Detecting and Segmenting White Blood Cells in Microscopy Images of Thin Blood Smears

A malarial infection is diagnosed and monitored by screening microscope images of blood smears for parasite-infected red blood cells. Millions of blood slides are manually screened for parasites every year, which is a tedious and error-prone process, and which largely depends on the expertise of the microscopists. We have developed a software to perform this task on a smartphone, using machine learning and image analysis methods for counting infected red blood cells automatically. The method we implemented first needs to detect and segment red blood cells. However, the presence of white blood cells (WBCs) contaminates the red blood cell detection and segmentation process because WBCs can be miscounted as red blood cells by automatic cell detection methods. As a result, a preprocessing step for WBC elimination is essential. Our paper proposes a novel method for white blood cell segmentation in microscopic images of blood smears. First, a range filtering algorithm is used to specify the location of white blood cells in the image following a Chan-Vese level-set algorithm to estimate the boundaries of each white blood cell present in the image. The proposed segmentation algorithm is systematically tested on a database of more than 1300 thin blood smear images exhibiting approximately 1350 WBCs. We evaluate the performance of the proposed method for the two WBC detection and WBC segmentation steps by comparing the annotations provided by a human expert with the results produced by the proposed algorithm. Our detection technique achieves a 96.37% overall precision, 98.37% recall, and 9736% F1-score. The proposed segmentation method grants an overall 82.28% Jaccard Similarity Index. These results demonstrate that our approach allows us to filter out WBCs, which significantly improves the precision of the cell counts for malaria diagnosis.