Enhanced White Blood Cell and Platelet Segmentation: A Particle Swarm Optimization-based Chromaticity approach

Microscopic image examination is essential for medical diagnostics to identify anomalies using cell counts based on morphology. Sickle Cell Disease (SCD) is an inherited blood condition characterized by defective hemoglobin, leading to severe anemia and complications. Detecting sickle cells in blood smears is essential, but the presence of White blood cells (WBCs) and platelets often leads to miscounting as they are classified incorrectly as red blood cells (RBCs). This study proposed an approach for segmenting WBCs and platelets by resembling the human color recognition process to differentiate the regions for accurate identification. First, the RGB color space is converted to RG chromaticity to locate WBCs and platelets with high pixel chromatic variance. Parametric segmentation is applied to the RG chromaticity images to identify the appropriate chromaticity channel for segmentation based on probability distribution values. The optimal threshold values have been determined using Particle Swarm Optimization (PSO) by dynamically narrowing the search space using values obtained through manual experimentation ranging from 0.001 to 1. This systematic process effectively identifies and segments platelets and WBCs, ensuring that overlapping platelets and WBCs are accurately segmented. Compared to state-of-the-art techniques, the proposed approach achieved an accuracy of 96.32 %, 96.97% for sensitivity, 96.96 % for precision and 97.46% for F- score in the pixel-wise segmentation of WBCs and platelets.