Cervical Cell Segmentation from Overlapped Cells Using Fuzzy C-Means Clustering
Prianka R R1, Celine Kavitha A2 

1Prianka R R, Department of CSE, RMK College of Engineering & Technology, Chennai, India.
2Prof. Celine Kavitha A, Department of Physics, Vel Tech Multi Tech Dr. RR Dr.SR Engineering College, Chennai, India.

Manuscript received on 11 March 2019 | Revised Manuscript received on 19 March 2019 | Manuscript published on 30 July 2019 | PP: 3401-3404 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1442058119/19©BEIESP | DOI: 10.35940/ijrte.A1442.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cervical cancer is the symptomless disease to cause death amongst women due to cancer. Most of the cervical cancer diagnosis process microscopic images are taken as sample to identify Segmentation of cervical cells. In this paper, Fuzzy c-means clustering algorithm is used to preserve the colour and data loss during segmentation is minimal. It accurately segments the individual cytoplasm and nuclei from a cluster of overlapping cervical cells. Recent methods cannot undertake such absolute segmentation due to various challenges involved in delineating cells coping with overlap and poor contrast. Improved method for detecting overlapping cervical cells using advanced tests yields better results in detection. The cervical cancer can be prevented through both early detection and best treatment based on the acuteness of the disease.
Index Terms: Cervical Cancer, Overlapping Cell Segmentation, Pap Smear Image Analysis, Visual Inspection.

Scope of the Article: Fuzzy Logics