BLOOD CELL IMAGE SEGMENTATION AND COUNTING

In traditional terms, blood cell analysis i.e. complete blood cell count (CBC) is done as a “convention”. In which it measures the red blood cells, white blood cells usually assesses the size and shape of red blood cells as per old delayed procedures. Today in this busy hectic schedule; pathologists need some help in terms of software for blood cell analysis. Thus, the idea of our paper is to serve the pathologists, medical technicians for the same, by using Image Processing techniques as Pulse-Coupled Neural Network (PCNN) has been shown to be a very powerful image processing tool. Here we present a method for blood cell image segmentation and counting. The method can not only de-noise and segment blood cell image perfectly, but also can well eliminate disturbed objects which will serious impact the blood cell counting step, and is able to segment specific isolated cell from its background.. It is a novel use of Image Processing, as we take clean and properly stained blood cell sample image for our software, to assist the pathologists and medical technicians.