Nucleus segmentation in histology images with hierarchical multilevel thresholding

Hady Ahmady Phoulady; Dmitry B. Goldgof; Lawrence O. Hall; Peter R. Mouton

doi:10.1117/12.2216632

23 March 2016 Nucleus segmentation in histology images with hierarchical multilevel thresholding

Hady Ahmady Phoulady, Dmitry B. Goldgof, Lawrence O. Hall, Peter R. Mouton

Author Affiliations +

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 979111 (2016) https://doi.org/10.1117/12.2216632
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Automatic segmentation of histological images is an important step for increasing throughput while maintaining high accuracy, avoiding variation from subjective bias, and reducing the costs for diagnosing human illnesses such as cancer and Alzheimer's disease. In this paper, we present a novel method for unsupervised segmentation of cell nuclei in stained histology tissue. Following an initial preprocessing step involving color deconvolution and image reconstruction, the segmentation step consists of multilevel thresholding and a series of morphological operations. The only parameter required for the method is the minimum region size, which is set according to the resolution of the image. Hence, the proposed method requires no training sets or parameter learning. Because the algorithm requires no assumptions or a priori information with regard to cell morphology, the automatic approach is generalizable across a wide range of tissues. Evaluation across a dataset consisting of diverse tissues, including breast, liver, gastric mucosa and bone marrow, shows superior performance over four other recent methods on the same dataset in terms of F-measure with precision and recall of 0.929 and 0.886, respectively.

Citation Download Citation

Hady Ahmady Phoulady, Dmitry B. Goldgof, Lawrence O. Hall, and Peter R. Mouton "Nucleus segmentation in histology images with hierarchical multilevel thresholding", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979111 (23 March 2016); https://doi.org/10.1117/12.2216632

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