Abstract

White blood cell image segmentation plays a vital role in the accurate analysis and diagnosis of blood-related diseases, facilitating the identification and quantification of white blood cells in microscopic images. This process is essential for early disease detection, treatment monitoring, and immune response studies, ultimately supporting clinical decision-making. In this paper, we propose an enhanced approach based on the Segment Anything Model. First, Contrast Limited Adaptive Histogram Equalization is applied for pre-processing to enhance the features of white blood cells. Then, Segment Anything Model is utilized for segmentation. Experimental results demonstrate that our method achieves state-of-the-art performance on cross-domain datasets, providing accurate and reliable segmentation of white blood cells.