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The detection of red blood cells based on morphology and colorimetricappearance is very important in improving hematology diagnostics. There areautomatons capable of detecting certain forms, but these have limitations withregard to the formal identification of red blood cells because they considercertain cells to be red blood cells when they are not and vice versa. Otherautomata have limitations in their operation because they do not cover asufficient area of the blood smear. In spite of their performance, biologistshave very often resorted to the manual analysis of blood smears under anoptical microscope for a morphological and colorimetric study. In this paper,we present a new strategy for semi-automatic identification of red blood cellsbased on their isolation, their automatic color segmentation using Otsu'salgorithm and their morphology. The algorithms of our method have beenimplemented in the programming environment of the scientific software MATLABresulting in an artificial intelligence application. The application, oncelaunched, allows the biologist to select a region of interest containing theerythrocyte to be characterized, then a set of attributes are computedextracted from this target red blood cell. These attributes includecompactness, perimeter, area, morphology, white and red proportions of theerythrocyte, etc. The types of anemia treated in this work concern theiron-deficiency, sickle-cell or falciform, thalassemia, hemolytic, etc. forms.The results obtained are excellent because they highlight different forms ofanemia contracted in a patient.

A New Strategy for the Morphological and Colorimetric Recognition of  Erythrocytes for the Diagnosis of Forms of Anemia based on Microscopic Color  Images of Blood Smears