Research Article

Papsmear Image based Detection of Cervical Cancer

by  Sreedevi M T, Usha B S, Sandya S
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Issue 20
Published: May 2012
Authors: Sreedevi M T, Usha B S, Sandya S
10.5120/7035-9698
PDF

Sreedevi M T, Usha B S, Sandya S . Papsmear Image based Detection of Cervical Cancer. International Journal of Computer Applications. 45, 20 (May 2012), 35-40. DOI=10.5120/7035-9698

                        @article{ 10.5120/7035-9698,
                        author  = { Sreedevi M T,Usha B S,Sandya S },
                        title   = { Papsmear Image based Detection of Cervical Cancer },
                        journal = { International Journal of Computer Applications },
                        year    = { 2012 },
                        volume  = { 45 },
                        number  = { 20 },
                        pages   = { 35-40 },
                        doi     = { 10.5120/7035-9698 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2012
                        %A Sreedevi M T
                        %A Usha B S
                        %A Sandya S
                        %T Papsmear Image based Detection of Cervical Cancer%T 
                        %J International Journal of Computer Applications
                        %V 45
                        %N 20
                        %P 35-40
                        %R 10.5120/7035-9698
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a newapproach is proposed for the early detection of cervical cancer using Papsmear images. Regular Papsmear screening is the most successful attempt of medical science and practice for the early detection of cervical cancer. Manual analysis of the cervical cells is time consuming, laborious and error prone. This paper presents an algorithm for classifying Cervical cells as normal or abnormal. It is tested on 80Papsmear images and the experimental results show that the algorithm is on par with the results obtained by earlier work and gives satisfactory results in terms of sensitivity (100%) and specificity (90%).

References
  • Cervical Cancer in India, South Asia Centre for chronic disease website available at: http://sancd. org/uploads/pdf/cervical_cancer. pdf
  • Dinshaw KA, Shastri SS, PatilSS,"Cancer Control Programme In India: Challenges For The New Millennium", Health Administrator Vol: XVII, Number 1: 10-13,pg.
  • The Times of India newspaper website available at: http://articles. timesofindia. indiatimes. com/2012-02-04/mumbai/31024533_1_cervical-cancer-breast-cancer-globocan
  • Cervical Cancer-Overview and Incidence website available at: http://www. medindia. net/patients/patientinfo/cervicalcancer-incidence. htm#ixzz1oDusUgV1
  • Jens Byriel,"Neuro-Fuzzy Classification of Cells in Cervical Smears", M. Sc Thesis
  • Erik Martin," Pap-Smear Classification" Thesis Report, 22nd September 2003
  • Norup, "Classification of pap-smear data by transductive Neuro-fuzzy methods", Master Thesis
  • Marina E. Plissiti, Christophoros Nikou1 and Antonia Charchanti," Combining shape, texture and intensity features for cell nuclei extraction in pap smear images", Pattern Recognition Letters, Vol. 32, No. 6, pp. 838-853, 2011
  • BustanurRosidi, NorainiJalil, Nur. M. Pista, Lukman H. Ismail, EkoSupriyantoTati L. Mengko "Classification of Cervical Cells Based on Labeled Colour Intensity Distribution" International Journal of Biology and Biomedical Engineering, Issue 4, Volume 5, 2011
  • J. Serra "Image Analysis and Mathematical Morphology", Academic Press, London, 1982
  • M. E. Plissiti, A. Charchanti, O. Krikoni and D. I. Fotiadis, "Automated segmentation of cell nuclei in PAP smear images"
  • Rafael C. Gonzalez, Richard E. Woods, "Digital Image Processing", Pearson Education, Inc. and Dorling Kindersley Publications, Inc.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Segmentation Feature Extraction Classification Sensitivity Specificity

Powered by PhDFocusTM