Abstract
To improve the accuracy of leukocyte segmentation, this paper presents a novel method based on adaptive histogram thresholding (AHT). The proposed method first employs color component combination and AHT to extract the nucleus of leukocyte and utilizes image color features to remove the complex backgrounds such as red blood cells (RBCs) and substantial dyeing impurities. Then, Canny edge detection is performed to extract the entire leukocyte. Finally, the cytoplasm of the leukocyte is obtained by subtracting the nucleus with the entire leukocyte. Experimental results on an image dataset containing 60 leukocyte images show that the proposed method generates more accurate segmentation results than the counterparts.
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Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (61772254 and 61202318), Fuzhou Science and Technology Project (2016-S-116), Program for New Century Excellent Talents in Fujian Province University (NCETFJ), Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), Young Scholars in Minjiang University (Mjqn201601), and Fujian Provincial Leading Project (2017H0030).
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Zhou, X., Wang, C., Li, Z., Zhang, F. (2020). Adaptive Histogram Thresholding-Based Leukocyte Image Segmentation. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_47
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DOI: https://doi.org/10.1007/978-981-13-9710-3_47
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