Segmentation and Counting of White Blood Cells Using Image Processing Techniques

— The counting of white blood cells (WBCs) is an extremely essential measurement parameter to diagnose some particular diseases and identify various infections that are concealed within the human body. Within the hospital, manual counting of WBCs is time-consuming, laborious, and needs experienced experts for accurate results. Thus, computer-aided diagnosis methods can help pathologists to perform accurate counting with less effort. To achieve this, a new method for WBCs counting based on incorporating the marker-controlled watershed algorithm with morphological filters for a microscopic blood sample image is proposed in this paper. To begin with, color correction is applied to standardize the amount of color intensity in the original blood-smeared image. Segmentation of white blood cells is then carried out using hue-saturation-value (HSV) model color analysis with the Otsu threshold. Noise and undesirable regions that emerge during the segmentation process are removed using morphological filters. For overlapping WBCs, an effective segmentation method based on a watershed algorithm is introduced to overcome the limitations in the existing WBCs counting methods. Images from the ALL_IDB1 dataset are utilized to apply and evaluate the proposed approach. An accuracy of 95% is achieved in the counting of WBCs. The evaluation results reveal that the proposed method outperforms the accuracy of the traditional methods and overcomes their shortages.