Abstract
Automatic screening systems play an increasingly important role in the diagnosis of pathologists. Image measurement and classification are the key techniques of automatic screening systems, which directly determine the performance. The distortion in grey and texture after overlapping nuclei segmentation seriously degrades the DNA content measurement and nuclei classification. In order to solve this problem, this paper presents a new method to reconstruct the pixels in overlapping regions based on the GMM-UBM (Gaussian mixture model–universal background model). In this method, a large amount of data are first used to train a GMM (named UBM). Then, the GMM of each nucleus is derived by maximizing a posteriori adaptation with the UBM and the normal grey value of this nucleus. The grey values are randomly generated by the GMM and filled to the overlapping region, with the offset to fine-tuning the Gaussian components. Finally, the image inpainting algorithm is used to repair the connected region. Experimental results show that this method can effectively recover the nucleus features, such as texture, grey and optical density, and improve the accuracy of nucleus measurement and classification.
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Acknowledgements
This research is partly supported by The National Natural Science Foundation of China (61673142), the Foundation of Education Department of Heilongjiang Province (12511096), Natural Science Foundation of HeiLongjiang Province of China (F2017013), Natural Science Foundation of HeiLongjiang Province of China (JJ2019JQ0013), University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(UNPYSCT-2016034), Outstanding Youth Talent Foundation of Harbin of China (2017RAYXJ013), and the Research Fund for the Doctoral Program of Higher Education of China (20132303120003) and the Science Funds for the Young Innovative Talents of HUST (20152).
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Zhao, J., Xie, Y., Tang, L. et al. Overlapping region reconstruction in nuclei image segmentation. Vis Comput 37, 1623–1635 (2021). https://doi.org/10.1007/s00371-020-01926-1
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DOI: https://doi.org/10.1007/s00371-020-01926-1