Wendong Li, Yufan Zeng, Xuejun Zhang, Yu Huang, L. Long, H. Fujita
{"title":"肝纤维化的CT分期:优化切片厚度和纹理特征","authors":"Wendong Li, Yufan Zeng, Xuejun Zhang, Yu Huang, L. Long, H. Fujita","doi":"10.1109/ISBB.2011.6107698","DOIUrl":null,"url":null,"abstract":"Texture features are useful in analyzing the hepatic fibrosis on CT images, however properly selecting features and slice thickness is still uncertain. In this paper, five types of slice thickness and 15 features extracted from co-occurrence matrix are investigated to select the optimal parameters. Each combination will be checked by using SVM (Support Vector machine) with leave-one-case-out method. 149 cases including 6 grades of hepatic fibrosis are acquired by CT scanner and divided into two groups: normal & mild fibrosis vs severe fibrosis & typical cirrhosis. Iteration test on all of the subsets indicates that 5 to 7 features with slice thickness of 1.25mm is the optimal combination with relative higher accuracy in classification of fibrosis.","PeriodicalId":345164,"journal":{"name":"International Symposium on Bioelectronics and Bioinformations 2011","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Staging the hepatic fibrosis on CT images: Optimizing the slice thickness and texture features\",\"authors\":\"Wendong Li, Yufan Zeng, Xuejun Zhang, Yu Huang, L. Long, H. Fujita\",\"doi\":\"10.1109/ISBB.2011.6107698\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Texture features are useful in analyzing the hepatic fibrosis on CT images, however properly selecting features and slice thickness is still uncertain. In this paper, five types of slice thickness and 15 features extracted from co-occurrence matrix are investigated to select the optimal parameters. Each combination will be checked by using SVM (Support Vector machine) with leave-one-case-out method. 149 cases including 6 grades of hepatic fibrosis are acquired by CT scanner and divided into two groups: normal & mild fibrosis vs severe fibrosis & typical cirrhosis. Iteration test on all of the subsets indicates that 5 to 7 features with slice thickness of 1.25mm is the optimal combination with relative higher accuracy in classification of fibrosis.\",\"PeriodicalId\":345164,\"journal\":{\"name\":\"International Symposium on Bioelectronics and Bioinformations 2011\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Symposium on Bioelectronics and Bioinformations 2011\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBB.2011.6107698\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Bioelectronics and Bioinformations 2011","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBB.2011.6107698","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Staging the hepatic fibrosis on CT images: Optimizing the slice thickness and texture features
Texture features are useful in analyzing the hepatic fibrosis on CT images, however properly selecting features and slice thickness is still uncertain. In this paper, five types of slice thickness and 15 features extracted from co-occurrence matrix are investigated to select the optimal parameters. Each combination will be checked by using SVM (Support Vector machine) with leave-one-case-out method. 149 cases including 6 grades of hepatic fibrosis are acquired by CT scanner and divided into two groups: normal & mild fibrosis vs severe fibrosis & typical cirrhosis. Iteration test on all of the subsets indicates that 5 to 7 features with slice thickness of 1.25mm is the optimal combination with relative higher accuracy in classification of fibrosis.