Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection

Abstract

Aims: The proposed method falls into the category of medical image processing. Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis. Objective: This paper aims to present a leukocyte segmentation method, which improves segmentation accuracy under rapid and standard staining conditions. Methods: The proposed method first localizes leukocytes by color component combination and Adaptive Histogram Thresholding (AHT), and crops sub-image corresponding to each leukocyte. Then, the proposed method employs AHT to extract the nucleus of leukocyte and utilizes image color features to remove image backgrounds such as red blood cells and dyeing impurities. Finally, Canny edge detection is performed to extract the entire leukocyte. Accordingly, cytoplasm is obtained by subtracting nucleus with leukocyte. Results: Experimental results on two datasets containing 160 leukocyte images show that the proposed method obtains more accurate segmentation results than their counterparts. Conclusion: The proposed method obtains more accurate segmentation results than their counterparts under rapid and standard staining conditions.