Aims: Size, shape, and volume of Red Blood Cells are important factors in diagnosing blood-associated disorders such as iron deficiency and anemia. Every day, thousands of blood samples are tested by microscopes and automated cell counter devices in pathology laboratories around the world, which may be expensive and time-consuming. The objective of this study was to measure mean corpuscular volume of abnormal red blood cells using the adaptive neuro-fuzzy system with image processing.
Materials & Methods: This study was conducted on 60 blood samples from the archive of pathology laboratory of Sarem hospital including 40 normal samples and 20 abnormal samples. Adaptive local thresholding and bounding box methods were used to extract the inner and outer diameters of red cells to calculate MCV. An adaptive-network-based fuzzy inference system was used to classify blood samples to normal and abnormal groups. In this method, normal and abnormal blood samples were examined using image processing techniques and MATLAB software.
Findings: The Accuracy of the proposed method and area under the curve were found as 96.6% and 0.995%, respectively.
Conclusion: The proposed method provides diagnostic capability using a drop of the blood sample and showed suitable performance on pathological images. The designed automatic system can be a convenient and cost effective alternative for common laboratory procedures. In addition, the method provides a basis for calculating other parameters of blood test or CBC such as mean cell hemoglobin, mean cell hemoglobin concentration, RDW, hematocrit, and red blood cell count.