Article: An optimised pixel-based classification approach for automatic white blood cells segmentation Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2020 Vol.32 No.2 pp.144 - 160 Abstract: The pixel-based classification is an automatic approach for classifying all pixels in the image but does not take into account the spatial information for the region of interest. On the other hand, region-growing methods take into account the neighbourhood pixels information. However, in region-growing methods, a pixel-group called 'points of interest' are needed to initialise the growing process. In this paper, we proposed an optimised pixel-based classification by the cooperation of region growing strategy. This original segmentation scheme is performed in two phases for the automatic recognition of white blood cells (WBC): the first is a learning step with colour characteristics of each pixel in the image. The second is a region growing application by classifying neighbouring pixels from pixels of interest extracted by the ultimate erosion technique. This process has proved that the cooperation allows obtaining a nucleus and cytoplasm segmentation as closer to what as expected in the reference images. Inderscience Publishers - linking academia, business and industry through research

Title: An optimised pixel-based classification approach for automatic white blood cells segmentation

Authors: Nesma Settouti; Mohammed El Amine Bechar; Mostafa El Habib Daho; Mohammed Amine Chikh

Addresses: Biomedical Engineering Laboratory GBM, Tlemcen University, Algeria ' CNRS, Centrale Marseille, Institut Fresnel UMR 7249, Aix Marseille University, 13013, Marseille, France; Biomedical Engineering Laboratory GBM, Tlemcen University, Algeria ' Biomedical Engineering Laboratory GBM, Tlemcen University, Algeria ' Biomedical Engineering Laboratory GBM, Tlemcen University, Algeria

Abstract: The pixel-based classification is an automatic approach for classifying all pixels in the image but does not take into account the spatial information for the region of interest. On the other hand, region-growing methods take into account the neighbourhood pixels information. However, in region-growing methods, a pixel-group called 'points of interest' are needed to initialise the growing process. In this paper, we proposed an optimised pixel-based classification by the cooperation of region growing strategy. This original segmentation scheme is performed in two phases for the automatic recognition of white blood cells (WBC): the first is a learning step with colour characteristics of each pixel in the image. The second is a region growing application by classifying neighbouring pixels from pixels of interest extracted by the ultimate erosion technique. This process has proved that the cooperation allows obtaining a nucleus and cytoplasm segmentation as closer to what as expected in the reference images.

Keywords: automatic segmentation; region growing approach; pixel-based classification; mathematical morphology; random forest; white blood cells.

DOI: 10.1504/IJBET.2020.105651

International Journal of Biomedical Engineering and Technology, 2020 Vol.32 No.2, pp.144 - 160

Received: 14 Dec 2016
Accepted: 17 May 2017
Published online: 09 Mar 2020 *

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Title: An optimised pixel-based classification approach for automatic white blood cells segmentation

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