Abstract
Cytologic screening has been widely used for controlling the prevalence of cervical cancer. Errors from sampling, screening and interpretation, still concealed some unpleasant results. This study aims at designing a cellular image analysis system based on feasible and available software and hardware for a routine cytologic laboratory. Totally 1814 cellular images from the liquid-based cervical smears with Papanicolaou stain in 100x, 200x, and 400x magnification were captured by a digital camera. Cell images were reviewed by pathologic experts with peer agreement and only 503 images were selected for further study. The images were divided into 4 diagnostic categories. A PC-based cellular image analysis system (PCCIA) was developed for computing morphometric parameters. Then support vector machine (SVM) was used to classify signature patterns. The results show that the selected 13 morphometric parameters can be used to correctly differentiate the dysplastic cells from the normal cells (p<0.001). Additionally, SVM classifier has been demonstrated to be able to achieve a high accuracy for cellular classification. In conclusion, the proposed system provides a feasible and effective tool for the evaluation of gynecologic cytologic specimens.
Keywords
- Support Vector Machine
- Support Vector Machine Classifier
- Morphometric Parameter
- False Negative Case
- Cellular Image
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
DeMay, R.M.: Common problems in Papanicolaou smear interpretation. Arch. Pathol. Lab. Med. 121, 229–238 (1997)
Hartmann, K.E., Nanda, K., Hall, S., Myers, E.: Technologic advances for evaluation of cervical cytology: is newer better? Obstet. Gynecol. Surv. 56, 765–774 (2001)
Stoler, M.H.: Advances in cervical screening technology. Mod. Pathol. 13, 275–284 (2000)
Dawson, A.E.: Can we change the way we screen? the ThinPrep Imaging System. Cancer 102, 340–344 (2004)
Kardos, T.F.: The FocalPoint System: FocalPoint slide profiler and FocalPoint GS. Cancer 102, 334–339 (2004)
Mango, L.J.: Reducing false negatives in clinical practice: the role of neural network technology. Am. J. Obstet. Gynecol. 175, 1114–1119 (1996)
Taylor, R.N., Gagnon, M., Lange, J., Lee, T., Draut, R., Kujawski, E.: CytoView. A prototype computer image-based Papanicolaou smear proficiency test. Acta. Cytol. 43, 1045–1051 (1999)
Doornewaard, H., van der Schouw, Y.T., van der Graaf, Y., Bos, A.B., van den Tweel, J.G.: Observer variation in cytologic grading for cervical dysplasia of Papanicolaou smears with the PAPNET testing system. Cancer 87, 178–183 (1999)
Harris, M.V., Cason, Z., Benghuzzi, H., Tucci, M.: Cytomorphological assessment of benign and malignant dense hyperchromatic groups in cervicovaginal smears. Biomed. Sci. Instrum. 36, 349–354 (2000)
Nunobiki, O., Sato, M., Taniguchi, E., Tang, W., Nakamura, M., Utsunomiya, H., Nakamura, Y., Mori, I., Kakudo, K.: Color image analysis of cervical neoplasia using RGB computer color specification. Anal. Quant. Cytol. Histol. 24, 289–294 (2002)
Arora, B., Setia, S., Rekhi, B.: Role of computerized morphometric analysis in diagnosis of effusion specimens. Diagn. Cytopathol. 34, 670–675 (2006)
Wang, S.L., Wu, M.T., Yang, S.F., Chan, H.M., Chai, C.Y.: Computerized nuclear morphometry in thyroid follicular neoplasms. Pathol. Int. 55, 703–706 (2005)
Murata, S., Mochizuki, K., Nakazawa, T., Kondo, T., Nakamura, N., Yamashita, H., Urata, Y., Ashihara, T., Katoh, R.: Morphological abstraction of thyroid tumor cell nuclei using morphometry with factor analysis. Microsc. Res. Tech. 61, 457–462 (2003)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. Software (2001), available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Naib, Z.M.: Cytopathology, 4th edn., pp. 65–67. Little, Brown and Company, Bosten, New York (1996)
Ramesh, B.V., Padaki, V.C., Hegde, K.S., Hazarika, D., Verghese, C.A.: An interactive image analysis system for quantitative cytology & to classify cervical cells. Indian J. Med. Res. 96, 338–343 (1992)
Kirillov, V., Stebenyaeva, E., Paplevka, A., Demidchik, E.: A rapid method for diagnosing regional metastases of papillary thyroid cancer with morphometry. Microsc. Res. Tech 69, 721–728 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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. In: Zhang, D. (eds) Medical Biometrics. ICMB 2008. Lecture Notes in Computer Science, vol 4901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77413-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-77413-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77410-5
Online ISBN: 978-3-540-77413-6
eBook Packages: Computer ScienceComputer Science (R0)