Fuzzy mathematical morphology for biological image segmentation

Due to the imaging devices, real-world images such as biological images may have poor contrast and be corrupted by noise, so that regions in the images present soft edges and their segmentation turns out to be quite difficult. Fuzzy mathematical morphology can be successfully applied to segment biological images having such characteristics of vagueness and imprecision. In this work we introduce an approach based on fuzzy mathematical morphology to segment images of human oocytes in order to extract the oocyte region from the entire image. The approach applies fuzzy morphological operators to detect soft edges in the oocyte images, followed by morphological reconstruction operators to isolate the oocyte region. The main concepts from fuzzy mathematical morphology are briefly introduced and the results of applying fuzzy morphological operators are reported in low-contrast images of human oocytes.