poster

An online segmentation tool for cervicographic image analysis

Authors:

L. Rodney Long,

George R. ThomaAuthors Info & Claims

IHI '10: Proceedings of the 1st ACM International Health Informatics Symposium

Pages 425 - 429

https://doi.org/10.1145/1882992.1883056

Published: 11 November 2010 Publication History

Abstract

Cervicography is an important visual screening method for cervical cancer prevention. Automatic segmentation of clinically significant regions in acquired images may provide valuable assistance toward research in cervical cancer detection. This paper presents a Web-accessible cervicographic image segmentation system that incorporates several novel segmentation algorithms developed for particular tissue types and landmarks. The system combines the advantages of two commonly used programming languages, Matlab and Java. It relieves the research groups in academic institutes from the heavy burden of re-developing the sophisticated segmentation algorithms originally implemented in Matlab, while allowing medical experts to evaluate the segmentation algorithms using, perhaps, their own image data acquired at remote locations. It offers attractive properties of flexibility, extensibility and Web-accessibility in a prototype image processing application. The system is integrated with other applications that have been developed for uterine cervix image analysis at the U.S. National Library of Medicine. The architecture and concept of this system are generalizable and can be applied to different medical image processing tasks.

References

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Stafl, C. 1981. Cervicography: a new method for cervical cancer detection. Am. J. Obstet. Gynecol. 139, 7 (1981), 815--825.

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Bratti, M. C., Rodriguez, A. C., Schiffman, M., Hildesheim, A., Morales, J., Alfaro, M., etc. 2004. Description of a seven-year prospective study of human papillomavirus infection and cervical neoplasia among 10 000 women in Guanacaste. Costa Rica. Rev Panam Salud Publica 15, 2 (2004), 75--89.

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Jeronimo, J., Schiffman, M., Long, L. R., Neve, L., Antani, S. 2004. A tool for collection of region based data from uterine cervix images for correlation of visual and clinical variables related to cervical neoplasia. In Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems (Maryland, USA, June 24-25, 2004). CBMS'04. IEEE, 558--562. DOI= http://doi.ieeecomputersociety.org/10.1109/CBMS.2004.1311774.

Digital Library

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Zhu, Y., Wang, W., Huang, X., Lopresti, D., Long, L. R., Antani, S., Xue, Z., Thoma, G. R. 2008. Balancing the role of priors in multi-observer segmentation evaluation. Journal of Signal Processing System. 55, 1--3 (2008), 158--207. DOI= http://dx.doi.org/10.1007/s11265-008-0215-5.

Digital Library

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http://archive.nlm.nih.gov/proj/impactOfTools.html, last accessed on May 30th, 2010.

[7]

Greenspan, H., Gordon, S., Zimmerman, G., Lotenberg, S., Jeronimo, J., Antani, S., Long, L.R. 2009. Automatic detection of anatomical landmarks in uterine cervix images. IEEE Transactions on Medical Imaging. 28, 3 (Mar. 2009), 454--468. DOI= http://dx.doi.org/10.1109/TMI.2008.2007823.

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Xue, Z., Antani, S., Long, L. R., Thoma, G. R. 2007. Comparative performance analysis of cervix ROI extraction and specular reflection removal algorithms for uterine cervix image analysis. In Proceedings of SPIE Medical Imaging (California, USA, February 17--22, 2007). SPIE, 65124I-1-9. DOI= 10.1117/12.709588.

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Zimmerman, G. and Greenspan, H. 2006. Automatic detection of specular reflections in uterine cervix images. In Proceedings of SPIE Medical Imaging (California, USA, February 11-16, 2006). SPIE, 2037--2045. DOI= http://dx.doi.org/10.1117/12.653089

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Zimmerman, G., Gordon, S., Greenspan, H. 2004. Content-based indexing and retrieval of uterine cervix images. In Proceedings of the 23rd IEEE Convention of Electrical and Electronics Engineers in Israel (Tel-Aviv, Israel, Sept 6-7, 2004). IEEE, 181--185.

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Huang, X., Wang, W., Xue, Z., Antani, S., Long, L. R., Jeronimo, J. 2008. Tissue classification using cluster features for lesion detection in digital cervigrams. In Proceedings of SPIE Medical Imaging (California, USA, February 17-21, 2008). SPIE, 69141Z. DOI= http://dx.doi.org/10.1117/12.771088

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Helanterä, H., Salmenperä, M., Koivisto, H. 2004. Global condition monitoring system - implementing MATLAB-based analysis services. In Proceedings of the 1st International Conference on Informatics in Control, Automation and Robotics (Setúbal, Portugal, August 25-28, 2004). ICINCO'04. INSTICC Press, 300--305.

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Samsi, S., Krishnamurthy, A., Ahalt, S. 2003. A Java based web interface to MATLAB. The Seventh Annual High Performance Embedded Computing workshop (Massachusetts, USA, September 22-25, 2003). HPEC 2003. 46--47.

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Guo, J., Hughes, B., Mitra, S., Nutter, B. 2009. Ultra high resolution image coding and roi viewing using line-based backward coding of wavelet trees (L-BCWT). Picture Coding Symposium (Illinois, USA May 6-8, 2009). PCS 2009. 1-4. DOI= 10.1109/PCS.2009.5167436

Digital Library

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Lotenberg, S., Gordon, S., Greenspan, H. 2009. Shape priors for segmentation of the cervix region within uterine cervix images. J. Digital Imaging 22, 3 (June 2009), 286--296. DOI= 10.1007/s10278-008-9134-z.

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Alush, A., Greenspan, H., Goldberger, J. 2010. Automated and interactive lesion detection and segmentation in uterine cervix images. IEEE Transaction on Medical Imaging 29, 2 (2010), 488--501. DOI= http://dx.doi.org/10.1109/TMI.2009.2037201

Cited By

Tamang PGupta MThatal A(2024)Digital colposcopy image analysis techniques requirements and their role in clinical diagnosis: a systematic reviewExpert Review of Medical Devices10.1080/17434440.2024.2407549(1-15)Online publication date: 6-Oct-2024
https://doi.org/10.1080/17434440.2024.2407549
KARAPINAR ŞENTÜRK ZUZUN S(2022)An Improved Deep Learning Based Cervical Cancer Detection Using a Median Filter Based PreprocessingEuropean Journal of Science and Technology10.31590/ejosat.1045538Online publication date: 1-Jan-2022
https://doi.org/10.31590/ejosat.1045538
Zhao MZhang LWang JXia C(2022)Automated Classification of Cervical Image Based on Deep Neural Network2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS55054.2022.9858454(01-06)Online publication date: 3-Aug-2022
https://doi.org/10.1109/DDCLS55054.2022.9858454
Show More Cited By

Index Terms

An online segmentation tool for cervicographic image analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
      2. Computer vision tasks
  2. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors

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Information

Published In

cover image ACM Other conferences

IHI '10: Proceedings of the 1st ACM International Health Informatics Symposium

November 2010

886 pages

ISBN:9781450300308

DOI:10.1145/1882992

Editor:
Tiffany Veinot
University of Michigan, USA
,
General Chairs:
Ümit V. Çatalyürek
The Ohio State University, USA
,
Gang Luo
IBM Research, USA
,
Program Chairs:
Henrique Andrade
Goldman Sachs, USA
,
Neil R. Smalheiser
University of Illinois, Chicago, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGHIT: ACM Special Interest Group on Health Informatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 November 2010

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IHI '10: ACM International Health Informatics Symposium

November 11 - 12, 2010

Virginia, Arlington, USA

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Cited By

Tamang PGupta MThatal A(2024)Digital colposcopy image analysis techniques requirements and their role in clinical diagnosis: a systematic reviewExpert Review of Medical Devices10.1080/17434440.2024.2407549(1-15)Online publication date: 6-Oct-2024
https://doi.org/10.1080/17434440.2024.2407549
KARAPINAR ŞENTÜRK ZUZUN S(2022)An Improved Deep Learning Based Cervical Cancer Detection Using a Median Filter Based PreprocessingEuropean Journal of Science and Technology10.31590/ejosat.1045538Online publication date: 1-Jan-2022
https://doi.org/10.31590/ejosat.1045538
Zhao MZhang LWang JXia C(2022)Automated Classification of Cervical Image Based on Deep Neural Network2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS55054.2022.9858454(01-06)Online publication date: 3-Aug-2022
https://doi.org/10.1109/DDCLS55054.2022.9858454
Zhang BZhang QZhou HXia CWang J(2020)Automated Prediction of Cervical Precancer Based on Deep LearningProceedings of 2020 Chinese Intelligent Systems Conference10.1007/978-981-15-8450-3_52(485-494)Online publication date: 24-Sep-2020
https://doi.org/10.1007/978-981-15-8450-3_52
Fernandes KCardoso JFernandes J(2018)Automated Methods for the Decision Support of Cervical Cancer Screening Using Digital ColposcopiesIEEE Access10.1109/ACCESS.2018.28393386(33910-33927)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2839338
Nguyen KPeng BLi TChen Q(2014)Online Evaluation System of Image SegmentationPractical Applications of Intelligent Systems10.1007/978-3-642-54927-4_50(527-536)Online publication date: 19-Jul-2014
https://doi.org/10.1007/978-3-642-54927-4_50

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