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

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  • 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.
    Mathematics Subject Classification: Primary: 68U10; Secondary: 65K05.


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  • [1]

    D. Anoraganingrum, Cell segmentation with median filter and mathematical morphology operation, in "International Conference on Image Analysis and Processing," 9 (1999), 183-188.


    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), 141-168.


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


    H. Chan, J. Li-Jun and B. Jiang, Wavelet transform and morphology image segmentation algorism for blood cell, in "Industrial Electronics and Applications" ICIEA 2009, 4th IEEE Conference (2009), 542-545.


    L. Costrarido, "Medical Image Analysis Methods: Medical-image Processing and Analysis for CAD Systems," $2^{nd}$ edition, Taylor and Francis, New York, 2005.


    D. L. Donoho, An ideal spatial adaptation by wavelet shrinkage, Biometrika, 81 (1994), 425-455.doi: 10.1093/biomet/81.3.425.


    D. L. Donoho, De-noising by soft thresholding, IEEE. Trans. Inf. Theory, 41 (1995), 613-627.doi: 10.1109/18.382009.


    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), 209-226.doi: 10.3934/mbe.2005.2.209.


    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), 447-458.


    K. B. How, A. S. Kok Bin, N. T. Siong and K. K. Soo, Red blood cell segmentation utilizing various image segmentation techniques, in "Proceedings of International Conference on Man-Machine Systems," Malaysia, (2006).


    K. Jiang, Qing-Min and S. Y. Dai, A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering, in "Proceedings of The Second International Conference on Machine Learning and Cybernetics," Xian, (2003).


    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).


    J. Liang, S. Elangovan and J. Devotta, Application of wavelet transform in travelling wave protection, Int. Elect. Pow. Energy, 22 (2000), 537-542.


    D. Liu and T. Chen, DISCOV: A framework for discovering objects in video, Int. Trans. Multimedia, 10 (2008), 200-208.


    S. Mallat, Zero-crossings of a wavelet transform, Int. Trans. Inf. Theory, 37 (1991), 1019-1033.doi: 10.1109/18.86995.


    S. Mallat and W. L. Hwang, Singularity detection and processing with wavelets, Int. Trans. Inf. Theory, 38 (1992), 617-643.doi: 10.1109/18.119727.


    S. Mallat and S. Zhong, Charaterization of signals from multiscale edges, Int. Trans. Patt. Anal. Mac. Int., 14 (1992), 710-732.


    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), 2-7.doi: 10.1016/j.cam.2005.07.013.


    P.Soille, "Morphological Image Analysis: Principles and Applications," $2^{nd}$ edition, Springer-Verlag, New York, 1999.


    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 "Proceedings of Biomedical Imaging: From Nano to Macro 2007, ISBI 2007 4th IEEE International Symposium," (2007).


    M. A. Wani, D. Zhang and H. Arabnia, Parallel edge-region-based segmentation algorithm targeted at reconfigurable multiRing network, Journal of Supercomputing, 25 (2003), 43-62.


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


    Y. Zhai, D. Zhang, J. Sun and B. Wu, A novel variational model for image segmentation, Journal of Computational and Applied Mathematics, 235 (2011), 2234-2241.doi: 10.1016/j.cam.2010.10.020.


    J. Y. Zhou, X. Fang and K. Ghosh, Multiresolution filtering with application to image segmentation, Math. Comp. Model., 24 (1996), 177-195.doi: 10.1016/0895-7177(96)00121-5.


    J. Y. Zhou, E. P. Ong and C. C. Ko, Video object segmentation and tracking for content-based video coding, in "Proceedings of IEEE International Conference on Multimedia and Expo, ICME 2000," USA (2000).

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