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Staining pattern classification of antinuclear autoantibodies based on block segmentation in indirect immunofluorescence images.

Li J, Tseng KK, Hsieh ZY, Yang CW, Huang HN - PLoS ONE (2014)

Bottom Line: The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research.Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance.Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image) with a total accuracy of about 94.62%.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China.

ABSTRACT
Indirect immunofluorescence based on HEp-2 cell substrate is the most commonly used staining method for antinuclear autoantibodies associated with different types of autoimmune pathologies. The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research. Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance. Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image) with a total accuracy of about 94.62%.

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Related in: MedlinePlus

ANA patterns in the experimental dataset: (a) coarse speckled (b) fine speckled (c) nucleolar (d) peripheral.
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pone-0113132-g001: ANA patterns in the experimental dataset: (a) coarse speckled (b) fine speckled (c) nucleolar (d) peripheral.

Mentions: Before the classification of staining patterns, relevant patterns (see Figure 1) related to the most recurrent ANAs should be considered [5], [6] in the experimental dataset.


Staining pattern classification of antinuclear autoantibodies based on block segmentation in indirect immunofluorescence images.

Li J, Tseng KK, Hsieh ZY, Yang CW, Huang HN - PLoS ONE (2014)

ANA patterns in the experimental dataset: (a) coarse speckled (b) fine speckled (c) nucleolar (d) peripheral.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256175&req=5

pone-0113132-g001: ANA patterns in the experimental dataset: (a) coarse speckled (b) fine speckled (c) nucleolar (d) peripheral.
Mentions: Before the classification of staining patterns, relevant patterns (see Figure 1) related to the most recurrent ANAs should be considered [5], [6] in the experimental dataset.

Bottom Line: The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research.Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance.Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image) with a total accuracy of about 94.62%.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China.

ABSTRACT
Indirect immunofluorescence based on HEp-2 cell substrate is the most commonly used staining method for antinuclear autoantibodies associated with different types of autoimmune pathologies. The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research. Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance. Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image) with a total accuracy of about 94.62%.

Show MeSH
Related in: MedlinePlus