Open AccessUsing wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images
Author(s) -
Macarena Boix,
Begoña Cantó
Publication year - 2013
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2013.10.279
Subject(s) - wavelet , artificial intelligence , noise reduction , segmentation , mathematical morphology , computer science , pattern recognition (psychology) , computer vision , image segmentation , noise (video) , matlab , wavelet transform , image processing , image (mathematics) , image denoising , non local means , operating system
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.