Blood cell segmentation from microscopic blood images

Blood cell segmentation is a critical innovation for automatic differential blood counting, classification and analysis in clinical examination. In color blood cell images segmentation and recognition are two essential issues in the field of biomedical cell morphology. This paper approaches methods to segment the blood cells from microscopic thin blood images. This data is the premise to perform higher level tasks for example, automatic differential blood counting, detection of different diseases such as Malaria, Babesia, Chagas disease, Anemia, Leukemia etc. A noteworthy necessity of an automated, real-time, computer vision-based cell segmentation system is an efficient method for segmenting different blood component such as Red Blood Cells (RBCs), White Blood Cells (WBCs), Platelets from input images for blood count as well as to detect the parasites present in blood cells. Input images are captured by connecting digital camera to microscope. Captured images are enhanced and segmented using K-mean clustering as well as global threshold. Overlapping cells are separated using Sobel edge detector and Watershed transform.