Limits...
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%.

Show MeSH

Related in: MedlinePlus

ANA pattern classification methods: segmentation, feature extraction and classification.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4256175&req=5

pone-0113132-g002: ANA pattern classification methods: segmentation, feature extraction and classification.

Mentions: Numerous features utilized in ANA pattern classification were investigated, including texture features and shape features, as shown in Figure 2. Since the same object may have a variety of different colours but a similar shape, many queries may arise as to the shape of the image instead of the colour of the image. There are two methods of presenting shape features: contour feature and regional characteristics. However, shape features lack a model, and have high computation and storage requirements. In [12], the shape measurement of a single feature vector, with greater weight by far given to texture, is used to identify the cytoplasmatic class and the shape feature (calculated as the area divided by the square of the perimeter) is able to recognize most samples of this category based on a single parameter. In [4], four shape features, area, perimeter, inside area and perimeter area, in the feature vector are utilized as the inputs for a self-organizing map (SOM) model to determine the similarity of the cells.


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 pattern classification methods: segmentation, feature extraction and classification.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0113132-g002: ANA pattern classification methods: segmentation, feature extraction and classification.
Mentions: Numerous features utilized in ANA pattern classification were investigated, including texture features and shape features, as shown in Figure 2. Since the same object may have a variety of different colours but a similar shape, many queries may arise as to the shape of the image instead of the colour of the image. There are two methods of presenting shape features: contour feature and regional characteristics. However, shape features lack a model, and have high computation and storage requirements. In [12], the shape measurement of a single feature vector, with greater weight by far given to texture, is used to identify the cytoplasmatic class and the shape feature (calculated as the area divided by the square of the perimeter) is able to recognize most samples of this category based on a single parameter. In [4], four shape features, area, perimeter, inside area and perimeter area, in the feature vector are utilized as the inputs for a self-organizing map (SOM) model to determine the similarity of the cells.

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