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 Ruilin He;Chen Li;Xinyi Yang;Jinzhu Yang;Tao Jiang;Marcin Grzegorzek;Hongzan Sun

作者机构：Microscopic Image and Medical Image Analysis GroupCollege of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical ImageMinistry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent MedicineChengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent SystemsChengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical InformaticsUniversity of LuebeckLuebeckGermany Department of Knowledge EngineeringUniversity of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical UniversityShenyangLiaoning 110136China 

出 版 物：《Intelligent Medicine》 (智慧医学（英文）)

年 卷 期：2024年第4卷第3期

页      面：141-152页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 100207[医学-影像医学与核医学] 1002[医学-临床医学] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 100214[医学-肿瘤学] 10[医学] 

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

主　　题：Gastric cancer Graph-curvature feature Image identification 

摘      要：Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** 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 *** 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 *** 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.