to characterize IIF images. The fluorescence intensity classificationwas obtained by performing an image preprocessing phase and implementing a Support VectorMachines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

AB - The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. Themost commonmethods to determineANAare indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic systemable to characterize IIF images. The fluorescence intensity classificationwas obtained by performing an image preprocessing phase and implementing a Support VectorMachines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentation methods, based on the Hough transform for ellipses, and on an active contours model. In order to classify the HEp-2 cells, six SVM and one k-nearest neighbors (KNN)classifiers were developed. The system was tested on a public database consisting of 2080 IIF images. Unlike almost all work presented on this topic, the proposed system automatically addresses all phases of the HEp-2 image analysis process. All results have been evaluated by comparing them with some of the most representative state-of-the-art work, demonstrating the goodness of the system in the characterization of HEp-2 images.

UR - http://hdl.handle.net/10447/337547

UR - https://www.mdpi.com/2076-3417/9/2/307/pdf

M3 - Article

VL - 9

SP - 307-1-307-21

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

ER -