2013, 10(2): 279-294. doi: 10.3934/mbe.2013.10.279

Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images

1. 

Universitat Politècnica de València, Plaza Ferrándiz y Carbonell, n.2, Alcoy (Alicante), 03801, Spain, Spain

Received  April 2012 Revised  October 2012 Published  January 2013

Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows a segmentation with the largest area in the cell. We study different wavelet families and we conclude that the wavelet db1 is the best and it can serve for posterior works on blood pathologies. The proposed method generates goods results when it is applied on several images. Finally, the proposed algorithm made in MatLab environment is verified for a selected blood cells.
Citation: Macarena Boix, Begoña Cantó. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images. Mathematical Biosciences & Engineering, 2013, 10 (2) : 279-294. doi: 10.3934/mbe.2013.10.279
References:
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D. Anoraganingrum, Cell segmentation with median filter and mathematical morphology operation,, in, 9 (1999), 183.   Google Scholar

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D. A. Bader, J. Jájá, D. Harwood and L. L. Davis, Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study,, Journal of Supercomputing, 10 (1996), 141.   Google Scholar

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H. Chan, J. Li-Jun and B. Jiang, Wavelet transform and morphology image segmentation algorism for blood cell,, in, (2009), 542.   Google Scholar

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L. Costrarido, "Medical Image Analysis Methods: Medical-image Processing and Analysis for CAD Systems,", $2^{nd}$ edition, (2005).   Google Scholar

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D. L. Donoho, An ideal spatial adaptation by wavelet shrinkage,, Biometrika, 81 (1994), 425.  doi: 10.1093/biomet/81.3.425.  Google Scholar

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D. L. Donoho, De-noising by soft thresholding,, IEEE. Trans. Inf. Theory, 41 (1995), 613.  doi: 10.1109/18.382009.  Google Scholar

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F. Gibou, D. Levy, C. Cárdenas, P. Liu and A. Boyer, Partial differential equations-based segmentation for radiotherapy treatment planning,, Mathematical Biosciences and Engineering, 2 (2005), 209.  doi: 10.3934/mbe.2005.2.209.  Google Scholar

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V. Grau, A. U. Mewes, M. Alcáñiz, R. Kikinis and S. K. Warfield, Improved watershed transform for medical image segmentation using prior information,, IEEE. Trans. Med. Imaging, 23 (2004), 447.   Google Scholar

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K. B. How, A. S. Kok Bin, N. T. Siong and K. K. Soo, Red blood cell segmentation utilizing various image segmentation techniques,, in, (2006).   Google Scholar

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K. Jiang, Qing-Min and S. Y. Dai, A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering,, in, (2003).   Google Scholar

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R. S. Kumar, A. Verma and J. Singh, Color image segmentation and multi-level thresholding by maximization of conditional entrophy,, Int. Sig. Processing, 3 (2006).   Google Scholar

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J. Liang, S. Elangovan and J. Devotta, Application of wavelet transform in travelling wave protection,, Int. Elect. Pow. Energy, 22 (2000), 537.   Google Scholar

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D. Liu and T. Chen, DISCOV: A framework for discovering objects in video,, Int. Trans. Multimedia, 10 (2008), 200.   Google Scholar

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S. Mallat, Zero-crossings of a wavelet transform,, Int. Trans. Inf. Theory, 37 (1991), 1019.  doi: 10.1109/18.86995.  Google Scholar

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S. Mallat and W. L. Hwang, Singularity detection and processing with wavelets,, Int. Trans. Inf. Theory, 38 (1992), 617.  doi: 10.1109/18.119727.  Google Scholar

[17]

S. Mallat and S. Zhong, Charaterization of signals from multiscale edges,, Int. Trans. Patt. Anal. Mac. Int., 14 (1992), 710.   Google Scholar

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B. Ninga, D. Qinyuna, H. Darena and F. Jib, Image coding based on multiband wavelet and adaptive quad-tree partition,, Journal of Computational and Applied Mathematics, 195 (2006), 2.  doi: 10.1016/j.cam.2005.07.013.  Google Scholar

[19]

P.Soille, "Morphological Image Analysis: Principles and Applications,", $2^{nd}$ edition, (1999).   Google Scholar

[20]

M. Wang, X. Zhou, F. Li, J. Huckins, R. W. King and S. T. Wong, Novel cell segmentation and online learning algorithms for cell phase identification in automated time-lapse microscopy,, in, (2007).   Google Scholar

[21]

M. A. Wani, D. Zhang and H. Arabnia, Parallel edge-region-based segmentation algorithm targeted at reconfigurable multiRing network,, Journal of Supercomputing, 25 (2003), 43.   Google Scholar

[22]

J. Wu, P. Zeng, Y. Zhou and C. Olivier, A novel color segmentation method and its application to white blood cell image analysis,, in, (2006).   Google Scholar

[23]

Y. Zhai, D. Zhang, J. Sun and B. Wu, A novel variational model for image segmentation,, Journal of Computational and Applied Mathematics, 235 (2011), 2234.  doi: 10.1016/j.cam.2010.10.020.  Google Scholar

[24]

J. Y. Zhou, X. Fang and K. Ghosh, Multiresolution filtering with application to image segmentation,, Math. Comp. Model., 24 (1996), 177.  doi: 10.1016/0895-7177(96)00121-5.  Google Scholar

[25]

J. Y. Zhou, E. P. Ong and C. C. Ko, Video object segmentation and tracking for content-based video coding,, in, (2000).   Google Scholar

show all references

References:
[1]

D. Anoraganingrum, Cell segmentation with median filter and mathematical morphology operation,, in, 9 (1999), 183.   Google Scholar

[2]

D. A. Bader, J. Jájá, D. Harwood and L. L. Davis, Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study,, Journal of Supercomputing, 10 (1996), 141.   Google Scholar

[3]

C. C. Chiang, Y. P. Hung and G. C. Lee, A learning state-space model for image retrieval,, IEEE. Trans. Mult., 10 (2008), 1.   Google Scholar

[4]

H. Chan, J. Li-Jun and B. Jiang, Wavelet transform and morphology image segmentation algorism for blood cell,, in, (2009), 542.   Google Scholar

[5]

L. Costrarido, "Medical Image Analysis Methods: Medical-image Processing and Analysis for CAD Systems,", $2^{nd}$ edition, (2005).   Google Scholar

[6]

D. L. Donoho, An ideal spatial adaptation by wavelet shrinkage,, Biometrika, 81 (1994), 425.  doi: 10.1093/biomet/81.3.425.  Google Scholar

[7]

D. L. Donoho, De-noising by soft thresholding,, IEEE. Trans. Inf. Theory, 41 (1995), 613.  doi: 10.1109/18.382009.  Google Scholar

[8]

F. Gibou, D. Levy, C. Cárdenas, P. Liu and A. Boyer, Partial differential equations-based segmentation for radiotherapy treatment planning,, Mathematical Biosciences and Engineering, 2 (2005), 209.  doi: 10.3934/mbe.2005.2.209.  Google Scholar

[9]

V. Grau, A. U. Mewes, M. Alcáñiz, R. Kikinis and S. K. Warfield, Improved watershed transform for medical image segmentation using prior information,, IEEE. Trans. Med. Imaging, 23 (2004), 447.   Google Scholar

[10]

K. B. How, A. S. Kok Bin, N. T. Siong and K. K. Soo, Red blood cell segmentation utilizing various image segmentation techniques,, in, (2006).   Google Scholar

[11]

K. Jiang, Qing-Min and S. Y. Dai, A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering,, in, (2003).   Google Scholar

[12]

R. S. Kumar, A. Verma and J. Singh, Color image segmentation and multi-level thresholding by maximization of conditional entrophy,, Int. Sig. Processing, 3 (2006).   Google Scholar

[13]

J. Liang, S. Elangovan and J. Devotta, Application of wavelet transform in travelling wave protection,, Int. Elect. Pow. Energy, 22 (2000), 537.   Google Scholar

[14]

D. Liu and T. Chen, DISCOV: A framework for discovering objects in video,, Int. Trans. Multimedia, 10 (2008), 200.   Google Scholar

[15]

S. Mallat, Zero-crossings of a wavelet transform,, Int. Trans. Inf. Theory, 37 (1991), 1019.  doi: 10.1109/18.86995.  Google Scholar

[16]

S. Mallat and W. L. Hwang, Singularity detection and processing with wavelets,, Int. Trans. Inf. Theory, 38 (1992), 617.  doi: 10.1109/18.119727.  Google Scholar

[17]

S. Mallat and S. Zhong, Charaterization of signals from multiscale edges,, Int. Trans. Patt. Anal. Mac. Int., 14 (1992), 710.   Google Scholar

[18]

B. Ninga, D. Qinyuna, H. Darena and F. Jib, Image coding based on multiband wavelet and adaptive quad-tree partition,, Journal of Computational and Applied Mathematics, 195 (2006), 2.  doi: 10.1016/j.cam.2005.07.013.  Google Scholar

[19]

P.Soille, "Morphological Image Analysis: Principles and Applications,", $2^{nd}$ edition, (1999).   Google Scholar

[20]

M. Wang, X. Zhou, F. Li, J. Huckins, R. W. King and S. T. Wong, Novel cell segmentation and online learning algorithms for cell phase identification in automated time-lapse microscopy,, in, (2007).   Google Scholar

[21]

M. A. Wani, D. Zhang and H. Arabnia, Parallel edge-region-based segmentation algorithm targeted at reconfigurable multiRing network,, Journal of Supercomputing, 25 (2003), 43.   Google Scholar

[22]

J. Wu, P. Zeng, Y. Zhou and C. Olivier, A novel color segmentation method and its application to white blood cell image analysis,, in, (2006).   Google Scholar

[23]

Y. Zhai, D. Zhang, J. Sun and B. Wu, A novel variational model for image segmentation,, Journal of Computational and Applied Mathematics, 235 (2011), 2234.  doi: 10.1016/j.cam.2010.10.020.  Google Scholar

[24]

J. Y. Zhou, X. Fang and K. Ghosh, Multiresolution filtering with application to image segmentation,, Math. Comp. Model., 24 (1996), 177.  doi: 10.1016/0895-7177(96)00121-5.  Google Scholar

[25]

J. Y. Zhou, E. P. Ong and C. C. Ko, Video object segmentation and tracking for content-based video coding,, in, (2000).   Google Scholar

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