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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: 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.The relationship between quantified value and pathological feature can be established by these descriptors.

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

Representation of cervical squamous epithelial cells in different categories of TBS. (a) Normal. (b) ASC-US. (c, d) LISL. (e, f) HISL. (g, h) SCC.
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fig2: Representation of cervical squamous epithelial cells in different categories of TBS. (a) Normal. (b) ASC-US. (c, d) LISL. (e, f) HISL. (g, h) SCC.

Mentions: In pathological diagnosis, liquid thin-layer cytology production technology is applied to get cervical smears, from which people can observe conveniently and obtain high-quality microscopic images [10]. In Figure 2, there are many images in different stages. The categories are defined in the Bethesda system (TBS) [11].


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)

Representation of cervical squamous epithelial cells in different categories of TBS. (a) Normal. (b) ASC-US. (c, d) LISL. (e, f) HISL. (g, h) SCC.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: Representation of cervical squamous epithelial cells in different categories of TBS. (a) Normal. (b) ASC-US. (c, d) LISL. (e, f) HISL. (g, h) SCC.
Mentions: In pathological diagnosis, liquid thin-layer cytology production technology is applied to get cervical smears, from which people can observe conveniently and obtain high-quality microscopic images [10]. In Figure 2, there are many images in different stages. The categories are defined in the Bethesda system (TBS) [11].

Bottom Line: 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.The relationship between quantified value and pathological feature can be established by these descriptors.

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