Diagnostic Parameters in Liquid-Based Cervical Cytology Using a Coagulant Suspension Fixative

2005 ◽  
Vol 49 (5) ◽  
pp. 513-519 ◽  
Author(s):  
Mathilde E. Boon ◽  
Elisabeth Ouwerkerk-Noordam ◽  
Albert J. H. Suurmeijer ◽  
Lambrecht P. Kok
Keyword(s):  
Cervical Cytology ◽  
Diagnostic Parameters

HPV testing versus cervical cytology for screening for cancer of the uterine cervix

2003 ◽  
Author(s):  
George Koliopoulos ◽  
Pierre PL Martin-Hirsch ◽  
Evangelos Paraskevaidis ◽  
Marc Arbyn ◽  
Maria Kyrgiou ◽  
...  
Keyword(s):  
Uterine Cervix ◽  
Cervical Cytology ◽  
Hpv Testing

IsChlamydia trachomatisassociated with dyskaryotic cervical cytology? Results of a prospective population-based study

1996 ◽  
Vol 16 (6) ◽  
pp. 535-537
Author(s):  
A. P. Warwick ◽  
H. Tucker ◽  
J. M. Jones ◽  
C. W. E. Redman ◽  
J. Gray
Keyword(s):  
Population Based ◽  
Cervical Cytology ◽  
Population Based Study

Role of Cervical Cytology in Surveillance after a Radical Trachelectomy in Patients with a Prior History of Cervical Cancer

2016 ◽  
Vol 143 (1) ◽  
pp. 204-205
Author(s):  
J.S. Shah ◽  
A.J. Brown ◽  
N.D. Fleming ◽  
A.M. Nick ◽  
P.T. Soliman ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cervical Cytology ◽  
Prior History ◽  
Radical Trachelectomy ◽  
History Of

scholarly journals A contour property based approach to segment nuclei in cervical cytology images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Iram Tazim Hoque ◽  
Nabil Ibtehaz ◽  
Saumitra Chakravarty ◽  
M. Saifur Rahman ◽  
M. Sohel Rahman
Keyword(s):  
Pap Smear ◽  
State Of The Art ◽  
Cervical Cytology ◽  
Average Intensity ◽  
Nucleus Size ◽  
Real Dataset ◽  
Art Methods ◽  
Convolution Filter ◽  
Cervical Cells ◽  
Nucleus Segmentation

Abstract Background Segmentation of nuclei in cervical cytology pap smear images is a crucial stage in automated cervical cancer screening. The task itself is challenging due to the presence of cervical cells with spurious edges, overlapping cells, neutrophils, and artifacts. Methods After the initial preprocessing steps of adaptive thresholding, in our approach, the image passes through a convolution filter to filter out some noise. Then, contours from the resultant image are filtered by their distinctive contour properties followed by a nucleus size recovery procedure based on contour average intensity value. Results We evaluate our method on a public (benchmark) dataset collected from ISBI and also a private real dataset. The results show that our algorithm outperforms other state-of-the-art methods in nucleus segmentation on the ISBI dataset with a precision of 0.978 and recall of 0.933. A promising precision of 0.770 and a formidable recall of 0.886 on the private real dataset indicate that our algorithm can effectively detect and segment nuclei on real cervical cytology images. Tuning various parameters, the precision could be increased to as high as 0.949 with an acceptable decrease of recall to 0.759. Our method also managed an Aggregated Jaccard Index of 0.681 outperforming other state-of-the-art methods on the real dataset. Conclusion We have proposed a contour property-based approach for segmentation of nuclei. Our algorithm has several tunable parameters and is flexible enough to adapt to real practical scenarios and requirements.

scholarly journals Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Hritam Basak ◽  
Rohit Kundu ◽  
Sukanta Chakraborty ◽  
Nibaran Das
Keyword(s):  
Cervical Cytology ◽  
Features Selection
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