Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2013 | OriginalPaper | Buchkapitel

A Review of Automated Techniques for Cervical Cell Image Analysis and Classification

verfasst von : Marina E. Plissiti, Christophoros Nikou

Erschienen in: Biomedical Imaging and Computational Modeling in Biomechanics

Verlag: Springer Netherlands

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Cervical smear screening is the most popular method used for the detection of cervical cancer in its early stages. The most eminent screening test is the Pap smear, which is based on the staining of cervical cells, using the technique that was first introduced by George Papanicolaou (Science 1942). With this screening technique, precancerous conditions and abnormal changes in cells that may develop into cancer are recognized. The widespread use of this test in developed countries has significantly reduced the incidence and mortality of invasive cervical cancer. In the last years, many methods have been appeared in the literature, which aim at the automated determination of the cytoplasm and the nucleus in these images. In this context, sophisticated image processing techniques and feature extraction and classification methods have been developed by several researchers, in order to derive useful conclusions for the characterization of the contents of the Pap smear images. In this work, an overview of the published techniques related to cervical smear screening is presented, in order to provide an integrated essay of the state of the art methods in the specific scientific field. Special focus has been paid on two main concepts with great research interest: the cell image segmentation and the classification techniques proposed for the characterization Pap smear images.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Modeling of Trabecular Architecture as Result of an Optimal Control Procedure
Literatur
Abulafia O, Pezzullo JC, Sherer DM (2003) Performance of ThinPrep liquid-based cervical cytology in comparison with conventionally prepared Papanicolaou smears. Gynecol Oncol 90(1):137–144CrossRef
Arbyn M, Bergeron C, Klinkhamer P, Martin-Hirsch P, Siebers AG, Bulten J (2008) Liquid compared with conventional cervical cytology: a systematic review and meta-analysis. Obstet Gynecol 111(1):167–177CrossRef
Bak E, Najarian K, Brockway JP (2004) Efficient segmentation framework of cell images in noise environments. Proc 26th Int Conf IEEE Eng Med Biol 1:1802–1805
Bamford P, Lovell B (1996) A water immersion algorithm for cytological image segmentation. In: Proceedings of the APRS image segmentation workshop, Sydney, pp 75–79
Bamford P, Lovell B (1998) Unsupervised cell nucleus segmentation with active contours. Signal Process 71(2):203–213MATHCrossRef
Bengtsson E, Eriksson O, Holmquist J (1979) High resolution segmentation of cervical cells. J Histochem Cytochem 27(1):621–628CrossRef
Bengtsson E, Eriksson O, Holmquist J, Jarkrans T, Nordin B, Stenkvist B (1981) Segmentation of cervical cells: detection of overlapping cell nuclei. Comput Graph Image Process 16:382–394CrossRef
Bondarenko AN, Katsuk AV (2005) Extracting feature vectors of biomedical images. In: Proceeding of the 9th Russian-Korean international symposium science and technology KORUS 2005 (Bondarenko and Katsuk) is Novosibirsk, 26 June–2 July 2005, Russia
Borst H, Abmayr W, Gais P (1979) A thresholding method for automatic cell image segmentation. J Histochem Cytochem 27(1):180–187CrossRef
Cahn RL, Poulsen RS, Toussaint G (1977) Segmentation of cervical cell images. J Histochem Cytochem 25(7):681–688CrossRef
Chang CW, Lin MY, Harn HJ, Harn YC, Chen CH, Tsai KH, Hwang CH (2009) Automatic segmentation of abnormal cell nuclei from microscopic image analysis for cervical cancer screening. In: Proceeding of the 3rd IEEE international conference on nano/molecular medicine and engineering, Tainan, Taiwan, pp 77–80
Chankong T, Theera-Umpon N, Auephanwiriyakul S (2009) Cervical cell classification using Fourier transform. Proc 13th Int Conf Biomed Eng 23:476–480
Davey E, Barratt A, Irwig L, Chan SF, Macaskill P, Mannes P, Saville AM (2006) Effect of study design and quality on unsatisfactory rates, cytology classifications, and accuracy in liquid based versus conventional cervical cytology: a systematic review. Lancet 367(9505):122–132CrossRef
Gallegos-Funes FJ, Gomez-Mayorga ME, Lopez-Bonilla JL, Cruz-Santiago R (2009) Rank M-type radial basis function (RMRBF) neural network for Pap smear microscopic image classification. Apeiron 16(4):542–554
Garrido A, Perez de la Blanca N (2000) Applying deformable templates for cell image segmentation. Pattern Recognit 33(5):821–832CrossRef
Harandi NM, Sadri S, Moghaddam NA, Amirfattahi R (2010) An automated method for segmentation of epithelial cervical cells in images of ThinPrep. J Med Syst 34:1043–1058CrossRef
Huang PC, Chan YK, Chan PC, Chen YF, Chen RC, Huang YR (2007) Quantitative assessment of Pap smear cells by PC-based cytopathologic image analysis system and support vector machine. Proc 1st Int Conf Med Biometrics Lect Notes Comput Sci 4901:192–199
Hutchinson ML, Isenstein LM, Goodman A, Hurley AA, Douglass KL, Mui KK, Patten FW, Zahniser DJ (1994) Homogeneous sampling accounts for the increased diagnostic accuracy using the ThinPrep processor. Am J Clin Pathol 101(2):215–219
Jackway P (1995) Morphological multiscale gradient watershed image analysis. In: Proceedings of the 9th Scandinavian Conference Image Analysis (SCIA 1995), Uppsala, pp 87–94
Jackway P (1996) Gradient watersheds in morphological scale space. IEEE Trans Image Process 5:913–921CrossRef
Jantzen J, Norup J, Dounias G, Bjerregaard B (2005) Pap-smear benchmark data for pattern classification. In: Proceedings of the Nature inspired Smart Information Systems (NiSIS), Albufeira, pp 1–9
Jung C, Kim C (2010) Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng 57(10):2600–2604MathSciNetCrossRef
Jung C, Kim C, Wan Chae S, Oh S (2010) Unsupervised segmentation of overlapped nuclei using Bayesian classification. IEEE Trans Biomed Eng 55(12):2825–2832CrossRef
Kale A, Aksoy S (2010) Segmentation of cervical images. In: Proceedings of the 20th international conference on pattern recognition, Istanbul, pp 2399–2402
Kim KB, Kim S, Sim KB (2006) Nucleus classification and recognition of uterine cervical Pap-Smears using fuzzy ART algorithm. Proc 6th Int Conf Simul Evol Learning, Lect Notes Comput Sci 4247:560–567
Kim KB, Song DH, Woo YW (2007) Nucleus segmentation and recognition of uterine cervical Pap-smears. Proc 11th RSFDGrC(2007), Lect Notes Comput Sci 4482:153–160
Klinkhamer PJ, Meerding WJ, Rosier PF, Hanselaar AG (2003) Liquid based cervical cytology: a review of the literature with methods of evidence-based medicine. Cancer Cytopathol 99(5):263–271
Lassouaoui N, Hamami L (2003) Genetic algorithms and multifractal segmentation of cervical cell images. Proc 7th Int Symp Signal Processing Appl 2:1–4
Lezoray O, Cardot H (2002) Cooperation of colour pixel classification schemes and colour watershed: a study for microscopic images. IEEE Trans Image Process 11(7):783–789CrossRef
Li Z, Najarian K (2001) Automated classification of Pap smear tests using neural networks. Proc Int Joint Conf Neural Networks. Washington, Dc 4:2899–2901. Accessed 15–19 July 2001
Li Z, Najarian K (2007) Biomedical image segmentation based on shape stability. In: Proceedings of the 14th IEEE International Conference on Image Process (ICIP 2007), San Antonio, pp 281–284
Lin CH, Chan YK, Chen CC (2009) Detection and segmentation of cervical cell cytoplast and nucleus. Int J Imag Syst Technol 19(3):260–270CrossRef
MacAulay C, Palcic B (1988) A comparison of some quick and simple threshold selection methods for stained cells. Anal Quant Cytol Histol 10(2):134–138
Malm P, Brun A (2009) Closing curves with Riemannian dilation: application to segmentation in automated cervical cancer screening. Proc 5th Int Symp Visual Comput 2009, Lect Notes Comput Sci 5875:337–346
Marinakis Y, Marinaki M, Dounias G (2008) Particle swarm optimization for Pap-smear diagnosis. Expert Syst Appl 35:1645–1656CrossRef
Marinakis Y, Dounias G, Jantzen J (2009) Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbour classification. Comput Biol Med 39:69–78CrossRef
Mat Isa NA, Mashor MY, Othman NH (2008) An automated cervical pre-cancerous diagnostic system. Artif Intell Med 42:1–11CrossRef
Mustafa N, Mat Isa NA, Mashor MY (2009) Automated multicells segmentation of ThinPrep® image using modified seed based region growing algorithm. Biomed Soft Comput Hum Sci 14(2):41–47
Nallaperumal K, Krishnaveni K (2008) Watershed segmentation of cervical images using multiscale morphological gradient and HSI colour space. Int J Imag Sci Eng (IJISE) 2(2):212–216
Oliver LH, Poulsen RS, Toussaint GT, Louis C (1979) Classification of atypical cells in the automatic cytoscreening for cervical cancer. Pattern Recognit 11:205–212MATHCrossRef
Papanicolaou GN (1942) A new procedure for staining vaginal smears. Science 95(2469):438–439CrossRef
Plissiti ME, Nikou C (2011) Cell nuclei segmentation by learning a physically based deformable model. In: Proceedings of the 17th international conference on Digital Signal Processing (DSP’11) Corfu, Greece. Accessed 6-8 July 2011
Plissiti ME, Nikou C, Charchanti A (2010a) Watershed-based segmentation of cell nuclei boundaries in Pap smear images. In: Proceedings of the 10th IEEE international conference on Information Technology Applications in Biomedicine (ITAB’10), Corfu, Greece. Accessed 3–5 November 2010
Plissiti ME, Nikou C, Charchanti A (2010b) Accurate localization of cell nuclei in pap smear images using gradient vector flow deformable models. In: Proceeding of the 3rd international conference on Bio-inspired Signals and Systems (BIOSIGNALS), Valencia, Spain, pp 284–289. Accessed 20–23 January 2010
Plissiti ME, Nikou C, Charchanti A (2011a) Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering. IEEE Trans Inf Technol Biomed 15(2):233–241CrossRef
Plissiti ME, Nikou C, Charchanti A (2011b) Combining shape, texture and intensity features for cell nuclei extraction in pap smear images. Pattern Recognit Lett 32:838–853CrossRef
Poulsen RS, Pedron I (1995) Region of interest finding in reduced resolution colour imagery-application to cancer cell detection. Pattern Recognit 28(11):1645–1655CrossRef
Riana D, Murni A (2009) Performance evaluation of Pap smear cell image classification using quantitative and qualitative features based on multiple classifiers. In: Proceedings of the international conference on Advanced Computer Science and Information Systems (ACSIS’09) Jakarta, Indonesia pp 38–42. Accessed 7–8 December 2010
Sobrevilla P, Montseny E, Lerma E (2008) A fuzzy-based automated cell detection system for color Pap smear tests –FACSDS. In: Fuzzy sets and their extensions: representation, aggregation and models, studies in fuzziness and soft computing. Springer Berlin, Heidelberg, 220:657–674CrossRef
Solomon D, Davey D, Kurman R, Moriarty A, O’Connor D, Prey M, Raab S, Sherman M, Wilbur D, Wright T, Young N (2001) The 2001 Bethesda system: terminology for reporting results of cervical cytology. J Am Med Assoc 287:2114–2119CrossRef
Tsai MH, Chan YK, Lin ZZ, Yang-Mao SF, Huang PC (2008) Nucleus and cytoplast contour detector of cervical smear image. Pattern Recognit Lett 29:1441–1453CrossRef
Vaschetto F, Montseny E, Sobrevilla P, Lerma E (2009) THREECOND: An automated and unsupervised three colour fuzzy-based algorithm for detecting nuclei in cervical Pap smear images. In: Proceedings of the 9th international conference on intelligent systems design and applications, Washington DC, pp 1359–1364
Walker RF, Jackway P, Lovell B, Longstaff ID (1994) Classification of cervical cell nuclei using morphological segmentation and textural feature extraction. In: Proceedings of the 2nd Australian and New Zealand conference on intelligent information systems, Brisbane, pp 297–301
Wu HS, Gil J, Barba J (1998a) Optimal segmentation of cell images. Proc IEE Vision,Image Signal Process 145(1):50–56CrossRef
Wu HS, Barba J, Gil J (1998b) A parametric fitting algorithm for segmentation of cell images. IEEE Trans Biomed Eng 45(3):400–407CrossRef
Yang-Mao SF, Chan YK, Chu YP (2008) Edge enhancement nucleus and cytoplast contour detector of cervical smear images. IEEE Trans Syst Man Cybernet B-Cybernet 38(2):353–366CrossRef
Zhang J, Liu Y (2004) Cervical cancer detection using SVM based feature screening. In: Proceeding of the 7th international conference on Medical Image Computing and Computer Aided Intervention (MICCAI’04), Saint-Malo, France pp 873–880
Metadaten
Titel
A Review of Automated Techniques for Cervical Cell Image Analysis and Classification
verfasst von
Marina E. Plissiti
Christophoros Nikou
Copyright-Jahr
2013
Verlag
Springer Netherlands
Buch
Biomedical Imaging and Computational Modeling in Biomechanics

Print ISBN: 978-94-007-4269-7

Electronic ISBN: 978-94-007-4270-3

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-94-007-4270-3_1

Neuer Inhalt

    Bildnachweise