Automatic Segmentation on Cell Image Fusing Gray and Gradient Information

To develop an automatic classifying and diagnostic system for the hematopoietic cells from the blood and bone marrow smears stained with Wright-Giemsa, an automated segmentation algorithm fusing gray level, colorful information and mathematical morphological gradient is proposed for segmentation of the nucleated hematopoietic cells (including nucleus and cytoplasm). For the accurate segmentation of the nucleus, the conventional iterative threshold segmentation has been improved. Color information and prior knowledge are fully used by transaction of color spaces for the purpose of cytoplasm segmentation. In order to prevent over-segmentation, the morphological gradient information is used to mark the background, nucleus and cytoplasm. The edge detection is implemented in gray gradient image since the morphological gradient can detect the contour better than other conventional edge detection operators. The success rate is 95.5 % for nucleus and 92.6 % for cytoplasm. The results show that the method is valid and efficient to segment color images from blood and bone marrow smears.