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Feature quantification and abnormal detection on cervical squamous epithelial cells.

Zhao M, Chen L, Bian L, Zhang J, Yao C, Zhang J - Comput Math Methods Med (2015)

Bottom Line: The relationship between quantified value and pathological feature can be established by these descriptors.Finally, an effective method is proposed for detecting abnormal cells based on feature quantification.Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

ABSTRACT
Feature analysis and classification detection of abnormal cells from images for pathological analysis are an important issue for the realization of computer assisted disease diagnosis. This paper studies a method for cervical squamous epithelial cells. Based on cervical cytological classification standard and expert diagnostic experience, expressive descriptors are extracted according to morphology, color, and texture features of cervical scales epithelial cells. Further, quantificational descriptors related to cytopathology are derived as well, including morphological difference degree, cell hyperkeratosis, and deeply stained degree. The relationship between quantified value and pathological feature can be established by these descriptors. Finally, an effective method is proposed for detecting abnormal cells based on feature quantification. Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.

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Related in: MedlinePlus

The detection results of individual cells.
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Related In: Results  -  Collection


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fig7: The detection results of individual cells.

Mentions: From the image sets of cervical squamous epithelial cells, we randomly select 40 cells. After feature quantification on cell images and nucleus images, our proposed fast abnormal detection method is applied to cells classification. The 40 cells are classified into 34 abnormal cells and 4 normal cells. The detection result is shown in Figure 7 and the detection accuracy is 100%. Experimental results show that the abnormal detection method on cervical squamous epithelial cells is efficient and effective.


Feature quantification and abnormal detection on cervical squamous epithelial cells.

Zhao M, Chen L, Bian L, Zhang J, Yao C, Zhang J - Comput Math Methods Med (2015)

The detection results of individual cells.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4385601&req=5

fig7: The detection results of individual cells.
Mentions: From the image sets of cervical squamous epithelial cells, we randomly select 40 cells. After feature quantification on cell images and nucleus images, our proposed fast abnormal detection method is applied to cells classification. The 40 cells are classified into 34 abnormal cells and 4 normal cells. The detection result is shown in Figure 7 and the detection accuracy is 100%. Experimental results show that the abnormal detection method on cervical squamous epithelial cells is efficient and effective.

Bottom Line: The relationship between quantified value and pathological feature can be established by these descriptors.Finally, an effective method is proposed for detecting abnormal cells based on feature quantification.Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

ABSTRACT
Feature analysis and classification detection of abnormal cells from images for pathological analysis are an important issue for the realization of computer assisted disease diagnosis. This paper studies a method for cervical squamous epithelial cells. Based on cervical cytological classification standard and expert diagnostic experience, expressive descriptors are extracted according to morphology, color, and texture features of cervical scales epithelial cells. Further, quantificational descriptors related to cytopathology are derived as well, including morphological difference degree, cell hyperkeratosis, and deeply stained degree. The relationship between quantified value and pathological feature can be established by these descriptors. Finally, an effective method is proposed for detecting abnormal cells based on feature quantification. Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification.

Show MeSH
Related in: MedlinePlus