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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 regions. (a) Cell regions. (b) Nucleus regions. (c) Binary map of cell regions with red points as the cell centroids. (d) Binary map of nucleus regions with red points as the nucleus centroids. (e, f) Region color labeling and the regions in (f) have the same color as (e) have owner-member relationship.
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fig5: Representation of regions. (a) Cell regions. (b) Nucleus regions. (c) Binary map of cell regions with red points as the cell centroids. (d) Binary map of nucleus regions with red points as the nucleus centroids. (e, f) Region color labeling and the regions in (f) have the same color as (e) have owner-member relationship.

Mentions: Taking Figure 2(c) image as an example, its related manual segmentation results are shown in Figure 5. The informative sections of Figure 2(c) are the combination of Figure 5(a), the cell regions, and Figure 5(b), the nucleus regions. After morphological dilation, erosion, open, close, and filling operations, the corresponding binary maps [19] can be produced with individual regions as shown in Figures 5(c) and 5(d). The centroids are marked in red and each region is labeled with numbers. Single cell image set and aggregation cell image set are the storage of all the information.


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 regions. (a) Cell regions. (b) Nucleus regions. (c) Binary map of cell regions with red points as the cell centroids. (d) Binary map of nucleus regions with red points as the nucleus centroids. (e, f) Region color labeling and the regions in (f) have the same color as (e) have owner-member relationship.
© Copyright Policy
Related In: Results  -  Collection

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

fig5: Representation of regions. (a) Cell regions. (b) Nucleus regions. (c) Binary map of cell regions with red points as the cell centroids. (d) Binary map of nucleus regions with red points as the nucleus centroids. (e, f) Region color labeling and the regions in (f) have the same color as (e) have owner-member relationship.
Mentions: Taking Figure 2(c) image as an example, its related manual segmentation results are shown in Figure 5. The informative sections of Figure 2(c) are the combination of Figure 5(a), the cell regions, and Figure 5(b), the nucleus regions. After morphological dilation, erosion, open, close, and filling operations, the corresponding binary maps [19] can be produced with individual regions as shown in Figures 5(c) and 5(d). The centroids are marked in red and each region is labeled with numbers. Single cell image set and aggregation cell image set are the storage of all the information.

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