Multi Features Based Approach for White Blood Cells Segmentation and Classification in Peripheral Blood and Bone Marrow Images

Mohammed Lamine Benomar (1) , Amine M. Chikh (1) , Xavier Descombes (2) , Mourtada Benazzouz (1)

1 GBM - Laboratoire Génie BioMédical [Tlemcen]
2 MORPHEME - Morphologie et Images

In this paper, we propose a complete automated framework for white blood cells differential count in peripheral blood and bone marrow images, in order to reduce the analysis time and increase the accuracy of several blood disorders diagnosis. A new colour transformation is first proposed to highlight the white blood cells regions; then, a marker controlled watershed algorithm is used to segment the region of interest. The nucleus and cytoplasm are subsequently separated. In the identification step, a set of colour, texture and morphological features are extracted from both nucleus and cytoplasm regions. Next, the performances of a random forest classifier on a set of microscopic images are compared and evaluated. The obtained results reveal high recognition accuracies for both segmentation and classification stage.

Mots clés: white blood cells; bone marrow images; cells classification; texture features; morphological features; peripheral blood images; color transformation; cells segmentation

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]; Bio-informatique [q-bio.QM]; Imagerie médicale; Traitement du signal et de l'image [eess.SP]

Mohammed Lamine Benomar, Amine M. Chikh, Xavier Descombes, Mourtada Benazzouz. Multi Features Based Approach for White Blood Cells Segmentation and Classification in Peripheral Blood and Bone Marrow Images. International Journal of Biomedical Engineering and Technology (IJBET), 2021, ⟨10.1504/IJBET.2021.113729⟩. ⟨hal-02279352v2⟩