Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer  

作　　者：Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang Marcin Grzegorzek Hongzan Sun 

机构地区：[1]Microscopic Image and Medical Image Analysis Group,College of Medicine and Biological Information Engineering,Northeastern University,Shenyang,Liaoning 110016,China [2]Key Laboratory of Intelligent Computing in Medical Image,Ministry of Education,Northeastern University,Shenyang,Liaoning 110016,China [3]School of Intelligent Medicine,Chengdu University of Traditional Chinese Medicine,Chengdu,Sichuan 610075,China [4]International Joint Institute of Robotics and Intelligent Systems,Chengdu University of Information Technology,Chengdu,Sichuan 610225,China [5]Institute for Medical Informatics,University of Luebeck,Luebeck,Germany [6]Department of Knowledge Engineering,University of Economics in Katowice,Katowice,Poland [7]Shengjing Hospital of China Medical University,Shenyang,Liaoning 110136,China

出　　处：《Intelligent Medicine》2024年第3期141-152,共12页智慧医学（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.82220108007).

摘　　要：Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some drawbacks.This study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological images.Methods The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for classification.First,the U-net was applied to segment the image.Next,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was drawn.The third step was to extract the graph-curvature features of the histopathological image according to the MST image.Finally,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be obtained.Results During the experiment,we used various methods for comparison.In the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were used.We found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological images.In the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern feature.In the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and inception-V3.Through comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关 键 词：Gastric cancer Graph-curvature feature Image identification 

分 类 号：R445[医药卫生—影像医学与核医学] R73[医药卫生—诊断学]