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A novel method of cervical cell image segmentation via region merging and SLIC

Published: 08 December 2016 Publication History

Abstract

Considering that the existing methods of cell segmentation are sensitive to noise, in this paper, a novel method was proposed to obtain the contour of cervical cell accurately and robustly. Firstly, the mean shift algorithm was utilized to smooth the cell image. Then, the initial contour of cervical cell was extracted by an adaptive threshold algorithm. Secondly, SLIC (Simple Linear Iterative Clustering, SLIC) was applied to the smoothed cell image to get the superpixels of the whole cell image. Finally, based on the initial contour, we can automatically set some marks for background and foreground in a cell image full of superpixels. Then the superpixels were merged by the rule of maximal similarity. A key property of our superpixel merging is that it does not require a preset threshold, and the non-marker background regions are merged with the marked area automatically, while the non-marker superpixels are identified to avoid from being merged into background. We validate our method via the cervical cell image database and demonstrate that our method can extract the contour of cytoplasm from a single-cell cervical smear image accurately in a relatively short time.

References

[1]
Moo E K, Abusara Z, Osman N A A, et al.: Dual photo excitation microscopy and image thresshold segmentation in live cell imaging during compression testing. Journal of Biomechanics 46 (2013) 2024--2031.
[2]
Malladi R, Sethian J A, Vemuri B C.: Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 158--175.
[3]
XU C Y, PRINCE J L.: Snakes, shapes and gradient vector flow. IEEE Trans on Image Pocessing 7 (1998) 359--369.
[4]
Li K, Lu Z, Liu W Y, et al.: Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake. Pattern recognition 45 (2012) 1255--1246.
[5]
REN X F, MALIK J. Learning a classification model for segmentation{C}// Proceeding of the 9th IEEE International Conference on Computer Vision. Washington DC: IEEE Computer Society, 2003: 10--17.
[6]
Felzenszwalb P F, Huttenlocher D P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59 (2004) 167--181.
[7]
Chanta R, Shaji A, Smith K, et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (2012) 2274--2282.
[8]
HI Jian-bo, MALIK J.: Normalized cuts and image segmentation. IEEE Trans on Pattern Analysis and Machine Intelligence 22 (2000) 888--905.
[9]
Sonka M, Hlavac V, Boyle R. Image Processing, Analysis and Computer Vision{M}. Thomson,2007: 15--72.
[10]
Tan L Y, Wang S J.: A Fast Image Segmentation Based on Path-based Similarity and Dual Super-pixel Sets. Acta Automatica Sinica 39 (2013) 1653--1664.
[11]
Wang C Y, Chen J Z, Li W.: Superpixel segmentation algorithms review. Journal of Application Research of Computers 31 (2014) 6--12.
[12]
Rao Q, Wen H, Yu W, et al.: Review about superpixels and its applications. Computer and Information Technology 21 (2013) 1--3.
[13]
ACHANTA R, SHAJI A, SMITH K, et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans on Pattern Analysis and Machine Intelligence 34 (2012) 2274--2282.
[14]
Lucchi A, Smith K, Achanta R, et al. A fully automated approach to segmentation of irregularly shaped cellular structures in EM images{C}// Medical Image Computing and Computer Assisted Intervention. Berlin: Springer-Verlag, 2010: 463--471.
[15]
Comaniciu D, Ramesh V, Meer P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003) 564--577.
[16]
Luchhi A, Smith K, Achanta R, etal.: Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features. IEEE Trans on Medical Imaging 31 (2012) 474--486.
[17]
Zhang Y Y, Liu X W, Liu F T, etal.: Color Image Segmentation Based on Improved SLIC Method. Computer Engineering 41(2015) 205--209.
[18]
Comaniciu D, Meer P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (2002) 603--619.

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  • (2024)Review of cervical cell segmentationMultimedia Tools and Applications10.1007/s11042-024-19799-0Online publication date: 2-Aug-2024

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  1. A novel method of cervical cell image segmentation via region merging and SLIC

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    cover image ACM Other conferences
    SoICT '16: Proceedings of the 7th Symposium on Information and Communication Technology
    December 2016
    442 pages
    ISBN:9781450348157
    DOI:10.1145/3011077
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    Published: 08 December 2016

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    Author Tags

    1. image segmentation
    2. region merging
    3. simple linear iterative clustering

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    SoICT '16 Paper Acceptance Rate 58 of 132 submissions, 44%;
    Overall Acceptance Rate 147 of 318 submissions, 46%

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    • (2024)Review of cervical cell segmentationMultimedia Tools and Applications10.1007/s11042-024-19799-0Online publication date: 2-Aug-2024

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