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Detection and Characterization of Abnormal Vascular Patterns in Automated Cervical Image Analysis

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Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4292))

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Abstract

In colposcopy, mosaic and punctation are two major abnormal vessels associated with cervical intraepithelial neoplasia (CIN). Detection and characterization of mosaic and punctation in digital cervical images is a crucial step towards developing a computer-aided diagnosis (CAD) system for cervical cancer screening and diagnosis. This paper presents automated techniques for detection and characterization of mosaic and punctation vessels in cervical images. The techniques are based on iterative morphological operations with various sizes of structural elements, in combination with adaptive thresholding. Information about color, region, and shape properties is used to refine the detection results. The techniques have been applied to clinical data with promising results.

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References

  1. Reid, R., Scalzi, P.: Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infections from high-grade cervical intraepithelial neoplasia. Am. J. Obstet. Gynecol. 153(6), 611–618 (1985)

    Google Scholar 

  2. Ji, Q., Engel, J., Craine, E.: Texture analysis for classification of cervix lesions. IEEE Trans. Med. Imaging 19(11), 1144–1149 (2000)

    Article  Google Scholar 

  3. Srinivasan, Y., Hernes, D., Tulpule, B., Yang, S., et al.: A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features. In: Proc. of SPIE Medical Imaging, vol. 5747, pp. 995–1003 (2005)

    Google Scholar 

  4. Li, W.: Automated image analysis of uterine cervical images. In: Proc. of SPIE Medical Imaging (submitted, 2007)

    Google Scholar 

  5. Thackray, B.D., Nelson, A.C.: Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Trans. Med. Imaging 12(3), 385–392 (1993)

    Article  Google Scholar 

  6. Mikhail, M.S., Romney, S.L.: Computerized Measurement of Intercapillary Distance using Image Analysis in Women with Cervical Intraepithelial Neoplasia: Correlation with Severity. Obstet. Gynecol. 95(4 Supplement), 2S–3S (2002)

    Google Scholar 

  7. Ferris, D., Cox, J.T., O’Connor, D.M., Wright, V.C., Foerster, J.: Modern Colposcopy, Textbook and Atlas. In: Second American Society for Colposcopy and Cervical Pathology (2004)

    Google Scholar 

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Authors and Affiliations

  1. STI Medical Systems, 733 Bishop St. Honolulu, Hawaii, 96813, USA

    Wenjing Li & Allen Poirson

Authors
  1. Wenjing Li
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  2. Allen Poirson
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, W., Poirson, A. (2006). Detection and Characterization of Abnormal Vascular Patterns in Automated Cervical Image Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_63

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  • DOI: https://doi.org/10.1007/11919629_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48626-8

  • Online ISBN: 978-3-540-48627-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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