Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentation algorithm based on both sparsity and geometry constraints. Specifically, we first construct a sparse image representation via combining the HSL color space and the RGB color channels, followed by the use of a sparsity constraint to only preserve useful information from the nuclei features. In addition, we introduce a robust model fitting strategy (i.e., the geometry constraint) to detect cells. Our model fitting strategy is able to significantly improve the robustness of the proposed segmentation algorithm against outliers that could seriously contaminate WBCs. The experimental results show that the proposed algorithm presents clear advantages over the state-of-the-art WBC segmentation algorithms in terms of accuracy. INDEX TERMS Geometry constraint, sparsity constraint, white blood cell segmentation.
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.
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