  The detection of red blood cells based on morphology and colorimetric
appearance is very important in improving hematology diagnostics. There are
automatons capable of detecting certain forms, but these have limitations with
regard to the formal identification of red blood cells because they consider
certain cells to be red blood cells when they are not and vice versa. Other
automata have limitations in their operation because they do not cover a
sufficient area of the blood smear. In spite of their performance, biologists
have very often resorted to the manual analysis of blood smears under an
optical microscope for a morphological and colorimetric study. In this paper,
we present a new strategy for semi-automatic identification of red blood cells
based on their isolation, their automatic color segmentation using Otsu's
algorithm and their morphology. The algorithms of our method have been
implemented in the programming environment of the scientific software MATLAB
resulting in an artificial intelligence application. The application, once
launched, allows the biologist to select a region of interest containing the
erythrocyte to be characterized, then a set of attributes are computed
extracted from this target red blood cell. These attributes include
compactness, perimeter, area, morphology, white and red proportions of the
erythrocyte, etc. The types of anemia treated in this work concern the
iron-deficiency, sickle-cell or falciform, thalassemia, hemolytic, etc. forms.
The results obtained are excellent because they highlight different forms of
anemia contracted in a patient.

Signal Processing