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

作　　者：Afzan Adam Abdul Hadi Abd Rahman Nor Samsiah Sani Zaid Abdi Alkareem Alyessari Nur Jumaadzan Zaleha Mamat Basela Hasan 

机构地区：[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

出　　处：《Computers, Materials & Continua》2021年第4期761-777,共17页计算机、材料和连续体（英文）

基　　金：supported by the Center for Research and Innovation Management[CRIM]Universiti Kebangsaan Malaysia[Grant No.FRGS-1-2019-ICT02-UKM02-6]and Ministry of Higher Education Malaysia.

摘　　要：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.

关 键 词：Digital pathology grading dysplasia tissue boundary effect 

分 类 号：R73[医药卫生—肿瘤]