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Segmentation of hepatocellular carcinoma and dysplastic liver tumors in histopathology images using area based adaptive expectation maximization

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Abstract

The differentiation of a cluster of nuclei and multi-nucleation is a critical issue in automated diagnosis systems. Due to the similarities between said clusters and malignant nuclei, misclassification of these regions can affect the automated systems’ final decision. In this paper, a method for differentiating clusters from multi nucleated cells in histopathological images is proposed. Hepatocellular Carcinoma(HCC) and Dysplasia are characterized by cellular and nuclear enlargement, nuclear pleomorphism and multinucleation, which possess prominent threat Data was obtained from Global Hospital and Research Center from patients diagnosed with Hepatocellular Carcinoma and Dysplasia. This paper introduces a hybrid diagnosis method that uses texture, layout and context features of nuclei and cytoplastic cells in order to enhance the poor diagnosis of liver tumors in Infra Red (IR) images. We propose a Area based Adaptive Expectation Maximization(EM) that grows the clusters, which avoids the need for initial cluster selection in order to obtain texton maps of nuclei and cytoplasm. A linear regression model of nuclei and cytoplastic changes were built by incorporating the aforementioned features efficiently. The proposed method provides better classification and segmentation accuracy of nuclei and extra nuclear content in HCC and dysplasia, compared to the state-of-the-art methods like convolutional networks and classical methods like Adaptive K means and EM method in constant time. In conclusion, this system detects the malignant cells and the highly eligible precancerous cells which is cost effective and reproducible.

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Acknowledgements

We would like to thank and extend a deep sense of gratitude to Dr. Mohamed Rela, MS FRCS, Consultant HPB Surgeon and Dr. Balajee, MD, HOD of Laboratory Medicine & Senior Consultant of Global Hospital, Chennai for providing the medical image data and interpretation for the analysis. They have helped us a lot in getting a better insight and assessing the number of liver metastases in histopathology images to a greater extent.

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Authors and Affiliations

  1. SSN College of Engineering, Anna University, Chennai, India

    Lekshmi Kalinathan

  2. P.S.R. Engineering College, Anna University, Sivakasi, India

    Ruba Soundar Kathavarayan

  3. Gastroenterologist, Melmaruvathur Adhiparasakthi Institute of Medical Sciences and Research, Melmaruvathur, India

    Dinakaran Nagendram

  4. Global Hospital and Research Centre, Chennai, India

    Mukul Vij

  5. King’s College Hospital, London, UK

    Mohamed Rela

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  1. Lekshmi Kalinathan
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  2. Ruba Soundar Kathavarayan
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  3. Dinakaran Nagendram
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  4. Mukul Vij
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  5. Mohamed Rela
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Correspondence to Lekshmi Kalinathan.

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Funding

No. The study was approved by Institutional Ethics Committee-Global Hospital and Health City (IEC-GHHC).

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Ethics Committee-Global Hospital and Health City(IEC-GHHC).

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Kalinathan, L., Kathavarayan, R., Nagendram, D. et al. Segmentation of hepatocellular carcinoma and dysplastic liver tumors in histopathology images using area based adaptive expectation maximization. Multimed Tools Appl 77, 1761–1782 (2018). https://doi.org/10.1007/s11042-016-4260-y

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  • DOI: https://doi.org/10.1007/s11042-016-4260-y

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