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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

Four staining patterns and the corresponding  descriptors: (a) coarse speckled (CS); (b) fine speckled (FS); (c) nucleolar (NU); (d) peripheral (PE).
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pone-0113132-g008: Four staining patterns and the corresponding descriptors: (a) coarse speckled (CS); (b) fine speckled (FS); (c) nucleolar (NU); (d) peripheral (PE).

Mentions: In this study, the IIF images were collected based on HEp-2 substrate at a serum dilution of 1∶80. A physician takes images of slides with an acquisition unit consisting of the fluorescence microscope coupled with a commonly used fluorescence microscope (Axioskop 2, CarlZeiss, Jena, Germany) at 640-fold magnification. The IIF images were taken by an operator with a colour digital camera (E-330, Olympus, Tokyo, Japan). The digitized images were of 8-bit photometric resolution for each RGB colour channel with a resolution of 3136×2352 pixels [9]. This image database contains 260 samples belonging to four different patterns, i.e. coarse speckled (CS), fine speckled (FS), nucleolar (NU) and peripheral (PE). The number of samples in each pattern were 167 (CS), 20 (FS), 38 (NU) and 35 (PE), and the odd-numbered half of them were selected to belong to the training set, and the remainder were the test set (Table 1). If ANA testing detects any of the four patterns, the patients may have specific systemic autoimmune diseases. For example, if the test detected the CS pattern, the patients may have systemic lupus erythematosus (SLE), mixed connective tissue disease (MCTD), progressive systemic sclerosis (PSS) or cryoglobulinemia. Experiments have shown that the best features for ANA classification are features, which are shown in Figure 8.


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)

Four staining patterns and the corresponding  descriptors: (a) coarse speckled (CS); (b) fine speckled (FS); (c) nucleolar (NU); (d) peripheral (PE).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0113132-g008: Four staining patterns and the corresponding descriptors: (a) coarse speckled (CS); (b) fine speckled (FS); (c) nucleolar (NU); (d) peripheral (PE).
Mentions: In this study, the IIF images were collected based on HEp-2 substrate at a serum dilution of 1∶80. A physician takes images of slides with an acquisition unit consisting of the fluorescence microscope coupled with a commonly used fluorescence microscope (Axioskop 2, CarlZeiss, Jena, Germany) at 640-fold magnification. The IIF images were taken by an operator with a colour digital camera (E-330, Olympus, Tokyo, Japan). The digitized images were of 8-bit photometric resolution for each RGB colour channel with a resolution of 3136×2352 pixels [9]. This image database contains 260 samples belonging to four different patterns, i.e. coarse speckled (CS), fine speckled (FS), nucleolar (NU) and peripheral (PE). The number of samples in each pattern were 167 (CS), 20 (FS), 38 (NU) and 35 (PE), and the odd-numbered half of them were selected to belong to the training set, and the remainder were the test set (Table 1). If ANA testing detects any of the four patterns, the patients may have specific systemic autoimmune diseases. For example, if the test detected the CS pattern, the patients may have systemic lupus erythematosus (SLE), mixed connective tissue disease (MCTD), progressive systemic sclerosis (PSS) or cryoglobulinemia. Experiments have shown that the best features for ANA classification are features, which are shown in Figure 8.

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