The blood specimens were obtained from (20) different patients referred to Emam Reza hospital of Tabriz, Iran, containing one or more types of abnormal red cells. For preparation of stained blood smear, a drop of blood was put on the slide,” 1” inch from one end and spreaded with another slide. To get a smear of proper thickness, the spreading slide was held of an angle around 25. The blood smear was air dried, fixed and stained with Giemsa.
The digital images of stained blood smear were captured by Nikon, DS-Fi1 camera which was attached to a microscope with × 1000 magnification. All the images were interpreted after collection and all the cells were classified by an expert.
At the first step, preprocessing methods were used for image enhancement and improvement of the results. Preprocessing, includes converting RGB image to gray scale and then to binary image, eliminating small objects that are so smaller than the smallest red blood cell, complementing image and clearing border cells.
In the method we introduced in this article, we extracted some features from red blood cells and used them for classification process. After labeling, area and perimeter were calculated for each red blood cell. Area is the sum of the pixels that are enveloped inside the cell's perimeter pixels. Perimeter is the sum of boundary pixels for each cell. Respect to the following equation, diameter is calculated in this way:
Diameter = 4*area / perimeter
In the next steps we described a new factor for surveying some geometric properties of cells such as elongation and circularity. This factor was named “Shape Geometric Factor". Generally red blood cell shapes are similar to the oval. Normal, hypochrome, macrocyte, microcyte, target cell, spherocyte and stomato red blood cells have a circle shape that is special form of oval, an oval with equal diagonal. Elongated cells are oblong ovals, with one big and one small diagonal. Helmet cells are semicircles, for other cells we can describe such similarities.
Regarding these similarities between oval and red blood cells, we traced peripheral oval for each red blood cell. This oval encompasses whole cell and cell embeds completely in this oval. As this similarity between cell's shape and peripheral oval is near circle, the diameters of peripheral oval are near each other in size, and as this similarity tended to elongation diameters have a big difference in size. Shape geometric factor is proportion of big diameter of peripheral oval to small diameter of this oval, equation 2:
SGF = larger diameter / smaller diameter
Figure 1.
Processing Steps
Figure 2.
Standard oval, d1 and d2 are diameters. SGF is proportion of diameters
Table 1.
Similarity between Oval and Different Kind of Red Blood Cell
With this factor we calculated circularity and elongation for each cell. If SGF is larger than 1.2, we know that cell has elongation and if SGF is less than 1.2 we know that cell has circle shape. After SGF we defined other factor for each cell, "deviation value". This factor is described for measuring deviation of cell area from peripheral oval area. Deviation value is calculated with division of shape geometric factor to cell area, equation 3:
DV = SGF/area
If cell shape has no disorders such as spicules, cell area and the peripheral oval area are near to each other, and DV factor is less than 0.20, otherwise this factor is bigger than 0.20. Application of DV is in separation of sickle cells from other cells. Sickle cell has elongated shape, so SGF for sickle cell is bigger than 1.2, but peripheral oval's area and sickle cell's area have big difference.For this reason the DV factor in these cells is bigger than 0.20.
Deal of hue in center of cell has direct relationship with hemoglobin content of cell.
The more hemoglobin content, the more the redness. This region's name in blood smear images is central pallor. In gray scale images this region is pallid. Next factor that we calculated for each cell is "central pallor". This factor is described for detecting central pallor in cell. For each cell that has a central pallor this factor is 1, otherwise this factor is 0. Regarding to importance of central pallor in classification of red blood cells, in the next step, area, perimeter and diameter were calculated for each of them. "Area proportion" is the next factor that was calculated for each cell. This factor described for separation of hypochromic cells to other cells. Hypochromic cells have big central pallor, because of this AP factor in this cells are big. Threshold for this factor is 1.5. This factor is calculated with division of whole cell area to central pallor area of the same cell, equation 4:
AP = central pallor area / cell area
Figure 3.
Central Pallor and the Whole Red Blood Cell. A1 is central pallor area, a2 is total area of the cell and Ap is proportion of these
Figure 4.
CP flag rise if one object will be found in cell, TF Flag is rise if two objects will be found.
Using factors that described in this article we classify red blood cells in to 12 classes.
Table 2.
Description of Factors for Separation of Classes and Abbreviation for Each of Them
Feature Description A Area Sum of pixels enclosed by cell boundary P Perimeter Sum of perimeter pixels D Diameter Area/ (4*perimeter) SGF Shape geometric factor Proportion of peripheral oval's diameters AP Area proportion Area of cell/area of central pallor DV Deviation value shape geometric factor/cell area TF Target flag Rise if central pallor has another object CP Central pallor It is 1 if central pallor p presents
3.1. Classification Method
In this article we used some factors for classification which were completely described above. Red blood cells were classified into 12 classes. In order to separate those classes, we used some threshold limitations. With comparing the factors with the defined thresholds, one branch of decision tree was traversed. These thresholds were obtained by studying atlas of hematology and consulting with several pathologists. For example at first SGF factor was surveyed, if this factor was less than 1.2, the cell would be classified as circle shape cells. In the next phase, for this cell which was classified as a circular type, diameter was surveyed. If diameter was bigger than 8.5 the cell would be classified as macrocyte cells. After that, central pallor was surveyed, if this factor was 1, we knew that the cell had central pallor and AP factor would be surveyed for continuance of decision tree, if AP was bigger than 1.2 the cell would be classified as hypochromic macrocytes, otherwise this cell was classified as normochromic macrocyte. If central pallor factor was 0 the cell would be classified as macrocytes without central pallor. For each cell, decision tree was traversed, we traced this tree in following three figures, Figures 5 , 6 and 7 .
Figure 5.
Procedure of Classification.This Algorithm Determines Classes 1,2,6,9
Figure 6.
Classification Procedure.The Above Algorithm Determines Cells Belonging to Class 12 and 5.
Figure 7.
Classification Procedure. The Algorithm Classify Cells into Classes 3, 4, 7, 8, 9 and 10
Table 3.
Classes or Red Blood Cells
Class Number Class Name 1 Normocyte 2 Normocyte without central pallor 3 Macrocyte without central pallor 4 Spherocyte 5 Normochromic macrocyte 6 Target cell 7 Hypochromic microcyt 8 Hypochromic macrocyte 9 Hypochromic normocyte 10 Normochromic microcyte 11 Elongated cell 12 Sickle cell
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