Automatic anemia identification through morphological image processing

Though blood cell manipulation has been an interesting research area for many years, most of the techniques presented in literature produce poor segmentation results for images with high overlapped blood cells. In this paper, we introduce a fully automatic low cost and accurate system to identify four common types of anemia and report on blood cell count. The results of our system indicate a good impact with the manually processed results of 99.678% accuracy of Red Blood Cell count. The diagnosis of Elliptocytes, Microcytes, Macrocyte and Spherocytes anemia result in the range of 91%-97% accuracy.