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Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Afzan Adam1,*, Abdul Hadi Abd Rahman1, Nor Samsiah Sani1, Zaid Abdi Alkareem Alyessari1, Nur Jumaadzan Zaleha Mamat2, Basela Hasan3

1 Faculty of Information Science and Technology, Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43300, Malaysia
2 Faculty of Science and Technology, School of Mathematical Sciences, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43300, Malaysia
3 Department of Information Technology and Computing, Faculty of Computer Studies, Arab Open University, Amman, Jordan

* Corresponding Author: Afzan Adam. Email:

(This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)

Computers, Materials & Continua 2021, 67(1), 761-777.


Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only. Then, the textural and spatial features along the estimated layer are used for dysplastic tissue detection. The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue. The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification. The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue. Thus, quantifying and classifying the morphological patterns for dysplasia can be automated. The textural and spatial features on the detected epithelial layer can capture the changes in tissue.


Cite This Article

A. Adam, A. Hadi Abd Rahman, N. Samsiah Sani, Z. Abdi Alkareem Alyessari, N. Jumaadzan Zaleha Mamat et al., "Epithelial layer estimation using curvatures and textural features for dysplastic tissue detection," Computers, Materials & Continua, vol. 67, no.1, pp. 761–777, 2021.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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