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J Med Imaging (Bellingham). 2017 Apr;4(2):027502. doi: 10.1117/1.JMI.4.2.027502. Epub 2017 Jun 21.

Automatic extraction of cell nuclei from H&E-stained histopathological images.

Yi F1, Huang J2, Yang L1,3, Xie Y1,4,5, Xiao G1,4,5.

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University of Texas Southwestern Medical Center, Quantitative Biomedical Research Center, Department of Clinical Science, Dallas, Texas, United States.
University of Texas at Arlington, Department of Computer Science and Engineering, Arlington, Texas, United States.
Chinese Academy of Medical Science and Peking Union Medical College, National Cancer Center/Cancer Hospital, Department of Pathology, Chaoyang District, Beijing, China.
University of Texas Southwestern Medical Center, Department of Bioinformatics, Dallas, Texas, United States.
University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Dallas, Texas, United States.


Extraction of cell nuclei from hematoxylin and eosin (H&E)-stained histopathological images is an essential preprocessing step in computerized image analysis for disease detection, diagnosis, and prognosis. We present an automated cell nuclei segmentation approach that works with H&E-stained images. A color deconvolution algorithm was first applied to the image to get the hematoxylin channel. Using a morphological operation and thresholding technique on the hematoxylin channel image, candidate target nuclei and background regions were detected, which were then used as markers for a marker-controlled watershed transform segmentation algorithm. Moreover, postprocessing was conducted to split the touching nuclei. For each segmented region from the previous steps, the regional maximum value positions were identified as potential nuclei centers. These maximum values were further grouped into [Formula: see text]-clusters, and the locations within each cluster were connected with the minimum spanning tree technique. Then, these connected positions were utilized as new markers for a watershed segmentation approach. The final number of nuclei at each region was determined by minimizing an objective function that iterated all of the possible [Formula: see text]-values. The proposed method was applied to the pathological images of the tumor tissues from The Cancer Genome Atlas study. Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei.


cell nuclei extraction; color deconvolution; digital pathology; hematoxylin and eosin-stained image; image analysis; watershed transform

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