Abstract
This paper presents a gradient direction edge enhancement based contour (GDEEBC) detector to segment the nucleus and cytoplasm from each cervical smear image. In order to eliminate noise from the image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noise but still preserve the edge sharpness of an object. In addition, a bi-group enhancer is proposed to make a clear-cut separation for the pixels lying between two objects. Finally, a gradient direction (GD) enhancer is presented to suppress the gradients of noise and to brighten the gradients of object contours as well. The experimental results show that all the techniques proposed above have impressive performances. In addition to cervical smear images, these proposed techniques can also be utilized in object segmentation of other types of images.
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
Davies, E.: Machine Vision: Theory, Algorithms and Practicalities, vol. 5. Academic Press, London (1990)
Gonzalez, R., Woods, R.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)
National Health Insurance.: Cause of Mortality Report 2004. Health and National Health Insurance statistics information (2004), http://www.doh.gov.tw/statistic/
Martin, E.: Cervical smear Classification. Master’s thesis, Technical University of Denmark: Oersted-DTU, Automation (2003)
Mat-Isa, N.A., Mashor, M.Y., Othman, N.H.: Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer. Int. J. Computer, the Internet and Management 13(1), 61–70 (2005)
Norup, J.: Classification of Cervical smear Data by Transductive Neuro-Fuzzy Methods. Master’s thesis, Technical University of Denmark: Oersted-DTU, Automation (2005)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66 (1979)
Russo, F., Lazzari, A.: Color Edge Detection in Presence of Gaussian Noise Using Nonlinear Prefiltering. IEEE Transactions on Instrumentation and Measurement 54(1), 352–358 (2005)
Sezgin, M., Sankur, B.: Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation. J. Electronic Imaging 13(1), 146–165 (2004)
Wu, H.-S., Gil, J., Barba, J.: Optimal Segmentation of Cell Images. IEE Proc. Online-Visual Image Signal Processing 145(1), 50–56 (1998)
Walker, R.F.: Adaptive Multi-Scale Texture Analysis with Application to Automated Cytology. Department of Electrical and Computer Engineering, University of Queensland, Dissertation (1997)
Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)
Yin, L., Basu, A., Chang, J.K.: Scalable Edge Enhancement with Automatic Optimization for Digital Radiographic Images. Pattern Recognition 37(7), 1407–1422 (2004)
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
Yang-Mao, SF., Chen, YF., Chan, YK., Tsai, MH., Chu, YP. (2007). Gradient Direction Edge Enhancement Based Nucleus and Cytoplasm Contour Detector of Cervical Smear Images. 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_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-77413-6_37
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)