Extraction and Separation of Overlapped Squamous Cell Cytoplasm with Disjoint Nuclei in Cervical Pap Smear Image
DOI:
https://doi.org/10.13052/jmm1550-4646.1833Keywords:
Pap smear, Disjoint, Cervical cell, Squamous, Overlapped, Hough transformationAbstract
The automated analysis of Papanicolaou (Pap) smear images is a challenging issue due to the occlusion of cells. The feature extraction and interpretation depend on the accuracy of cell segmentation. This is important to find the difference between normal and abnormal cells. However, most of the time complications are due to overlapping in the Pap smear cells. The overlapping can be in nuclei regions or cytoplasmic regions or sometimes both the regions. There are two types of cells found in Pap smear images – Columnar and Squamous cells. Most of the time abnormalities are found in squamous cells which in turn may lead to cervical cancer. Hence, this work concentrates on the separation of squamous cells in which cytoplasmic regions are overlapped with disjoint nuclei regions. The main objective of this work is to identify the intersecting points where cytoplasmic regions of two cells meet, called Concavity points, which are calculated using a scope of points along the cytoplasmic boundary of the cells, and separating the cells along with these concavity points.
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References
World Health Organization, Comprehensive Cervical Cancer Control-A guide to essential practice Second edition, pp. 8, 2014.
Afaf Tareef, Yang Song, Heng Huang, Dagan Feng, Mei Chen, Yue Wang, and Weidong Cai, ‘Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells’, IEEE Transactions on Medical Imaging, pp. 2044–2059, 2018.
Plissiti, M. E., Nikou, C. and Charchanti, A., ‘Automated detection of cell nuclei in Pap smear images using morphological reconstruction and clustering’, IEEE Transactions on Information Technology in Biomedicine, pp. 233–241, 2011.
Lu, Z., Carneiro, G. and Bradley, A. P., ‘An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells’, IEEE Transactions on Image Processing, pp. 1261–1272, 2015.
Malm, P., Balakrishnan, B. N., Sujathan, V. K., Kumar, R. and Bengtsson. E, ‘Debris removal in Pap-smear images’, Computer Methods and Programs in Biomedicine, pp. 128–138, 2013.
Pin Wang, Jiaxin Wang, Yongming Li and Linyu Li, H. Z., ‘Adaptive Pruning of Transfer Learned Deep Convolutional Neural Network for Classification of Cervical Pap Smear Images’, IEEE Access: Multidisciplinary, pp. 50674–50683, 2020.
Youyi Song, Ee-Leng Tan, Xudong Jiang, Jie-Zhi Cheng, Dong Ni, Siping Chen, Baiying Lei and Tianfu Wang, ‘Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images’, IEEE Transactions on Medical Imaging, pp. 288–300, 2017.
Plissiti, M. E. and Nikou, C., ‘Overlapping cell nuclei segmentation using a spatially adaptive active physical model’, IEEE Transactions on Image Processing, pp. 4568–4580, 2012.
Deepa, T. P., and Nagaraja Rao, A., ‘A Study on Denoising of Poisson Noise in Pap Smear Microscopic Image’, Indian Journal of Science and Technology, pp. 1–10, 2016.
Byriel J., ‘Neuro-Fuzzy classification of cells in cervical smears’, Technical University of Denmark, 1999.
Ruberto, C. Di, ‘Generalized Hough Transform for Shape Matching’, International Journal of Computer Applications, 2102.
Downloads. http://mde-lab.aegean.gr/index.php/downloads
Rajeev Srivastava, J.R.P. Gupta, and Harish Parthasarathy, ‘Enhancement and Restoration of Microscopic Images Corrupted with Poisson’s Noise using a Nonlinear Partial Differential Equation-based Filter’, Defence Science Journal, Vol. 61, No. 5, September 2011, pp. 452–461.