Detection of hypochromic anemia in blood sample microscopic images using adaptive network-based fuzzy inference system

Volume of red blood cell is an important factor in distinguishing its abnormalities. Mean corpuscular volume MCV of red blood cells contributes much to differentiation of several blood diseases like iron deficiency and other types of anemia. This paper proposes an automated system to classify blood samples using cell microscopic images instead of pathology test results. Adaptive local thresholding is first used to segment cell images. The volumes of red cells are then estimated by assuming torus geometry for cells. Finally, an adaptive network-based fuzzy inference system ANFIS is used to classify blood samples to normal and abnormal. Accuracy of the proposed system and area under Receiver Operating Characteristics ROC curve are 100% and 1 respectively.