Abstract
The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.
Highlights
Human blood contains different type of cells; red blood cells (RBC) or erythrocyte are the most abundant cell type
108 RBCs are labeled as biconcaves, 106 RBCs labeled as stomatocytes, 38 RBCs are labeled as Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics on 05 Oct 2020 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use
We have presented and assessed the use of pattern recognition neural network (PRNN) applied to the 2-D and 3-D features of RBCs obtained through digital holographic microscopy (DHM) in order to categorize and count biconcave, stomatocyte, flat-disc, and echinostomatocyte RBCs in an RBC sample with multiple types
Summary
Human blood contains different type of cells; red blood cells (RBC) or erythrocyte are the most abundant cell type. In conventional RBC classification problems, the experts deal with two-dimensional (2-D) erythrocyte images obtained by conventional microscopes and cameras.[13,16,17,18,19] These methods generally have good performance but most of them have a significant number of features since they need to discriminate groups by utilizing 2-D features. We have extracted 108 biconcave RBCs from a healthy sample stored for 1 day in the blood bank, 106 samples of stomatocyte shape from a sample with predominantly of stomato cells, 38 samples of flat-disc shape, and 71 samples of echinospherocyte shape for training and testing PRNN. Our experimental results demonstrate that the PRNN trained by 3-D features gives a good performance in classifying and counting RBCs in multiple human RBCs in an automated manner in comparisons with the 2-D features.
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