[1]
International Agency for Research on Cancer, All Cancers - WHO 2018,, All Cancers 2018, vol. 876, p.2, (2018).
Google Scholar
[2]
N. Nahrawi, W. A. Mustafa, and S. N. A. M. Kanafiah, Knowledge of Human Papillomavirus ( HPV ) and Cervical Cancer among Malaysia Residents : A Review,, Sains Malaysiana, vol. 49, no. 7, p.1687–1695, (2020).
DOI: 10.17576/jsm-2020-4907-19
Google Scholar
[3]
W. A. Mustafa, A. Halim, and K. S. A. Rahman, A Narrative Review : Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis,, Oncologie, vol. 22, no. 2, p.53–63, (2020).
DOI: 10.32604/oncologie.2020.013660
Google Scholar
[4]
NCRI, Cervical Cancer Trends Report,, Cancer Trends, no. 35, p.2–4, (2015).
Google Scholar
[5]
World Cancer Research Fund, Cervical cancer statistics: Cervical cancer is the eighth most common cancer worldwide,, Am. Inst. Cancer Res., vol. 746, no. 92, p.1088–1153, (2019).
Google Scholar
[6]
Cervical Cancer Prevention, Recent Evidence on Cervical Cancer Screening in Low-Resource Settings,, Allience Cerv. Cancer Progr., no. May, p.1–8, (2011).
Google Scholar
[7]
N. A. Parmin, U. Hashim, W. A. Mustafa, S. C. B. Gopinath, Z. Rejali, and M. N. A. Uda, In Vitro Nucleic Acid Hybridization for the Identification of High-Risk Human Papillomavirus ( HPV ) in Cervical Clinical Specimens,, J. Biomimetics, Biomater. Biomed. Eng., vol. 42, p.51–58, (2019).
DOI: 10.4028/www.scientific.net/jbbbe.42.51
Google Scholar
[8]
F. H. Cheng and N. R. Hsu, Automated cell nuclei segmentation from microscopic images of cervical smear,, in 2016 International Conference on Applied System Innovation, IEEE ICASI 2016, (2016).
DOI: 10.1109/icasi.2016.7539846
Google Scholar
[9]
P. Yu, M. Park, M. Xu, S. Luo, J. S. Jin, Y. Cui, and W. S. Felix Wong, Detection of nuclei clusters from cervical cancer microscopic imagery using C4.5,, in ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings, 2010, vol. 3.
DOI: 10.1109/iccet.2010.5485792
Google Scholar
[10]
A. C. Society, Cervical Cancer What is cervical cancer ?,, Am. Cancer Soc., p.4–7, (2016).
Google Scholar
[11]
Cancer Research UK, Cervical cancer incidence statistics,, Cancer Research UK, 2014.
Google Scholar
[12]
W. A. Mustafa, A. Halim, M. A. Jamlos, and Z. S. Syed Idrus, A Review : Pap Smear Analysis Based on Image Processing Approach,, J. Phys. Conf. Ser., vol. 1529, no. 022080, p.1–13, (2020).
DOI: 10.1088/1742-6596/1529/2/022080
Google Scholar
[13]
Y. Jusman, S. C. Ng, and N. A. Abu Osman, Intelligent screening systems for cervical cancer,, Scientific World Journal, vol. 2014. (2014).
DOI: 10.1155/2014/810368
Google Scholar
[14]
P. D. Palma, L. Moresco, P. G. Rossi, P. Borgia, and T. Jefferson, Computer-assisted Pap test for cervical cancer screening,, Epidemiol. Prev., vol. 36, no. 5, p.1–48, (2012).
Google Scholar
[15]
S. Maiti, D. Bhattacharya, and A. Kar, Detection of cervical cancer - An application of computer vision,, in Proceedings of the IASTED International Conference on Biomedical Engineering, 2003, p.172–177.
Google Scholar
[16]
W. A. Mustafa and H. Yazid, Conversion of the Retinal Image Using Gray World Technique,, J. Biomimetics, Biomater. Biomed. Eng., vol. 36, p.70–77, (2018).
DOI: 10.4028/www.scientific.net/jbbbe.36.70
Google Scholar
[17]
S. Ragothaman, S. Narasimhan, M. G. Basavaraj, and R. Dewar, Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model,, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, (2016).
DOI: 10.1109/cvprw.2016.173
Google Scholar
[18]
D. Riana, M. Wahyudi, and A. N. Hidayanto, Comparison of nucleus and inflammatory cell detection methods on Pap smear images,, in Proceedings of the 2nd International Conference on Informatics and Computing, ICIC 2017, (2018).
DOI: 10.1109/iac.2017.8280540
Google Scholar
[19]
W. William, A. Ware, A. H. Basaza-Ejiri, and J. Obungoloch, A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images,, Biomed. Eng. Online, vol. 18, no. 1, p.1–22, (2019).
DOI: 10.1186/s12938-019-0634-5
Google Scholar
[20]
W. William, A. Ware, A. H. Basaza-Ejiri, and J. Obungoloch, Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm,, Informatics in Medicine Unlocked, vol. 14. p.23–33, (2019).
DOI: 10.1016/j.imu.2019.02.001
Google Scholar
[21]
R. R. Prianka and A. Celine Kavitha, Cervical cell segmentation from overlapped cells using fuzzy C-means clustering,, Int. J. Recent Technol. Eng., vol. 8, no. 2, p.3401–3404, (2019).
Google Scholar
[22]
K. P. Win, Y. Kitjaidure, K. Hamamoto, and T. M. Aung, Computer-assisted screening for cervical cancer using digital image processing of pap smear images,, Appl. Sci., vol. 10, no. 5, (2020).
DOI: 10.3390/app10051800
Google Scholar
[23]
K. P. Win, Y. Kitjaidure, M. P. Paing, and K. Hamamoto, Cervical cancer detection and classification from pap smear images,, in ACM International Conference Proceeding Series, 2019, p.47–54.
DOI: 10.1145/3366174.3366178
Google Scholar
[24]
M. S. Nosrati and G. Hamarneh, Segmentation of overlapping cervical cells: A variational method with star-shape prior,, in Proceedings - International Symposium on Biomedical Imaging, (2015).
DOI: 10.1109/isbi.2015.7163846
Google Scholar
[25]
T. Wang, X. Jiang, S. Chen, Y. Song, E.-L. Tan, B. Lei, J.-Z. Cheng, and D. Ni, Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,, IEEE Trans. Med. Imaging, vol. 36, no. 1, p.288–300, (2016).
DOI: 10.1109/tmi.2016.2606380
Google Scholar
[26]
R. M. Thanki, A. M. Kothari, R. M. Thanki, and A. M. Kothari, Morphological Image Processing,, in Digital Image Processing using SCILAB, 2019, p.99–113.
DOI: 10.1007/978-3-319-89533-8_5
Google Scholar
[27]
W. K. Pratt, Morphological Image Processing,, in Introduction to Digital Image Processing, 2020, p.399–438.
Google Scholar
[28]
B. Taha, J. Dias, and N. Werghi, Classification of cervical-cancer using pap-smear images: A convolutional neural network approach,, in Communications in Computer and Information Science, 2017, vol. 723, p.261–272.
DOI: 10.1007/978-3-319-60964-5_23
Google Scholar
[29]
H. Bandyopadhyay and M. Nasipuri, Segmentation of Pap Smear Images for Cervical Cancer Detection,, in 2020 IEEE Calcutta Conference, CALCON 2020 - Proceedings, 2020, p.30–33.
DOI: 10.1109/calcon49167.2020.9106484
Google Scholar
[30]
Kurnianingsih, K. H. S. Allehaibi, L. E. Nugroho, Widyawan, L. Lazuardi, A. S. Prabuwono, and T. Mantoro, Segmentation and classification of cervical cells using deep learning,, IEEE Access, vol. 7, p.116925–116941, (2019).
DOI: 10.1109/access.2019.2936017
Google Scholar
[31]
Grayscale Image,, in Definitions, (2020).
Google Scholar
[32]
C. Saravanan, Color image to grayscale image conversion,, in 2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010, 2010, vol. 2, p.196–199.
DOI: 10.1109/iccea.2010.192
Google Scholar
[33]
Grayscale Image Processing and Segmentation,, Med. Imaging Technol., vol. 35, no. 1, p.3–10, (2017).
Google Scholar
[34]
C. A. B. de Mello, Image thresholding,, in Digital Document Analysis and Processing, 2013, p.71–98.
Google Scholar
[35]
Image Analyst, Image Segmentation Tutorial - File Exchange - MATLAB Central,, MATLAB Central File Exchange, (2008).
Google Scholar
[36]
M. Sinecen, Digital Image Processing with MATLAB,, in Applications from Engineering with MATLAB Concepts, (2016).
DOI: 10.5772/63028
Google Scholar
[37]
W. A. Mustafa and M. M. M. A. Kader, Binarization of Document Images: A Comprehensive Review,, J. Phys. Conf. Ser., vol. 1019, no. 012023, p.1–9, (2018).
DOI: 10.1088/1742-6596/1019/1/012023
Google Scholar
[38]
L. Z. Wei, W. A. Mustafa, M. A. Jamlos, S. Z. S. Idrus, and M. H. Sahabudin, Cervical Cancer Classification Using Image Processing Approach : A Review,, IOP Conf. Ser. Mater. Sci. Eng., vol. 917, no. 012068, p.1–9, (2020).
DOI: 10.1088/1757-899x/917/1/012068
Google Scholar
[39]
W. A. Mustafa and M. M. M. A. Kader, A Comparative Study of Automated Segmentation Methods for Cell Nucleus Detection,, Malaysian Appl. Biol., vol. 47, no. 2, p.125–129, (2018).
Google Scholar
[40]
W. A. Mustafa, H. Yazid, and M. Jaafar, An Improved Sauvola Approach on Document Images Binarization,, J. Telecommun. Electron. Comput. Eng., vol. 10, no. 2, p.43–50, (2018).
Google Scholar
[41]
W. A. Mustafa, A. S. Abdul-Nasir, and Z. Mohamed, Malaria Parasites Segmentation Based on Sauvola Algorithm Modification,, Malaysian Appl. Biol., vol. 47, no. 2, p.71–76, (2018).
Google Scholar
[42]
W. A. Mustafa and M. M. M. A. Kader, Binarization of Document Image Using Optimum Threshold Modification,, J. Phys. Conf. Ser., vol. 1019, no. 012022, p.1–8, (2018).
DOI: 10.1088/1742-6596/1019/1/012022
Google Scholar
[43]
B. Bataineh, S. N. H. S. Abdullah, and K. Omar, An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows,, Pattern Recognit. Lett., vol. 32, p.1805–1813, (2011).
DOI: 10.1016/j.patrec.2011.08.001
Google Scholar
[44]
D. Bradley and G. Roth, Adaptive Thresholding Using the Integral Image,, J. Graph. GPU, Game Tools, vol. 12, no. 2, p.13–21, (2011).
Google Scholar