Evidential Segmentation Scheme of Bone Marrow Images - Benazzouz, Mourtada; Baghli, Ismahan; Benomar, Amine; Ammar, Med; Benmouna, Youcef; Chikh, Med Amine (European Journal of Applied Sciences, 2016)
The analysis of microscope images can provide useful information concerning health of patients. In this paper a new and ecient supervised method for color image segmentation is presented. The segmentation here is to extract leukocytes (white blood cells) and separate its constituents, nucleus and cytoplasm. Since red cells, leukocytes and background had dierent color in image of bone marrow smear, they were extracted according to their own colors. First, we train an SVM in dierent color spaces by a learning set. SVM with xed parameters is used here to yield several classications, and the basic technique consists on information fusion from dierent sources via evidence theory. This combination is performed by integrating uncertainties and redundancies for each one of the color spaces. From the experiments, we achieve good segmentation performances in the entire nucleus and cytoplasm segmentation. We evaluate the segmentation performance of the proposed technique by comparing its results with the cell images manually segmented by an expert.