# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 2 Background  
Blood images obtained from healthy patients usually consist of three components, at least: the platelets (or thrombocytes), the Red Blood Cells (RBCs or erythrocytes), and the White Blood Cells (WBCs or leukocytes), as shown in Figure 1.  
The blood platelets are the smallest cells of the blood, averaging about 2 to 4 \(\upmu\)m in diameter. They are disc shaped and do not present a nucleus. The tiny granules of platelets contain substances necessary for the clot formation activity because their function is related to homeostasis and the prevention and control of bleeding. For this reason, a discrete number of platelets is necessary. As a consequence, a platelet count is usually evaluated by preparing a blood smear to visualize any anomalies in shape or size directly.  
RBCs are round and have the form of biconcave disks with a diameter of 7-8 \(\upmu\)m. Peripherally, the presence of hemoglobin gives an area with a central pallor equal to 1-3 \(\upmu\)m, approximately 30-45% of the diameter of the cells. RBCs exist in different types, and each one has a particular form. Abnormal variability in their size, unusual variation in shape, and significant differences among erythrocyte types may indicate the presence of a disease [1]. At the same time, their color is representative of the concentration of the hemoglobin in the cell. The cytoplasm of all healthy RBCs does not contain any granule or other structures, e.g., typical of WBCs, while the inclusions are the result of unique conditions, and their identification can be clinically helpful.  
WBCs are the most prominent cells in peripheral circulation and range in size from 10 to 20 \(\upmu\)m. They are composed of a darker nucleus and lighter cytoplasm. Therefore, their identification is more intuitive than the other regions. However, WBCs have vast differences in their shapes, dimensions, and edges; consequently, their analysis results are challenging. Figure 2 shows that WBCs differentiate into two main groups: cells containing granules, called granulocyte or myelocytic, and cells without granules, called mononuclear or lymphoid. Thus, we can distinguish the cells according to their shape,  
Figure 1: Blood smear components. The red rectangle presents an example of an erythrocyte; the white box shows a leukocyte; while the violet one includes some platelets.  
size, the presence of granules in the cytoplasm, and the number of lobes in the nucleus. The lobes are the most significant part of the nucleus, and thin filaments connect them. WBCs mature into five distinct types that include neutrophils, basophils, and eosinophils for the granulocyte cell type and lymphocytes and monocytes for the non-granulocytic one. Neutrophils constitute the majority of WBCs in a healthy adult.  
WBCs' life in peripheral circulation is short; therefore, any alteration either in quantity or quality of a particular type can be dramatic for the patient. Several diseases affect the absolute or relative count of WBCs and their appearance on a blood smear. This is the case of parasitic infections that lead to a rise in the number of WBCs, while the most severe cases are certainly leukemias. When this happens, the peripheral smear usually shows a vast number of immature or abnormal cells. In this context, the automated cell counters are not able to distinguish normal from abnormal cells, and even worse, they could fail due to the presence of abnormal cells. Many computer-aided systems for digitized images have been proposed in the last few years to overcome these issues.