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Mathematical Biosciences and Engineering

2013, Volume 10, Issue 2: 279-294. doi: 10.3934/mbe.2013.10.279
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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
  • Received: 01 April 2012 Accepted: 29 June 2018 Published: 01 January 2013

  • MSC : Primary: 68U10; Secondary: 65K05.

  • Accurate image segmentation is used in medical diagnosis sincethis technique is a noninvasive pre-processing step for biomedicaltreatment. In this work we present an efficient segmentationmethod for medical image analysis. In particular, with this methodblood cells can be segmented. For that, we combine the wavelettransform with morphological operations. Moreover, the waveletthresholding technique is used to eliminate the noise and preparethe image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows asegmentation 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 generatesgoods results when it is applied on several images. Finally, theproposed 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[J]. Mathematical Biosciences and Engineering, 2013, 10(2): 279-294. doi: 10.3934/mbe.2013.10.279

    Related Papers:

  • Accurate image segmentation is used in medical diagnosis sincethis technique is a noninvasive pre-processing step for biomedicaltreatment. In this work we present an efficient segmentationmethod for medical image analysis. In particular, with this methodblood cells can be segmented. For that, we combine the wavelettransform with morphological operations. Moreover, the waveletthresholding technique is used to eliminate the noise and preparethe image for suitable segmentation. In wavelet denoising we determine the best wavelet that shows asegmentation 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 generatesgoods results when it is applied on several images. Finally, theproposed algorithm made in MatLab environment is verified for a selected blood cells.


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  • © 2013 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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