Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

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Abstract

Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 ± 5)% and a Accuracy equal to (98.0 ± 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically.
Lingua originaleEnglish
pagine (da-a)989-995
Numero di pagine7
RivistaIET Computer Vision
Volume12
Stato di pubblicazionePublished - 2018

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Image classification
Antigens
Antibodies
Screening

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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title = "Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method",
abstract = "Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7{\%} of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 ± 5){\%} and a Accuracy equal to (98.0 ± 0.5){\%}. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically.",
author = "Giuseppe Raso and Letizia Vivona and Vincenzo Taormina and Donato Cascio",
year = "2018",
language = "English",
volume = "12",
pages = "989--995",
journal = "IET Computer Vision",
issn = "1751-9632",
publisher = "Institution of Engineering and Technology",

}

TY - JOUR

T1 - Automated approach for indirect immunofluorescence images classification based on unsupervised clustering method

AU - Raso, Giuseppe

AU - Vivona, Letizia

AU - Taormina, Vincenzo

AU - Cascio, Donato

PY - 2018

Y1 - 2018

N2 - Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 ± 5)% and a Accuracy equal to (98.0 ± 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically.

AB - Autoimmune diseases (ADs) are a collection of many complex disorders of unknown aetiology resulting in immune responses to self-antigens and are thought to result from interactions between genetic and environmental factors. ADs collectively are amongst the most prevalent diseases in the U.S., affecting at least 7% of the population. The diagnosis of ADs is very complex, the standard screening methods provides seeking and recognizing of Antinuclear Antibodies (ANA) by Indirect ImmunoFluorescence (IIF) based on HEp-2 cells. In this paper an automatic system able to identify and classify the Centromere pattern is presented. The method is based on the grouping of centromeres present on the cells through a clustering K-means algorithm. The performances were obtained on two public database of IIF images (A.I.D.A. and MIVIA). Our results showed a sensitivity for image of (90 ± 5)% and a Accuracy equal to (98.0 ± 0.5)%. Results demonstrate that the system is able to identify and classify Centromere pattern with accuracy better or comparable with some representative state of the art works. Moreover, it should be noted that for the classification phase the works used for the comparison used an expert-manual segmentation while, in the present work, the segmentation was obtained automatically.

UR - http://hdl.handle.net/10447/299407

UR - http://digital-library.theiet.org/content/journals/iet-cvi

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VL - 12

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EP - 995

JO - IET Computer Vision

JF - IET Computer Vision

SN - 1751-9632

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