Abstract
Manual microscopic analysis is the gold standard for analyzing blood smear. Microscopic analysis of blood smear by a hematologist is subjected to many challenges such as inter-observer variations, operator experience, and conditions of observation. This study aims to examine several parameters extracting from the features of blood smear images. These parameters were used to develop a predictive function, which can be used to automate the microscopic analysis of blood cells instead of manual observation. Fractal dimension, roundness, and aspect ratio were estimated for two types of abnormal erythrocytes: echinocyte and sickle cell. Standard conditions and the choosing of the optimum parameters through the imaging preprocessing were done in order to ensure that the chosen parameters reflect the morphological characteristics of examined erythrocytes. Statistical discriminant analysis was used to build the predictive function for erythrocytes morphological change by a linear combination of the measured parameters. The measured fractal dimensions were 1.825 +/- 0.008, 1.502 +/- 0.019 and 1.620 +/- 0.018 for control, echinocyte, and sickle cell, respectively. The roundness values were 0.94 +/- 0.05, 0.83 +/- 0.04 and 0.56 +/- 0.02 for control, echinocyte, and sickle cell, respectively. The aspect ratio values were 1.005 +/- 0.151, 1.046 +/- 0.089 and 1.742 +/- 0.162 for control, echinocyte, and sickle cell, respectively. The differences between the image analysis parameters for echinocyte and sickle, when compared to control, were statistically significant. The constructed discriminant function using measured parameters was effectively differentiating between examined erythrocytes. The results demonstrated that the selected image analysis parameters extracted from microscopic images with conjunction with statistical discriminant analysis could be used as powerful tools in the classification of erythrocytes according to their morphological characteristics. The findings of this study, in addition to the previous attempts in this filed, could help in the enhancement of a fully automated microscopic system for blood smear analysis.