BLOOD CELL ANALYSIS USING IMAGE SEGMENTATION AND COUNTING

Nowadays, the complete blood cell count (CBC’s) is calculated manually in pathology labs. This traditional way is time consuming, so it needs some help in terms of software for blood cell analysis. This paper explains how the analysis of blood cell will be done. The idea of this paper is to serve pathologists and medical technicians by using image processing techniques such as Pulse coupled Neural Network (PCNN) and segmentation to count the blood cells. PCNN is a model with multiple parameters, and finding the proper values of these parameters is a tedious task. So a simplified PCNN is put forward. The method can not only de-noise and segment blood cell image perfectly, but also can well eliminate disturbed objects. It is an accurate and cost-effective model to assist pathologists and medical technicians.