CORRESPONDENCE: Image SegmentationNo Access

IMAGE SEGMENTATION BASED ON EDGE DETECTION AND REGION GROWING FOR THINPREP-CERVICAL SMEAR

Department of Computer Science and Information Engineering, National Taichung Institute of Technology, No. 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, R.O.C.

Search for more papers by this author

and

CHUN-CHIEH CHEN

Department of Electrical Engineering, National Chung Hsing University, No. 250, Kuokuang Rd., Taichung, Taiwan, R.O.C.

Search for more papers by this author

https://doi.org/10.1142/S0218001410008305Cited by:13 (Source: Crossref)

Abstract

This study has developed an object detection and segmentation technique for processing cytoplasm and cell nucleus on ThinPrep-cervical smear images at various magnifications. Both edge detection techniques and region growing for adaptive threshold were applied to a segment cell nucleus, a cytoplasm, and backgrounds using a cervical cell image.

To validate the accuracy and feasibility of the proposed method, we took a variety of cervical cell images to perform a series of experiments. The images were of superficial cells, intermediate cells, and abnormal cells, with each taken from ThinPrep smears at various magnifications. The results indicate that the proposed method can automatically segment cell nucleus and cytoplasm regions while accurately extracting object contours. These results can serve as a reference for examiners of cell pathologies.

Keywords:

References

A. Z. Arifin and A. Asano, Patt. Recogn. Lett. 27, 1515 (2006), DOI: 10.1016/j.patrec.2006.02.022. Crossref, Web of Science, Google Scholar
S. Aroraet al., Patt. Recogn. Lett. 29, 119 (2008), DOI: 10.1016/j.patrec.2007.09.005. Crossref, Web of Science, Google Scholar
J. F. Canny, IEEE Trans. Patt. Anal. Mach. Intell. 8(6), 679 (1986). Web of Science, Google Scholar
C. A. Davatzikos and J. L. Prince, IEEE Trans. Med. Imag. 14, 65 (1995), DOI: 10.1109/42.370403. Crossref, Web of Science, Google Scholar
L. Ding and A. Goshtasby, Patt. Recogn. 34, 721 (2001), DOI: 10.1016/S0031-3203(00)00023-6. Crossref, Web of Science, Google Scholar
J. Ding, R. Ma and S. Chen, IEEE Trans. Imag. Process. 17(2), (2008). Google Scholar
L. Donget al., Patt. Recogn. Lett. 29, 1311 (2008), DOI: 10.1016/j.patrec.2008.02.001. Crossref, Web of Science, Google Scholar
R. C. Gonzalez and R. E. Woods , Digital Image Processing , 2nd edn. ( Prentice Hall , 2002 ) . Google Scholar
K. Hariset al., IEEE Trans. Imag. Process. 7(12), 1684 (1998), DOI: 10.1109/83.730380. Crossref, Web of Science, Google Scholar
Q. Hu, Z. Hou and W. L. Nowinski, IEEE Trans. Imag. Process. 15(1), 228 (2006). Web of Science, Google Scholar
M. Kass, A. Witkin and D. Terzopoulos, Int. J. Comput. Vis. 1, 321 (1998), DOI: 10.1007/BF00133570. Crossref, Web of Science, Google Scholar
C. Li et al. , Level set evolution without re-initialization: A new variational formulation , Proc. 2005 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR'05) ( 2005 ) . Google Scholar
N. A. Mat-Isa, M. Y. Mashor and N. H. Othman, The Computer, the Internet and Manag. 13(1), 61 (2005). Web of Science, Google Scholar
S. Osher and J. A. Sethian, J. Comput. Phy. 79, 12 (1988), DOI: 10.1016/0021-9991(88)90002-2. Crossref, Web of Science, Google Scholar
N. Otsu, IEEE Trans. Syst. Man. Cybern. 9, 62 (1979). Crossref, Web of Science, Google Scholar
M. Sezgin and B. Sankur, J. Electron. Imag. 13(1), 146 (2004), DOI: 10.1117/1.1631315. Web of Science, Google Scholar
E. Sifakis, C. Garcia and G. Tziritas, J. Vis. Commun. Imag. Repre. 13(1), 44 (2002), DOI: 10.1006/jvci.2001.0474. Crossref, Web of Science, Google Scholar
T. Toda and N. Kimura, IEEE Trans. Imag. Process. 10(2), 266 (2001). Web of Science, Google Scholar
D. Wang, Patt. Recogn. 30(12), 2043 (1997), DOI: 10.1016/S0031-3203(97)00015-0. Crossref, Web of Science, Google Scholar
C. Xu and J. L. Prince, IEEE Trans. Imag. Process. 7(3), 359 (1998). Web of Science, Google Scholar