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

Macarena Boix; Begoña Cantó

doi:10.3934/mbe.2013.10.279

Article Contents

2013, Volume 10, Issue 2: 279-294. Doi: 10.3934/mbe.2013.10.279

This issue Previous Article Next Article Longitudinal displacement in viscoelastic arteries: A novel fluid-structure interaction computational model, and experimental validation

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

Macarena Boix^1, and
Begoña Cantó^1,

1.
Universitat Politècnica de València, Plaza Ferrándiz y Carbonell, n.2, Alcoy (Alicante), 03801

Received: March 31, 2012

Revised: September 30, 2012

Published: December 2012

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Abstract

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.

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

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